Forestry, Faculty of
DSpace
UBCV
Smith-Tripp, Sarah
Doctor of Philosophy - PhD
University of British Columbia
Recent wildfires have burned millions of hectares of North American forests, including over 4% of British Columbia since 2017. Post-fire monitoring in British Columbia lacks spatial and temporal continuity, limiting our capacity to predict and respond to changes in forest composition. Earth Observations satellites, such as Landsat, can help fulfil these data gaps; however, long-term observations require validating post-fire spectral responses to changes in recovering forest structure. Aligning the spatial and temporal scales of satellite imagery with field observations is challenging. Complementary remote sensing technologies, such as light detection and ranging (lidar) data from Remotely Piloted Aircraft (RPAs), can complement field observations and help estimate forest structure at scales that align with satellite data. This dissertation links RPA lidar estimates of conifer recovery to unique spectral responses from Landsat, highlighting the importance of integrating different remote sensing technologies in monitoring. First, RPA estimates of conifer structure were related to spectral time-series data 16 years post-fire. Unique spectral trends were associated with distinct forest composition and density. Building on these findings, early spectral trends (as early as five years after fire) were matched to rates and patterns of conifer establishment, based on a space-for-time RPA lidar dataset 5-20 years post-fire. Research identified two groupings of post-fire recovery across interior BC. The two groupings represented mixed re-growth or early deciduous growth, followed by dense conifer growth. To investigate controls on conifer recovery, structural groups were associated with ecological drivers, such as post-fire drought or pre-fire basal area. Structural recovery groupings were best predicted by post-fire weather and pre-fire composition. Lastly, the structural groupings and associated ecological drivers were systematically compared to broader research on post-fire conifer recovery. Importantly, the recovery observed in interior BC aligned with conifer recovery patterns and trends observed elsewhere in western North America. Temporal spectral trends from satellites played a crucial role in distinguishing the timing of conifer recovery, including delayed conifer establishment. Overall, this research demonstrates that combining the historical archive of Landsat imagery with fine-scale forest structure derived from RPAs lidar enables spatially and temporally monitoring of post-fire recovery.
POST-FIRE CONIFER RECOVERY AND ASSOCIATED ECOLOGICAL DRIVERS ACROSS LARGE EXTENTS ASSESSED FROM SATELLITE AND FIELD OBSERVATIONS by Sarah Smith-Tripp B.A., Wellesley College, 2019 M.Sc., University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2025 © Sarah Smith-Tripp, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Post-fire conifer recovery and associated ecological drivers across large extents assessed from satellite and field observations submitted by Sarah Smith-Tripp In partial fulfillment of the requirements for The degree of Doctor of Philosophy in Forestry Examining Committee: Nicholas C. Coops, Professor, Forest Resources Management, UBC Supervisor Joanne C. White, Research Scientist, Canadian Forest Service Supervisory Committee Member Lori Daniels, Professor, Forest Conservation Sciences, UBC University Examiner Karen Hodges, Professor, Biology, UBC Okanagan University Examiner Camille Stevens-Rumann, Associate Professor, Forest and Rangeland Stewardship, Colorado State University External Examiner Additional Supervisory Committee Members: Dominik Roeser, Professor, Forest Resources Management, UBC Supervisory Committee Member Sarah Gergel, Professor, Forest Conservation Sciences, UBC Supervisory Committee Member iii Abstract Recent wildfires have burned millions of hectares of North American forests, including over 4% of British Columbia since 2017. Post-fire monitoring in British Columbia lacks spatial and temporal continuity, limiting our capacity to predict and respond to changes in forest composition. Earth Observations satellites, such as Landsat, can help fulfil these data gaps; however, long-term observations require validating post-fire spectral responses to changes in recovering forest structure. Aligning the spatial and temporal scales of satellite imagery with field observations is challenging. Complementary remote sensing technologies, such as light detection and ranging (lidar) data from Remotely Piloted Aircraft (RPAs), can complement field observations and help estimate forest structure at scales that align with satellite data. This dissertation links RPA lidar estimates of conifer recovery to unique spectral responses from Landsat, highlighting the importance of integrating different remote sensing technologies in monitoring. First, RPA estimates of conifer structure were related to spectral time-series data 16 years post-fire. Unique spectral trends were associated with distinct forest composition and density. Building on these findings, early spectral trends (as early as five years after fire) were matched to rates and patterns of conifer establishment, based on a space-for-time RPA lidar dataset 5-20 years post-fire. Research identified two groupings of post-fire recovery across interior BC. The two groupings represented mixed re-growth or early deciduous growth, followed by dense conifer growth. To investigate controls on conifer recovery, structural groups were associated with ecological drivers, such as post-fire drought or pre-fire basal area. iv Structural recovery groupings were best predicted by post-fire weather and pre-fire composition. Lastly, the structural groupings and associated ecological drivers were systematically compared to broader research on post-fire conifer recovery. Importantly, the recovery observed in interior BC aligned with conifer recovery patterns and trends observed elsewhere in western North America. Temporal spectral trends from satellites played a crucial role in distinguishing the timing of conifer recovery, including delayed conifer establishment. Overall, this research demonstrates that combining the historical archive of Landsat imagery with fine-scale forest structure derived from RPAs lidar enables spatially and temporally monitoring of post-fire recovery. v Lay Summary Understanding how forests recover from large, severe, and frequent 21st-century fires is challenging, especially in remote places like interior British Columbia (BC). This thesis used satellite observations and 3-dimensional structure data collected by drones to understand post-fire conifer regrowth across the BC interior. Specifically, I compared the first 20 years of structural recovery to satellite observations early on (+5 years) post-fire. Across interior BC, I found similar early satellite observations indicated similar rates of conifer establishment and densities. I found that conifer trees generally recover well after fires in interior BC, and that recovery patterns of BC reflect patterns of other conifer forests across North America. Further, post-fire weather conditions impact recovery patterns in both BC and western North America. Overall, my work demonstrates new techniques to fuse satellite observations with field and/or drone-based structure estimates. These techniques improve our understanding of recovery and its drivers across large landscapes. vi Preface The research objectives of this dissertation were developed from discussions with the supervisory committee. The work presented in Chapters 3 and 4 of this dissertation are published in peer-reviewed journals. Chapters 5 and 6 are formatted for publication and have been submitted for peer review. These publications were framed from discussions with co-authors, the supervisory committee, and project collaborators. Generative AI tools were not used to create text, analysis, charts or images in this thesis. For each chapter, I implemented the experimental design, analyzed the resulting data, drafted the manuscripts, and revised the drafts based on the insightful comments of my co-authors and peer reviewers. Chapter 3 Smith-Tripp, S., Coops, N. C., Mulverhill, C., White, J. C., & Axelson, J. (2024). Landsat assessment of variable spectral recovery linked to post-fire forest structure in dry sub-boreal forests. ISPRS Journal of Photogrammetry and Remote Sensing, 208, 121–135. https://doi.org/10.1016/j.isprsjprs.2024.01.008 Chapter 4 Smith-Tripp, S., Coops, N. C., Mulverhill, C., White, J. C., & Gergel, S. (n.d.). Early spectral dynamics are indicative of distinct growth patterns in post-wildfire forests. Remote Sensing in Ecology and Conservation, 2024. https://doi.org/10.1002/rse2.420 Chapter 5 Smith-Tripp, S., Coops, N. C., Mulverhill, C., White, J. C., & Gergel, S., Post-Fire Structural Forest Recovery Associated with Anomalous Climate Conditions in Dry Sub-Boreal Forests. Under review Chapter 6 Smith-Tripp, S., Coops, N. C., Meyer, J., White, J. C., & Gergel, S., Axelson, J., Roeser, D., Synthesizing Spectral and Field Observations of Post-fire Conifer Recovery in Dry Conifer Forests. Under review vii In addition to these manuscripts, I have also presented research results at academic conferences listed below. Conference Presentations Smith-Tripp, S., Coops, N., White , J., Mulverhill., C., (2024) Early Landsat Spectral Responses Inform Temporal Patterns of Post-Fire Structural Development. American Geophysical Union. Washington DC, USA. Dec 9-13. Poster Smith-Tripp, S., Coops, N., White , J.,(2022) Combining Forest structure measurements with satellite spectral observations for forest recovery monitoring in burned environments of British Columbia, Canada. Forest Disturbance Symposium. Bertchesgarten, GE. Sept 21 – 24. Poster viii Table of Contents Abstract ................................................................................................................ iii Lay Summary ........................................................................................................ v Preface .................................................................................................................. vi Table of Contents............................................................................................... viii List of Tables ........................................................................................................ xi List of Figures .................................................................................................... xiv List of Supplementary Material ........................................................................ xxv List of Abbreviations ....................................................................................... xxvi Acknowledgements ....................................................................................... xxviii Dedication ........................................................................................................ xxix 1.1 Forest monitoring and management in the new fire reality ..................... 1 1.2 Ecological drivers that influence structural recovery .............................. 5 1.3 Methods to monitor post-fire recovery .................................................... 7 1.4 Monitoring landscapes at large spatial scales using satellite data ......... 9 1.5 Additional remote sensing data to verify structure captured by satellites . ............................................................................................................. 15 1.6 Dissertation rationale and gaps ............................................................ 18 1.7 Research objectives ............................................................................. 20 2.1 Study region: sub-boreal spruce and sub-boreal pine spruce ecozones .. ............................................................................................................. 24 ix 2.2 Principle data sources .......................................................................... 27 3.1 Introduction .......................................................................................... 33 3.2 Selected study sites ............................................................................. 36 3.3 Methods ............................................................................................... 38 3.4 Results ................................................................................................. 43 3.5 Discussion ............................................................................................ 60 3.6 Conclusion ........................................................................................... 65 4.1 Introduction .......................................................................................... 67 4.2 Methods ............................................................................................... 69 4.3 Results ................................................................................................. 75 4.4 Discussion ............................................................................................ 93 4.5 Conclusion ........................................................................................... 98 5.1 Introduction .......................................................................................... 99 5.2 Methods ............................................................................................. 102 5.3 Results ............................................................................................... 109 5.4 Discussion .......................................................................................... 126 5.5 Conclusions ........................................................................................ 133 x 6.1 Introduction ........................................................................................ 135 6.2 Methods ............................................................................................. 140 6.3 Overview of results ............................................................................. 147 6.4 Overview of findings ........................................................................... 171 6.5 Future research priorities: key questions and recommendations ....... 180 6.6 Conclusions ........................................................................................ 186 7.1 Research objectives ........................................................................... 187 7.2 Research synopsis ............................................................................. 187 7.3 Research innovations and significance .............................................. 190 7.4 Research limitations and future directions .......................................... 193 7.5 Closing statement............................................................................... 199 Appendix A Systematic Review Procedure .......................................................... 223 Appendix B Supplemental Tables of Systematic Review ......................................... 232 xi List of Tables Table 1. Spectral indices that describe forest recovery. References note type of forest where the reference use the index to monitor forest recovery. ............................... 11 Table 2. Description of research questions by analytical chapters. Each analytical chapter notes the research gap, question, and analytical approach. ...................... 22 Table 3. Outputs of general linear regression models generated using lidar data and field plot measures (N = 26). Names of beta slope estimates, standard errors are noted. For each model, a measure of fit appropriate for model type (R2 for continuous linear models and area under the curve (AUC)) is noted. Model fit values were computed as the average of all k-fold cross-validation estimates. ................. 46 Table 4. Average structural metrics for each study site and averages for all study sites. +/- is the standard deviation. ................................................................................... 47 Table 5. Eigenvectors for the first and second principal component of the principal component analysis. The colored column is the scaled magnitude of PC1 and PC2 scaled using the Pythagorean theorem. Values for PC1 and PC2 are proportion of variance explained by the spectral metric for each component. ............................. 48 Table 6. Average spectral metrics by index and cluster (1-6). The proportion of each index that is greater than baseline in 2021 and the average number of years to surpass baseline are also listed. Statistically similar spectral components (based on a Dunn-Test p < 0.1) are noted with the adjusted P-value. ..................................... 53 Table 7. Average structural metrics for each spectral cluster (+/- is the standard deviation). The study area refers to the total area covered by each cluster for the xii lidar flight acquisitions, and the Fire area is the entire 2006 Watlus Lake fire perimeter based on the maximum likelihood classification. .................................... 54 Table 8. Outputs of the Chi-Squared test and the Dunn test for each structural metric. Only statistically similar pairs are listed (based on a P-value < 0.05). .................... 56 Table 9. Confusion matrix of maximum likelihood classification for the Watlus Lake fire using spectral clusters (pixel # = 1445). Overall accuracy shown in bold. .............. 58 Table 10. Metrics of linear modeling from field plots and RPAS lidar metrics for additional bare ground model used in Chapter 4. ................................................... 80 Table 11. Results of MANOVA applied to unique spectral clusters to identify structural patterns of recovery. Permutations = 999. .............................................................. 83 Table 12. Average structural estimate for each structural group for each year after wildfire (± standard error of the mean). The number of observations is the number of 30x30 m pixels with RPAs lidar data. Note stem counts exclude areas where ABA stem count estimates were above field observed values (> 8000 stems / 900 m2). 85 Table 13. Post-hoc Dunn tests for nested ANOVA comparing emergent conifer and mixed forest structural groupings. These groupings are highlighted as they have data across all years. P-values are adjusted via the Holm method. Years where structural estimates group are different are starred. ............................................... 92 Table 14. Data sources or calculations citations for drivers of forest recovery. Abbreviation and units for each variable. References note data source. For each driver, a reference for both the data source, and prior reference support is noted to the right. Note: climate anomalies used in models are italicized. .......................... 107 xiii Table 15. Model coefficients and model type for the four modeled structure variables. Model type includes the model family. Df = degrees of freedom. Measure of fit (R2 or AUC) is included for each model........................................................................... 110 Table 16. Average values for each structural group (+/- standard deviation). Colors are the scaled values for the minimum (dark blue) and maximum (yellow) of structural estimates across all years. .................................................................................... 113 Table 17. Random forests built for different driver types. For each random forest the overall accuracy, variables, and associated variable importance is noted. The overall random forest selected no more than five variables of each recovery driver type and no driver with a variable importance < 0.01. Anomalies are italicized. ... 123 Table 18. List of Databases and Collections included in systematic literature review 140 Table 19. Inclusion and Exclusion Criteria for both title and abstract and full-text screening steps of literature review....................................................................... 142 Table 20. Description of methods used to process satellite data and identify spectral trends for studies included in the systematic review (n = 26). ............................... 158 Table 21. % of studies that measure distinct trends, forest-type, fire severity measure, structural measure, modeling method, and study type (spectral of field based). Proportions calculated from total number of unique models (n = 150). ................. 161 Table 22. Key outstanding research questions associated with each review objective. ... ....................................................................................................................... 184 Table 23.Considerations and recommendations for future research to enable synthesis and meta-analysis. ................................................................................................ 185 Table 24.Questions and key findings associated with research dissertation. ............. 189 xiv List of Figures Figure 1. (A) Image of Plateau Complex Fire which burned 540,000 ha in 2017. Photo taken in 2022. (B) Image of the Pelican Lakes Fire which burned in 2015. Photo taken in 2024. (C) Total area burned and total number of fires in British Columbia 2008-2023. Data sourced from Provincial fire perimeters (BC Data Catalogue, 2023). ..................................................................................................................... 2 Figure 2. Pre-fire composition and structure associated with four main recovery pathways. Patterns and trends that differentiate pathways described at right (based on concepts of Seidl et al., 2017). ............................................................... 4 Figure 3. (A) Metrics of spectral recovery for a given spectral index (NBR, NDVI, TCA, etc.) utilized throughout the dissertation (metrics: rate of recovery, recovery magnitude, and current year measure) (B) hypothetical spectral trends for a given index describe how rate could fluctuate based on recovery dynamics. Note: spectral saturation is when rate of recovery plateaus. .......................................... 14 Figure 4. Outline of fire disturbances and prior research for dry coniferous forests within the Western Cordillera Ecozone (Orange). The bar chart to the right is the total number of fires in km2 for each latitude band as identified by satellite data between 1984-2022. Fire locations identified using the Landtrendr algorithm (Kennedy et al., 2018) validated with the USGS MTBS (Finco et al., 2012) and the Canadian National Fire Database (Skakun et al., 2024). Fire locations are noted in black. Prior recovery reviews (large red points) and post-fire plots used in meta-analyses (white) are overlaid on ecozones (orange and green). The study area is highlighted within the black box (top). ................................................................... 17 xv Figure 5. Overview of thesis analytical chapters. Colors denote data-source type (derived within thesis, red, or sourced from literature, blue) and research gaps (yellow). Orange arrows link the research gaps across chapters. ......................... 23 Figure 6. Sub-boreal-spruce (SBS) and sub-boreal-pine-spruce (SBPS) biogeoclimatic zones (BEC) highlighted throughout the thesis. Black overlays define areas disturbed 1984-2022 (NTEMS data catalog, 2022). Inset map highlights location of BC (blue) within Canada. ..................................................... 25 Figure 7. (Left) Locations of RPA flights within study area. Areas of high disturbance wildfire are noted in grey, and biogeoclimate regions are in orange. Points are colored by year of RPA flight in years after wildfire. The inset map (right) highlights the study in purple. ............................................................................................... 30 Figure 8. Example of RPA lidar data pre- and post-processing. Pre-processed data displays topographic variability. Post-processed point cloud is normalized to heights above ground. .......................................................................................... 32 Figure 9. Top: Location of research sites in Quesnel TSA. Black areas are the disturbed areas (fire, harvest, road, etc) for the past 20 years. Blue dots show the study sites. Below: Outlines of study fires colored by dNBR fire severity (low = yellow, high = purple). Flight areas are outlined in blue. The inset map shows BC study site in reference to Canada. ........................................................................ 37 Figure 10. Orthomosaics of selected study sites flown with RPA lidar. All mosaics are at the same spatial scale. ..................................................................................... 38 Figure 11. Conceptual overview of study approach. First section (3. Data) describes data collection including satellite data and estimates of forest structure. Second xvi section (4. Methods) shows model development in three parts: (1) cluster development and assessment, (2) cluster association, and (3) application of clustering to larger fire area. ................................................................................. 40 Figure 12. Structural estimates predicted from lidar compared to field measurements for each 900 m2 field plot. Panels, from left to right, are: basal area (m2/ha), ratio of conifer to deciduous stems (1 = 100% conifer), and # of conifer stems > 1.3 m in height for each 900 m2 plot. Final model details, including parameter estimates and overall accuracies, are included in Table 3. ................................................... 44 Figure 13. Density distribution of forest structure metrics for three study acquisition areas. Black lines are the median of the estimates. Total number of unique pixels is 5909 (site 1 – 2073, site 2 – 2065, and site 3 – 1335 pixels). Density distributions show the square root transformation of pixel count to display structural estimates variability better. .................................................................... 44 Figure 14. Three lidar flight areas colored by estimate of forest structure (basal area m2/ha, coniferous: deciduous, # stems / 900 m2) based on ABA models (Table 3). . ................................................................................................................... 45 Figure 15. Principal component analysis of pixels based on spectral indices. Points are colored by spectral clusters. Ellipses show where 50% of the data plots. Loadings are the scaled proportion of variance in PC1 and PC2. ........................................ 49 Figure 16. Spectral trends of six (shown in color) spectral clusters. “Pre-fire baseline” based on observations of 10 years preceding the fire (1995-2005). Values are the difference (%) from the pre-fire baseline, where 0 (black line) is no difference from baseline measures. Yearly values are two-year moving averages, with the line xvii width equal to the standard deviation by spectral cluster and band. The vertical dashed line is the year of the fire (2006). .............................................................. 51 Figure 17. Average spectral metrics for each cluster for seven spectral indices colored by spectral clusters. 2021 Measure is the summer 2021 Landsat measure, regrowth magnitude is the overall change from 2006 to 2021, and the Thiel Sen slope is the estimate of the regression line between 2006 and 2021. Note: to allow metrics to plot on similar scales, NBR, NDMI, and NDVI are multiplied by 1000. . 52 Figure 18. Distribution of structural metrics: basal Area (m²/ha), stem count (stems/pixel), and the coniferous:deciduous ratio for each spectral cluster. The boxes note the interquartile region, and the black lines are the median values. Asterisks note spectral clusters with statistically similar structural estimates (Dunn test p < 0.05). ........................................................................................................ 55 Figure 19. Spectral classification 2006, Watlus Lake fire recovery. Sites covered by lidar are outlined in orange. White areas did not have enough spectral coverage for classification. ................................................................................................... 57 Figure 20. Overview of research methods and findings for research question 1. Left: Data inputs from lidar and Landsat highlighted in black diamonds. Middle: Landsat spectral trends and lidar cross-sections of associated structural forest expressions. Right: Classified outputs of spectral trends for the 2006 Marquis lake fire............ 59 Figure 21. Description of Landsat Data Processing, Spectral Clustering, and Spatio-temporal Analyses with RPAs Lidar Data. ............................................................ 70 Figure 22. (Left) Location biogeoclimate zones (SBS: Sub-Boreal Spruce, SBPS: Sub-Boreal Pine Spruce) in study area. Areas disturbed 1984 – 2022 colored in grey. xviii Blue dots represent lidar flight locations. The inset map shows the location of British Columbia and the study site (black box) in Canada. The yellow star noted the location of lidar structural estimates shown at right. (Right) Examples of the lidar structural estimates including the proportion of bare ground, basal area, coniferous to deciduous ratio, and stem counts. ................................................... 73 Figure 23. (A) Principal Component Analysis of spectral metrics used in clustering. Points represent a sample of the total dataset (10,000 random samples weighted by cluster frequency). Ellipses define where 40% of the data for each cluster is included. Black-labeled arrows that the variable loadings of the PCA. Colors denote associated spectral cluster (B) Variable loadings in order of importance. Values are scaled relative to the importance of each principal component........... 76 Figure 24. Average values of each spectral metric input in Kmeans ++ clustering for the eight identified spectral trends. All spectral metrics are multiplied by 100. ..... 78 Figure 25. Mean trends for each spectral cluster for all indices for year -3 to +20 after fire. Mean values are relative to a 3-year pre-disturbance baseline period for all clusters. The label above the panel notes the respected index. The black dotted line at year = 0 is year of the fire. .......................................................................... 79 Figure 26. Model graphs comparing field measurements (measured) to model predictions (predicted) for proportion of bare ground built from RPAS lidar. The black line shows the 1:1 line. ................................................................................ 80 Figure 27. Number of pixels for each spectral cluster with associated RPA lidar data. The total number of pixels for each cluster is noted by column. Colors denote the proportion of pixels by years since fire for each cluster. ....................................... 81 xix Figure 28. Post-hoc significance values for analysis of dissimilarities for each cross-comparison of clusters. Colors rank similarity of spectral clusters. In the structural grouping process, clusters were collapsed when post-hoc p-values > 0.1. .......... 83 Figure 29. The difference between the mean for each spectral pattern and the mean for each year since fire (5, 8, 11, 12, and 16 years). The horizontal black line is the mean for that year. For bare ground percentage, values above the line have more bare ground. For coniferous: deciduous, points above the line have a higher proportion of coniferous cover. For both BA and stem counts, values above the black line are greater than the mean for the year. Groups with fewer than three observations are omitted from the graph. ............................................................. 87 Figure 30. (Top) The most common structural groups are represented in 10,000 ha hexagons. (Bottom-left) number of distinct structural patterns in each hexagon. (Bottom-right) proportion of the hexagon covered by high-severity wildfire. The background for all maps is a hillshade. ................................................................. 89 Figure 31. (A) Estimates for each structural measure for years after the fire. All data (gray) are estimates across all groups. Emergent conifer (red) and mixed forest growth (orange) are subsets for visualization. Asterisks indicate when ANOVA and post-hoc Dunn test identify significant (< 0.05) differences among the emergent conifer and mixed forest groups. (B) Exemplar lidar cross-sections for emergent conifer and mixed forest regrowth groups. ............................................................ 91 Figure 32. Research methods and outputs for research question 2. Top panel describes methods to develop spectral responses (left) and a distribution of unique spectral responses across the study area (right). Bottom panel is example lidar xx cross sections highlighting structural growth over time for the two most common structural pathways – emergent conifer and mixed forest growth. ........................ 93 Figure 33. Overall research framework including development of structural groupings. Boxes beneath ‘drivers of recovery’ are the categories of drivers used in individual random forests as well as the global random forest. The overall impact of drivers of recovery o structural recovery (research objective) is accentuated in dark blue. 103 Figure 34. Post-hoc description of differences in spectral clusters based on structural variables input into MANOVA analysis. Spectral grouping combined spectral clusters based on similarity (p > 0.1). Values are adjusted using the p-holm method for non-parametric data. Cluster 7 was similar across all clusters, but was combined with cluster 1 because this had the highest degree of similarity (p = 0.89). Groupings are as follows: Mixed Growth = cluster 1 and cluster 7, regrowth delay = cluster 3 and cluster 4, Regenerative Conifer = cluster 5, 6, and 8, Coniferous dominant mixed growth = cluster 9. .................................................. 111 Figure 35. Orthophotos from RPA flights displaying distinct structural recovery groups 15-16 years post-fire. Values are the average values for each structural group 15-16 years post-fire. All images are at the same scale. ......................................... 112 Figure 36. Structural estimates by group for each post-disturbance epochs (or years post-fire). Black lines are median structural value, boxes span the interquartile range and 95 % confidence values. # pixels included in calculations noted in Table 16. Note: mixed growth was not sampled 21 years post-fire. .............................. 115 Figure 37. Spatial distribution of structural groupings across the sub-boreal spruce and sub-boreal pine spruce Ecozones – inlay map shows ecozone location within BC. xxi Hexagons are the most common structural group for each 10,000-ha area. Subsets (labeled A & B on map) left of the map are the groups at a 30 m resolution for two large fires (A - Chelaslie River Fire 2014, B- Plateau Complex Fire 2017). .. ................................................................................................................. 116 Figure 38. Average values of spectral metrics included in clustering for spectral groups. Values are the normalized scaling applied before clustering. ................ 118 Figure 39. Boxplots of variable importance from the Boruta implementation of the overall random forest model. Colors denote driver type, and distribution is the Boruta variable importance for 500 random forest trees. Importance range 0-100, where 0 is no impact. .......................................................................................... 119 Figure 40. Partial dependence plots for the top 6 most important drivers from the full random forest (19 selected drivers). The X-axis represents the predictor variable range, and the y-axis is the likelihood for a given structural group when all other variables in the random forest are held constant. Variables are listed in decreasing importance within the overall random forest. The labels above the plots note the units for each driver. “Z” is the z-score standardized value based on climate data 1980-2010 for each pixel. ................................................................................... 120 Figure 41. Confusion matrix for overall random forest model classifying structural recovery from validation data (n = 14440) colored by the % of correctly classified pixels for each structural grouping. ..................................................................... 122 Figure 42. Boxplots of structural groupings for (left) Patch size calculated from NTEMS dNBR data and (right) Pre-fire mean (n = 5 years) BA in m²/ha. Boxes are colored by structural group. ............................................................................................. 125 xxii Figure 43. Overview of forest recovery pathways, definition, and the importance of measurement timing and measurement metric in recovery monitoring. .............. 139 Figure 44. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram for systematic review, including the identification, screening, and inclusion process. Final number of distinct studies is 58. ............................. 147 Figure 45. Included studies (n = 58) ordered by year of publication (left to right) and colored by approach: field-based (black) or spectral remote sensing (blue). Lines are citation links. Studies colored in yellow are associated with other relevant publications that were ultimately excluded in the final data extraction due to duplicate field plots. Spectral studies also note whether the study uses a threshold, temporal, or machine learning/clustering response to identify recovery pathways (legend top left). .................................................................................................. 150 Figure 46. Map of the Western Cordilleran eco-region subset to areas that experience a continental climate. Areas with wildfires in this eco-region are shown in black. Total fires (in km2) from 1984-2022 by latitude band shown at left. Fire locations identified using the Landtrendr algorithm (Kennedy et al., 2018), validated with the USGS MTBS (Finco et al., 2012) and the Canadian National Fire Database (Skakun et al., 2024). Points are the center of the bounding box for studies included in data extraction. Studies that appear outside the Western Cordillera are landscape-level studies. Color denotes if the study is spectral (uses satellite data) or field-based (uses only plot-level data) ............................................................ 151 Figure 47. Coordinate plot representing the literature identified pre-fire forest types, post-fire forest types, and notable changes in conifer density or time to xxiii establishment (i.e., delay). Lines are colored by the distinct recovery pathway. Line width represents the frequency the pathway was extracted from studies (total observations = 107). ........................................................................................... 154 Figure 48. Frequency of different groupings (arranged by Category) of ecological drivers across all studies. Labels are the groupings, and numbers are the total number of times a driver within the group was extracted. ................................... 162 Figure 49. Counts of ecological drivers colored by associations with the metric of recovery (stem density, seedling presence, spectral) for interior dry conifer forests. Colors denote when driver association with recovery is negative (red) or positive (blue) for the recovery metric (note scales on the x-axis vary) with neutral (grey) offset. % is the percentage that the driver was tested and included in the final models. The y-axis labels are colored by driver Category (see legend to the left). ... ................................................................................................................. 166 Figure 50. Counts of ecological drivers colored by associations with the metric of recovery for common conifer species (lodgepole pine, ponderosa pine, Douglas fir). Colors denote when driver association with recovery is negative (red) or positive (blue) for the recovery metric (note scales on the x-axis vary) with neutral (grey) offset. % is the percentage that the driver was tested and included in the final models. The y-axis labels are colored by driver Category (see legend to the left). ................................................................................................................. 167 Figure 51. Relative importance of drivers with parameters normalized by model and model accuracy between -1 to -1. Values of -1 or 1 describe a driver with the largest effect size or greatest importance in a model that captures 100% of the xxiv variability. Negative values have a negative effect and positive values have a positive impact on the associated conifer recovery metric. Driver groups are shown only when N>3. (A) Diamonds and bars are the median and interquartile ranges for spectral- and field-based approaches. (B) Boxes show interquartile ranges and median values for each driver group across all approaches. Boxes are colored by driver Category (see legend at right). ................................................................. 170 xxv List of Supplementary Material AppendixB.zip B.1 Supplemental_Table_1.html B.2 Supplemental_Table_2.html B.3 Supplemental_Table_3.html B.4 Supplemental_Table_4.html xxvi List of Abbreviations ANOVA: Analysis Of Variance ARD: Analysis Ready Dataset AUC: Area Under the Curve AVG.: Average BA: Basal Area BAP: Best Available Pixel Composite BARC-A: Burned Area Reflectance Classification – Adjustable BC: British Columbia BEC: Biogeoclimatic Ecosystem Classification BORUTA ALGORITHM: A feature selection method used in machine learning to identify the most important variables for prediction in comparison to ‘dummy’ or shadow variables. C2C: Composite 2 Change CMD: Climate Moisture Deficit DBH: Diameter at Breast Height DEM: Digital Elevation Model DJI: Da Jiang Innovations (RPA Company) dNBR: difference in Normalized Burn Ratio EFI: Enhanced Forest Inventory GDEM: Global Digital Elevation Model GLM: Generalized Linear Model GNSS: Global Navigation Satellite System HA: Hectare IFI: Integrated Forest Index LIDAR (spelled lidar throughout): Light Detection and Ranging K-MEANS: A data partitioning algorithm to group points based on similarity to data centroids K-FOLDS: Model validation technique that subsamples the initial input data to ensure model coefficients are not overfit MANOVA: Multivariate Analysis of Variance MAP: Mean Annual Precipitation MAT: Mean Annual Temperature MAX: Maximum MIN: Minimum MPB: Mountain Pine Beetle NBR: Normalized Burn Ratio NDMI: Normalized Difference Moisture Index NDVI: Normalized Difference Vegetation Index NTEMS: National Terrestrial Ecosystem Monitoring Program OOB: Out-Of-Bag PAS: Precipitation as Snow PC: Principal Component PCA: Principal Component Analysis PERMANOVA: Permutational Multivariate Analysis of Variance xxvii PRISMA: Preferred Reporting Items for Systematic Reviews And Meta-Analyses RF: Random Forest ROSES: Reporting Standards For Systematic Evidence Syntheses RPA: Remotely Piloted Aircraft RQ: Research Question RECOVERY METRICS: Generally, rate of spectral recovery, magnitude of spectral recovery, and the measure of a spectral index SBPS: Sub-Boreal Pine Spruce Ecozone SBS: Sub-Boreal Spruce Ecozone SPIDER: Sample Size, Phenomenon of Interest, Study Design, Evaluation, and Research Type (Method used to derive systematic search query) SPECTRAL INDICES: Indices used to derive spectral responses TC: Tasseled Cap TCA: Tasseled Cap Angle TCB: Tasseled Cap Brightness TCG: Tasseled Cap Greenness TCW: Tasseled Cap Wetness TLS: Terrestrial Laser Scanning TPI: Topographic Position Index TRASP: Transformed Aspect TSA: Timber Supply Area TWI: Terrain Wetness Index US: United States VRI: Vegetation Resource Inventory ZENMUSE L1 & L2: DJI RPA lidar Sensor xxviii Acknowledgements I must first thank my supervisor, Nicholas Coops, for the research support throughout my degree. In particular, I thank Dr. Coops for the opportunities that helped me grow my independence and confidence as a scientist. I am grateful for the logistical and financial support that allowed me to pilot my own field campaigns in remote regions of British Columbia, contribute to international conferences, and even spend a summer as a visiting student at ETH Zurich. These experiences were foundational for grounding my research passions and career priorities. I thank my committee members: Dr. Joanne White, Dr. Sarah Gergel, and Dr. Dominik Roeser for helping me conceptualize my different chapters. My committee meetings and one-on-one discussions were pivotal for forming this work. I also thank the current and past members of the Integrated Remote Sensing Studio (IRSS) for their conceptual, technical, and even emotional support throughout my degree. In particular, I thank Dr. Chris Mulverhill for the pivotal role played in my data-chapters, Dr. Lukas Winiwarter for his statistical and coding advice, Evan Muise for his crucial role as an NTEMS data-processor, and Leanna Stackhouse for her role as IRSS baking master and a PhD friend. I also want to thank Professor Verena Griess and the entire FORM Lab at ETH Zurich for my experience learning about forestry and academia in a completely new context as a visiting student summer of 2024. Data for my research chapters were collected with help from: Mike Burnette, Dave Choi, Lukas Olson, Brianne Bouffard, and Joe Meyer. Finally, I extend my heartfelt gratitude to my family and friends for their willingness to lend an ear, offers of places to stay, and unending support throughout the rollercoaster known as the PhD process. xxix Dedication To my communities that root me near and far For it is with you I grow towards the canopy 1 Introduction 1.1 Forest monitoring and management in the new fire reality Repeated record-breaking wildfire seasons the 21st century reflect the combined effects of increased severity, frequency, and size of global fire (Seidl et al., 2017; Figure 1). Historically, fires in tropical regions burned more area and produced more carbon, but fires in northern and temperate regions now burn and produce more carbon than the tropics (Jones et al., 2024). Now, in northern and temperate regions, more extreme fire weather and increased fuel loadings compound to produce large, high-severity fires that threaten towns and communities (Guo et al., 2024; Parisien et al., 2020). Fires throughout Canada, including the province of British Columbia (BC), burn thousands of kilometers, emit millions of tons of carbon, and leave behind lunar-like landscapes (Figure 1; Jones et al., 2024; Parisien et al., 2023). Recovery monitoring and management is particularly difficult in Canada because a low population density means staff and finances are small compared to the scale of recent burns (Wulder et al., 2010). As climate change continues to increase burned area across Canada and in BC (Hanes et al., 2019; Parisien et al., 2023; Wang et al., 2016), so too will the short and long-term impacts of fires on forest resources, including timber resources (Clason et al., 2022). The longer-term impacts of increased fire activity on key ecosystem services, such as timber, depend on the recovery of forest composition and structure post-fire (Thom & Seidl, 2016). After a fire, tree and vegetation losses impact the forest-related goods and services, including water, carbon, and timber over the short and long term (Millar & Stephenson, 2015). Change between pre- and post-fire forests exacerbates fire impacts on ecosystem resources because changes, such as decreased forest density, alter 2 timber availability and carbon sequestration (Dahm et al., 2015; Giles-Hansen & Wei, 2022; Kiel et al., 2025). Understanding a fire’s full impact and then prioritizing appropriate post-fire management action is a critical challenge for Canada. Widespread wildfire makes characterizing recovery across landscapes difficult (Prichard et al., 2021) and managing current and/or future forests challenging (Achim et al., 2021). Monitoring recovering forests is important because warmer and drier climate conditions threaten successful conifer establishment (Hoecker et al., 2023). Knowing where and why conifers are not establishing is critical for communities of interior British Columbia, where many people continue to rely on a conifer-based timber industry (BC Forest Practices Board, 2020). Identifying areas at risk of reduced conifer recovery that benefit from proactive management, including planting, fertilization, or other site preparation (Sorenson et al., 2025), requires broad-scale, accurate, and timely forest monitoring of post-fire dynamics (Achim et al., 2021). Figure 1. (A) Image of Plateau Complex Fire which burned 540,000 ha in 2017. Photo taken in 2022. (B) Image of the Pelican Lakes Fire which burned in 2015. Photo taken in 2024. (C) Total area burned and total number of fires in British Columbia 2008-2023. Data sourced from Provincial fire perimeters (BC Data Catalogue, 2023). 3 Structural recovery, defined herein as the patterns and trends in conifer establishment and growth, is critical to characterize in early post-fire years. The first decade of conifer establishment defines the forest structure and composition for subsequent decades to centuries (Brullsauer et al., 1996; Tortorelli et al., 2024).The first decade of conifer establishment, in both rate and growth, determines the dominant forest structure and composition for the second phase; young forest growth (Bartels et al., 2016; Oliver & Larson, 1996). Recovery studies often measure structural variability (e.g., stem density, number of saplings) during the establishment phase, and use these measures to predict future forest outcomes (Baltzer et al. 2021). Structural measures are linked to recovery “pathways”, wherein a pathway is a spatially variable vegetation pattern (stem density) and a temporally distinct trend in vegetation growth (Seidl & Turner, 2022). Forest recovery pathways can be placed into four broad classes distinct in structure and composition (Baltzer et al., 2021; Seidl & Turner, 2022; White et al., 2023). These pathways include: (1) self-replacement where the same-species returns to the same density and height as pre-fire conditions; (2) density and height changes where the same species returns, but with different density or growth rates; (3) competition changes where the same forest type (e.g. conifer) maintains dominance, but with altered canopy species composition; and (4) state changes where a forest type (commonly conifers) lose dominance to deciduous trees, grasses, or even bare ground (Figure 2). Recovery pathways are time and space dependent, meaning a pathway of self-replacement could also have delayed establishment, resulting in an early period of bare ground or deciduous growth (Figure 2., Stevens-Rumann et al., 2022).4 Figure 2. Pre-fire composition and structure associated with four main recovery pathways. Patterns and trends that differentiate pathways described at right (based on concepts of Seidl et al., 2017).5 In interior BC, the historically dominant forest recovery pathway is self-replacement, where pre-fire conifer stands regrow as conifer stands, but establishment rates can vary (Brullsauer et al., 1996; Campbell & Antos, 2003; DeLong et al., 2003; Hamilton & Burton, 2023; Jull, 1990; Lee, 2004). Productive sites that are warm and have adequate precipitation have rapid and dense seedling establishment within the first few years post-fire (Brullsauer et al., 1996; Turner et al., 1999). Comparatively, cold and dry sites (also called xeric) are slow to establish new seedlings (Brullsauer et al., 1996). Recent research suggests that increases in fire severity, alongside warming climate conditions, are creating possibly novel pathways towards young forests of mixed composition (Johnstone et al., 2010; Kulakowski et al., 2013) or pine-grassland systems (Celebrezze et al., 2024; Hamilton & Burton, 2023). 1.2 Ecological drivers that influence structural recovery Variability in recovery pathways, in terms of both temporal trend and spatial pattern, is driven by ecological drivers that vary across spatial and temporal scales. These drivers include local site-specific conditions, such as microclimate or distance to seed source (Peven et al., 2024), and landscape-level variables such as post-fire climate conditions (Littlefield et al., 2020). Prior work has linked drivers to spatially variable patterns of stem density, species composition (Stevens-Rumann and Morgan, 2019), as well as temporally variable trends of conifer establishment (Littlefield, 2019). Typically, studies use models to test the relative effect of different drivers on their forest recovery metric. Yet, the effect of a driver within a model is determined by the scale of analysis, how the driver is measured, its level of variability, and the consideration of other drivers included in the models. 6 The relative impact of a driver can also shift based on which other studies are considered. For example, while know temperature and moisture control seedling establishment (Petrie et al., 2016), a 2019 review found few post-fire recovery studies integrated climate variables in models (Stevens-Rumann & Morgan, 2019), which makes it difficult to quantify the impact of climate on recovery. In some cases, driver effects can shift based on the scale of analysis (Peeler and Smithwick, 2020). For example, the effect of aspect and elevation on recovery pathways varies at the site and landscape scale, including fires of the same year and in the same region (Turner et al., 2004) and across the entire western US (Shrestha et al., 2024). Overall, despite the fact that the drivers of recovery are well known, the relative importance of a given driver can vary wildly across regions. Generally, regional models suggest that post-fire drought and high fire severity negatively affect conifer establishment (Davis et al., 2023; Stevens-Rumann et al., 2022). Recent research used field plot data in the western US to develop climate-sensitive models for post-fire conifer establishment, addressing a historical lack of climate in recovery modeling (Davis et al., 2019, 2023). Prior research demonstrates the importance of post-fire climate conditions for recovery in Western US interior dry forestd (Davis et al., 2019; Guz et al., 2021) and in boreal environments (Boucher et al., 2020; White et al., 2023), but other regions, namely the dry sub-boreal forests of Interior BC, lack research on the importance of post-fire climate (Talucci et al., 2019). Comparing the observed pathways of recovery against a gradient of ecological drivers can improve understanding how specific ecological drivers, like climate, impact recovery. Regional analyses from field data such as Davis et al. (2023) improve 7 understanding of structural recovery and associated ecological drivers. Yet, creating these landscape-scale field datasets is costly and labor-intensive, requiring detailed coordination to keep up-to-date (Pettorelli et al., 2018). Further, some regions, such as BC have relatively few post-fire studies and limited post-fire sampling (Figure 4, Bourgeois et al., 2018). 1.3 Methods to monitor post-fire recovery “Space-for-time” field measures are a common approach to gain long-term perspectives on ecological processes, including recovery pathways. Space-for-time explicitly considers the age of a forest post-fire and the spatial variability of pre- and post-fire conditions to measure similar sites at different ages post-fire across a landscape (Pickett, 1989). Forest managers then extrapolate measures of one forest phase (e.g. early establishment) to the next (e.g. young forest growth) based on observations of the older forest (Bartels et al., 2016; Oliver & Larson, 1996; Shugart & West, 1980). Space-for-time sampling assumes future characteristics of the younger forest site will mimic the characteristics an older site’s current characteristics. While space-for-time approaches are common, they require long periods post-fire (typically 30 + years) to relate spatial variation with temporal trends (Stevens-Rumann et al., 2022), especially in slow-growing forest environments (Kreyling, 2025). Space-for-time assumes that underlying controls on structural recovery do not vary temporally or spatially. As climate change pushes species to climatic range limits, we cannot assume that longer-term growth rates of one population translate to region-wide species’ growth rates (Klesse et al., 2020). One solution to overcome limitations of space-for-time is to increase the number of samples, which decreases bias towards a 8 specific population or age (Ni & Vellend, 2021). Consistent sampling across both space and time is key for space-for-time approaches. Unfortunately, many regions, including British Columbia, do not have consistent nor wide-spread monitoring pivotal for the first decade of conifer establishment. This monitoring is key to updating forest inventories. In British Columbia, pre-fire forest inventories are derived from the Vegetation Resources Inventory (VRI) program. VRI is a two-phase forest inventory system where photo-interpreters first manually delineate key silvicultural attributes (stand-height, basal area, stem-density, etc.) within homogenous forest stands. Then, estimates of silvicultural attributes are adjusted from systematic field samples. Homogenous forest stands are compared across the province based on their Biogeoclimatic Zone (BEC; Meidinger & Pojar, 1991). BEC divides BC’s land cover into three levels: zones, subzones, and site series, which together describe the expected vegetation at ecosystem climax (Meidinger & Pojar, 1991). BEC subzones are used to set “stocking” standards for forest structure and composition (Meidinger & Pojar, 1991). Stands are classified as “satisfactorily stocked” or “non-satisfactorily stocked” based on targets for stand density, species type, height, spacing, and growth (McWilliams & McWilliams, 2009). A satisfactorily stocked or “free-to-grow” stand meets standards where precedent supports that the stand is or will achieve desired forest composition and structure (generally the same as the pre-disturbance species, McWilliams & McWilliams, 2009). After natural disturbances like fire, there is no official post-fire monitoring framework. Instead post-fire forest composition and structure is updated haphazardly within the BC VRI system (Sandvoss et al., 2005). 9 After fire, forest attributes within the VRI are updated based on estimated disturbance severity from the annual BC aerial survey and/or Landsat satellites. Typically, post-fire mortality is estimated by aligning sites of similar satellite disturbance severity where some locations have post-fire aerial for field validation data (i.e. using space-for time substitution). Yet, aerial and field validation post-fire is sporadic, particularly in northern and central BC (BC Forest Inventory and Analysis Branch, 2023). After severe fires, such as the 2017 and 2018 wildfires (Ministry of Forests, 2018), the Province prioritizes acquiring more detailed validation data, but a historic sampling deficit (BC Forest Inventory and Analysis Branch, 2023) limits BC-wide estimates of post-fire mortality and recovery (Bourgeois et al., 2018). The BC VRI program is not designed to monitor after widespread wildfire or other large disturbance (Bourgeois et al., 2018). Therefore, there is a need for a monitoring methodology that comprehensively captures historic as well as current trends in forest recovery after landscape-level fire (Mansuy et al., 2020). 1.4 Monitoring landscapes at large spatial scales using satellite data Monitoring trends of post-fire forest recovery requires spatially and temporally consistent data of high-quality. One source of such data are surface reflectance observations from satellites (Wulder et al., 2022). In particular, spectral reflectance information from the Landsat Thematic Mapper’s suite of sensors offer over 40 years of spatially continuous measurements at a consistent spectral and radiometric resolution (Wulder et al., 2022). The opening of the Landsat archive in 2008 (Woodcock et al., 2008) expanded the use of satellite data for landscape-level forest monitoring (Kennedy et al., 2014; Young et al., 2017). A research synthesis on boreal forest recovery 10 assessed via remotely sensed data noted that between 2008 and 2024, studies using Landsat time series data increased sixfold (White, 2024). More recent studies also benefit from well-defined approaches for satellite data processing (Dwyer et al., 2018) and strong links between components of forest structure and spectral response (Chu and Guo, 2013). Spectral observations are frequently summarized using indices such as the normalized burn ratio (NBR, Key & Benson, 2006). These indices leverage differences between short-wave and near-infrared reflectance in different vegetation types to characterize forest structure (Pickell, 2015). For example, NBR combines the near infrared and shortwave reflectance to distinguish green vegetation from dead or burned vegetation. NBR is widely in fire severity assessments (Picotte et al., 2020), and the incremental increase in NBR after fire is strongly linked to recovering forests (White, Saarinen, et al., 2018). Past studies support that combining indices (e.g., NBR and TCG) can better capture forest structure, especially compositional elements, such as the proportion of deciduous stems (Cohen et al., 2020; Pflugmacher et al., 2012; Shimizu et al., 2019). Some of the most common indices used for forest recovery monitoring are summarized in Table 1 (Bolton et al., 2015; Chu & Guo, 2014; Cohen et al., 2020; Frolking et al., 2009; Nguyen et al., 2020a; Pflugmacher et al., 2012; Vanderhoof & Hawbaker, 2018; White et al., 2019). These indices are utilized throughout this dissertation to describe structural forest recovery. 11 Table 1. Spectral indices that describe forest recovery. References note type of forest where the reference use the index to monitor forest recovery. Index Full Name Equation to Derive from Spectral Bands Reference for Spectral Response Reference Forest Type Vegetation characteristics associated with higher measures NBR Normalized Burn Ratio 𝑁𝐼𝑅 − 𝑆𝑊𝐼𝑅2𝑁𝐼𝑅 + 𝑆𝑊𝐼𝑅2 (Key & Benson, 2006) boreal, Dry Deciduous Photosynthetic and biomass NDVI Normalized Difference Vegetation Index 𝑁𝐼𝑅 − 𝑅𝑁𝐼𝑅 + 𝑅 (Vanderhoof & Hawbaker, 2018) Dry coniferous Photosynthetic content NDMI Normalized Difference Moisture Index 𝑁𝐼𝑅 − 𝑆𝑊𝐼𝑅1𝑁𝐼𝑅 + 𝑆𝑊𝐼𝑅1 (Senf & Seidl, 2022) Coniferous and mixed broad leaved Photosynthetic and structure content TCA Tasseled Cap Angle arctan (𝑇𝐶𝐺𝑇𝐶𝐵) (Pflugmacher et al., 2012) Mixed-Conifer Proportion of vegetation vs soil cover TCG Tasseled Cap Greenness -0.1603B - 0.2819G -0.4934R+0.7940NIR-0.0002SWIR1- 0.1446SWIR2 (Coefficients of: Crist & Cicone, 1984; implemented in: Pflugmacher et al., 2012) Mixed-Conifer green cover TCB Tasseled Cap Brightness 0.2043B + 0.4158G + 0.5524R + 0.5741NIR + 0.3124SWIR1 + 0.2303SWIR2 (Coefficients of: Crist & Cicone, 1984; implemented in: Pflugmacher et al., 2012) Mixed-Conifer soil cover TCW Tasseled Cap Wetness 0.0315B + 0.2021G + 0.3102R + 0.1594NIR - 0.6806SWIR1 - 0.6109SWIR2 (Coefficients of: Crist & Cicone, 1984; implemented in: Nguyen et al., 2020) Dry Deciduous moisture content (indicative of vegetation structure) 12 Land cover changes spectral reflectance, which means temporal trends in spectral reflectance can be used as proxies for structural recovery metrics, including forest structure and composition (Chu and Guo, 2013; Frolking et al., 2009; White, 2024). For example, After a fire, deciduous trees generally establish more rapidly than conifers (Johnstone, Hollingsworth, et al., 2010). And therefore, in many regions, deciduous dominant forests have faster rates of spectral recovery compared to conifer dominant forests (Figure 3, Cohen et al., 2010; McManus et al., 2012; White et al., 2023). Recovery of spectral indices also characterizes other elements of forest structure. Recovering basal area would have positive increases in greenness indices, such as the Normalized Difference Vegetation Index (NDVI), as well as indices focused on structure, such as tasseled cap wetness (TCW) and NBR (Table 2, White et al., 2019). Temporal can adjust the classic space-for-time approach, where future rates of spectral recovery could be inferred by observations in older burns with similar early spectral responses (Chu & Guo, 2014). Post-fire spectral recovery quantifies recovering vegetation, and different spectral metrics describe post-fire spectral trends (Figure 3). Common spectral recovery metrics include: the time to recovery to pre-fire values, rate of recovery, and the normalized and absolute magnitude of regrowth (Liu et al., 2021; Nguyen et al., 2018; White et al., 2019). The rate of spectral recovery, or slope, is representative of the rate of vegetation establishment (Vanderhoof et al., 2021a). However, rapid deciduous ingrowth can saturate spectral signals, which makes it challenging to detect conifer growth from spectral recovery rates (Figure 3, Vanderhoof et al., 2021a). Comparatively, measures of the normal or absolute magnitude of recovery are not diminished by saturation (White 13 et al., 2019). Metrics highlighted throughout this dissertation include: the rate of recovery (Theil Sen slope), absolute magnitude of regrowth, and the current year or value five years post-fire (Figure 3). Importantly, spectral metrics are not direct indicators of forest structure or composition (Kasischke & French, 1997). As spectral recovery does not measure forest structure directly, the association between the spectral and structural recovery may differ by forest type, region, or time-period (White et al., 2022) and requires ground-validation (White et al., 2017a; White et al., 2018; White et al., 2022). To validate spectral recovery, the extent of ground-based measurements must match the extent of spectral data (i.e., the landscape). However, developing a field study that matches the extent of satellite data is infeasible at the study level (Senf, 2022). Additionally, plot sizes of established landscape monitoring networks are often not large enough to relate to spectral responses (Senf, 2022). For example, the US Forest Inventory Plots are 170 m2, and Landsat TM pixels are 900 m2 (Bechtold et al., 2005). To verify satellite estimates of recovery, recent, research has recently integrated measures from additional remote-sensing technology, namely light detection and ranging (lidar) to match three-dimensional forest structure with spatial scale of spectra data (Coops et al., 2021). 14 Figure 3. (A) Metrics of spectral recovery for a given spectral index (NBR, NDVI, TCA, etc.) utilized throughout the dissertation (metrics: rate of recovery, recovery magnitude, and current year measure) (B) hypothetical spectral trends for a given index describe how rate could fluctuate based on recovery dynamics. Note: spectral saturation is when rate of recovery plateaus. 15 1.5 Additional remote sensing data to verify structure captured by satellites Spatially continuous lidar data rectifies the spatial mismatch between field and satellite data (Senf, 2022). Lidar sensors measure the distance to any surface by calculating the time it takes for a pulse of light, emitted from a sensor, to be reflected off a surface back to the sensor. Three-dimensional lidar data help measure complex forest structures (Lefsky et al., 1999). Lidar measures the vertical distribution of forest structure, capturing both the overstory structure driving the spectral response, as well as below-canopy forest structure (Chisholm et al., 2013; Jarron et al., 2020). Beyond merely characterizing the overstory, lidar can capture key structure components such as forest gaps (Kane et al., 2010) or the number of stems (Wallace et al., 2016). Satellite, lidar, and field plot data can be merged, creating “enhanced forest inventories”, which are spatially and temporally continuous estimates of forest structure (Coops et al., 2023). Thus, one approach to assess forest recovery across spatial and temporal scales is to combine nearly continuous time series from satellite observations with comprehensive structural forest assessments from lidar (White et al., 2017a). Lidar data can be acquired both from the ground (commonly referred to as terrestrial laser scanning or TLS) and from the air using Remotely Piloted Aircraft (RPA) systems, airplanes, and spaceborne platforms (White et al., 2016). RPA lidar, while a more recent technology, is beneficial for rapid forest structure assessments (Goodbody et al., 2017). RPA operators do not need intensive training or expensive equipment (Eugenio et al., 2020). Lidar allows forest managers to sample areas of interest without high-capital investment or exhaustive pre-flight planning (Coops et al., 2021). Spatially extensive RPA datasets, combined with field measurements, can generate wall-to-wall 16 estimates of fine-scale vegetation characteristics, including basal area and stem counts Shrestha et al., 2021). Structural assessments from lidar can be correlated to current as well as historical spectral observations. For example, Menick et al. (2024) develop a model between Landsat spectral measures and lidar-derived canopy cover. They use this model to explain the temporal trends of post-fire canopy recovery. In their research, lidar was the calibration dataset for a temporal model of forest structure. In other cases, forest inventory is initially estimated from lidar and field plot data and is then updated using satellite data (Mulverhill et al., 2025). These examples showcase how lidar-derived structural estimates, correlated with satellite data, enhances forest monitoring (White et al., 2019). Fusing lidar and satellite data facilitates equal sampling across both space and time, which is critical for robust structural estimates. Theoretically, fusion of lidar and satellite data could then assist in BC’s transition from “as-needed” post-fire monitoring (Bourgeois et al., 2018) to a more dynamic, continuous, and spatially comprehensive form of landscape monitoring (Mansuy et al., 2020). 17 Figure 4. Outline of fire disturbances and prior research for dry coniferous forests within the Western Cordillera Ecozone (Orange). The bar chart to the right is the total number of fires in km2 for each latitude band as identified by satellite data between 1984-2022. Fire locations identified using the Landtrendr algorithm (Kennedy et al., 2018) validated with the USGS MTBS (Finco et al., 2012) and the Canadian National Fire Database (Skakun et al., 2024). Fire locations are noted in black. Prior recovery reviews (large red points) and post-fire plots used in meta-analyses (white) are overlaid on ecozones (orange and green). The study area is highlighted within the black box (top).18 1.6 Dissertation rationale and gaps Changes in post-disturbance compositional and structural recovery pathways are an expected outcome of climate change (Johnstone et al., 2016). For forest managers to respond to expected changes, monitoring must capture the spatial patterns and temporal trends of structural forest recovery in recently disturbed forests. Capturing these recovery pathways (what is growing and where) must be combined with efforts to understand what drives pathways (why is growing there, Seidl et al., 2014). The synthesized knowledge of what is growing, where it is growing, and why it is growing there is indispensable for monitoring forest recovery and devising effective management treatments. (MacKenzie & Mahony, 2021). The subsequent sections outline the research needs (gaps) to identify pathways and associated ecological drivers for central Interior British Columbia. 1.6.1 Current knowledge gaps Gap 1: identifying distinct post-fire structural expressions from spectral responses The rate of spectral recovery differs by forest type, but spectral differences are not necessarily indicative of post-fire structural recovery. Importantly, the early and highly dynamic forest recovery stages are rarely studied using satellite spectra. Most studies either identify disturbances themselves or classify fully mature forests (Cooke et al., 2012). There is a need to know what distinct expressions of young forest structure, particularly silviculture important expressions, are discernible from temporal trends of spectral indices aligned with lidar forest structure estimates. 19 Gap 2: establishing links with early spectral responses and longer-term structural growth patterns Generally, multiple decades are used to differentiate structural recovery via spectral response. This 10-20 year monitoring period does not promote proactive management or adaptation, as management action occurs rapidly after fire (often less than five years; North et al., 2019). Theoretically, the historical legacy of Landsat project could improve space-for-time approaches to help identify how a recently burned site may develop in the future. For example, in coniferous forests in China, Ye et al. (2021) identified a distinct decrease in spectral reflectance that indicated the severity of future insect disturbance. Therefore, future work should investigate if distinct forest recovery pathways can be identified from these early spectral responses. Important for investigation, the number of ground-truthing samples must be large enough to calculate population-level means for different structural responses, while accounting for both spatial and temporal correlations (Guo et al., 2013). Thus, capturing a population-level mean between distinct structural responses likely requires ground-truthing unique spectral signals over large areas and across time (Bartels et al., 2016; Holsinger et al., 2022). Gap 3: understanding how known drivers of forest recovery impact spectral responses Drivers of tree establishment are generally well known at both a site and landscape level. However, the impact of a driver may vary across spatial scales and by ecosystem type. As drivers likely vary across spatial scales, it is essential to know what drivers are captured by landscape-level monitoring via spectral response. Importantly, 20 identifying how spectrally identified known recovery drivers impact structural recovery improves predictions of future forest dynamics (Weiss et al., 2023). Gap 4: synthesizing spectral and field-based assessments of post-fire forest recovery across the western cordillera As we increasingly rely on satellites to monitor post-fire recovery, we need to understand if satellite monitoring of recovery consistently corresponds with in-field observations in different studies and different regions. Further, to be able to adapt to changing fire regimes and climate conditions, we also need to understand the drivers of observed pathways, independent of study approach. 1.7 Research objectives This dissertation characterizes forest recovery across the large-fire-affected regions of interior BC, which can be remote, inaccessible, or simply too expensive for field assessments. My research models pathways of forest recovery using a lidar-derived chronosequence of post-fire forest structure measures. Spatio-temporal approaches have been applied to central interior forests (Brullsauer et al., 1996; Jull, 1990), but I focus on recovery in earlier years (<20 years vs 50-600 years). My approach combines the strengths of satellite imagery (acquired from Landsat) with detailed structural assessments from RPA Lidar to better understand the underlying drivers of recovery at a landscape scale. As a whole, I aim to improve our understanding of spatiotemporal variability in early post-fire conifer recovery pathways and how underlying ecological factors drive these pathways. The following main question guides my overall research: 21 In British Columbia’s dry-conifer forests that experience stand-replacing fire, what are the pathways of forest recovery and how are these pathways influenced by ecological drivers? To answer this question, my research addresses four key questions that form the thesis analytical chapters. Chapter 2 introduces the study region and foundational datasets used throughout the thesis. The following analytical chapters (3-6) are described in Table 2. 22 Table 2. Description of research questions by analytical chapters. Each analytical chapter notes the research gap, question, and analytical approach. Analytical Chapter Research Gap / Need Question Approach R Q 1 3 Identifying distinct post-fire structural expressions from spectral responses What is the relationship between satellite spectral trends and distinct expressions of post-fire structure in a dry conifer forest? Identify spectral trends and associated forest expressions based on RPA lidar RQ2 4 Establishing links between early spectral responses and longer-term structural growth Are unique spectral trends of recovery indicative of longer-term forest structural recovery? Identify unique early spectral trends and associated pathways of recovery from RPA lidar RQ3 5 Understanding how known drivers of structural recovery impact spectral responses Which ecosystem drivers are associated with different recovery pathways? Relate pathways of recovery with ecological drivers RQ4 6 Integrating spectral and field-based research across the Western Cordillera What drives pathways of early recovery in coniferous dominant forests of the western cordillera? Incorporate learnings of chapters 3-5 and broader literature to synthesize how spectral and field-based studies inform post-fire conifer recovery The outcome of this dissertation is landscape level forest recovery assessment as captured from temporal spectral trends, validated with lidar. This outcome addresses an urgent need to better understand recovery in Interior BC. Landscape-level recovery assessment enables association between recovery pathways and underlying ecological drivers. Figure 5 outlines the key outcomes and data sources for each chapter of analysis. 23 Figure 5. Overview of thesis analytical chapters. Colors denote data-source type (derived within thesis, red, or sourced from literature, blue) and research gaps (yellow). Orange arrows link the research gaps across chapters. 24 Study Region and Data Overview 2.1 Study region: sub-boreal spruce and sub-boreal pine spruce ecozones This dissertation focuses on recovery in dry-coniferous forests of the Western Cordillera, specifically the understudied northern extent of the region. The northern extent of the Western Cordillera is disproportionally impacted by large disturbances such as fires and infestations of western spruce budworm and mountain pine beetle (Simpson and Robinson, 2018, Figure 6). A large proportion of the northern extent of the Western Cordillera lies within the sub-boreal and sub-boreal pine (SBS and SBPS) biogeoclimatic zones of British Columbia. In these ecozones, over 40% of the forests experienced some form of disturbance from 1984 to 2021 (Natural Resources Canada, 2021). Both ecozones are prone to large and high-severity wildfires with return intervals greater than 100 years (Meidinger and Pojar, 1991). These forests are dominated by lodgepole pine (Pinus contorta), white spruce (Picea glauca), subalpine fir (Abies lasiocarpa), and trembling aspen (Populus tremuloides). Both SBS and SBPS are important for forest resources (BC Ministry of Forests, 2016), but are also remote. The 25 remote yet valuable economic nature of these forests makes them ideal regions for remote sensing-based post-fire recovery monitoring. Figure 6. Sub-boreal-spruce (SBS) and sub-boreal-pine-spruce (SBPS) biogeoclimatic zones (BEC) highlighted throughout the thesis. Black overlays define areas disturbed 1984-2022 (NTEMS data catalog, 2022). Inset map highlights location of BC (blue) within Canada. 2.1.1 Extracting fire-impacted areas for study This dissertation focuses on areas naturally recovering from moderate to high-severity wildfire, excluding all areas either planted or fertilized after fire (British Columbia Data Catalogue, 2022). High fire severity areas were identified using the Canada-wide National Terrestrial Ecosystem Monitoring Program (NTEMS) disturbance data product (Natural Resources Canada, 2021). The NTEMS disturbance data product 26 identifies change regions by identifying ‘breakpoints’, or significant decreases, in temporal trends of spectral response. Specifically, NTEMS uses the composite-2-change (C2C) algorithm described by Hermosilla et al. (2015). The C2C approach uses a breakpoint analysis of spectra of BAP composites, specifically dNBR, to identify the year of change and post-disturbance spectral trends to identify and classify a disturbance. The random forest classifies disturbances as fire, harvest, road, and non-stand replacing disturbances and is well documented across Canada as a whole (White et al., 2017b). Outputs of the C2C approach include change attribution, change year, and change magnitude (Natural Resources Canada, 2021). In this thesis, I used the change magnitude or dNBR (difference in the Normalized Burn Ratio – the ratio of red and the short-wave spectral bands before and after disturbance) to identify areas of high-severity fire events in the SBS and SBPS ecozones. It is possible to measure fire severity from other indices, but I used dNBR as dNBR is the metric currently used by the BC Ministry of Forest to map burn severity (BC Ministry of Forests, 2021). I verified the burn perimeters using a fire polygon data set that catalogues all fires yearly from aerial and satellite imagery (BC Ministry of Forests, 2021). My methods to isolate high-severity fires from all fire-disturbed areas varied based on changes in fire severity mapping methodologies of the BC Ministry of Forest between 2020 and 2024 (BC Ministry of Forests, 2021, 2023a). Chapters 4 and 5 selected areas where dNBR values ranged from 1.5 standard deviations below the mean to the maximum dNBR value for pixels within high-severity polygons from the BC fire polygon datasets 2015-2021 (BC Ministry of Forests, 2021). Chapter 6 benefited from improved availability and documentation of the BC fire severity mapping (BC Ministry of Forests, 27 2023a). Chapter 6 used Ministry standards to transform dNBR into Burned Area Reflectance Classification – Adjustable (BARC-A) values, which ranged from 0-255 (Hudak et al., 2007). I excluded areas with BARC-A values below 130, a value chosen to best align with the data used in Chapter 5. This BARC-A value constrained research areas to high and moderate severity fire. 2.2 Principle data sources In areas affected by high-severity fire, I assessed how post-fire temporal spectral trends informed subsequent forest recovery. To do so, I fused two remote-sensing data sources: (1) time series of spectral data from Landsat composites, (2) estimates of forest structure that are generated at a landscape scale from RPA Lidar and ground-based measures of forest structure. 2.2.1 Time-series of spectral data from Landsat composites As a satellite data source, Landsat analyses are frequently challenged by the presence of cloud cover or fog in images (Young et al., 2017). A variety of image compositing approaches have been used to address cloud cover (White et al., 2014), I used the annual best available pixel (BAP) compositing approach. BAP selects pixels for compositing based on the distance to a target day of the year (generally August 1st, the peak of the growing season) and the percent cloud cover (Hermosilla et al., 2016a). Selected pixels are then mosaiced to produce a composite Landsat image that limits issues associated with cloud cover (White et al., 2014). BAP composites have been successfully used for temporal analyses to identify yearly changes of forests across the Canadian landscape (Bartels et al., 2016). 28 The launch of analysis-ready data products, specifically the Landsat Tier 1 products, facilitates reconstructing long-term spectral response time series (Dwyer et al., 2018). Analysis-ready products standardize approaches to infill temporal or spatial data gaps as well as adjust atmospheric reflectance and differences among satellite sensors (Chu and Guo, 2013). To successfully curate a time series of spectral responses, one must also adjust for cloud cover or fog, as these both obscure spectral responses (Young et al., 2017). To adjust for cloud and fog, a variety of image compositing approaches have been used to select high-quality pixels as inputs in a final mosaiced image. These compositing approaches generally mask pixels assumed to be cloudy or hazy. Then they merge pixels by maximum, average, or median pixel brightness value for a given range of time (White et al., 2014). I derived time series of Landsat spectral observations from cloud-free image composites developed using a Best Available Pixel (White et al., 2014) compositing approach. It is important to know that different approaches, such as average vs maximum, will impact final spectral values (Epstein et al., 2024). In spectral recovery literature, there is no established guideline for the best approach for developing image composites. In chapters 4 and 5, BAP images were downloaded from Google Earth Engine for 1984 – 2022 (Francini, 2021). Chapter 6 utilized an updated BAP composite developed as a compendium of Hermosilla et al (2022). Both data sources used the standard parameters to select pixels: prioritizing images closest to August 1st (corresponding to the peak of the growing season for Canadian forests) with minimal cloud cover (White et al., 2014). BAP composites excluded pixels more than 30 days from August 1st or Landsat scenes that had more than 70% cloud cover. Compositing also applied a 29 despiking algorithm with a tolerance of 0.7, which uses surrounding years' values to fill pixels where the difference from the prior year is large (>70% change) and also not observed in later years (Hermosilla et al., 2024). I selected this higher value (often set at 0.2 or lower) to capture disturbances without marking them as noise (Cohen et al., 2010). In my dissertation, I use BAP composites to calculate metrics that describe the spectral trend of recovery. My dissertation relied on three spectral metrics to describe the variability in post-fire spectral response for each fire-disturbed pixel. Spectral metrics included the magnitude of regrowth between the minimum post-disturbance value and the value five years later (“regrowth magnitude”), the median rate of change after the fire (Theil-Sen slope or “slope”), and the index measure either 16 (chapter 4) or 5 (chapter 5 and 6) years post-fire. To calculate indices, all pixels had to have more than three post-disturbance cloud-free pixels within 5 years after fire. 2.2.2 Structural recovery at landsat scale with lidar data To understand temporal patterns in structural recovery, this dissertation relies on a chronosequence of spatially continuous RPA lidar-derived forest structure estimates collected in forests 5-21 years after forest fire. The following section introduces locations of lidar and field measurements, lidar acquisition and processing, and linear modeling to develop structural estimates. See Figure 7 for the location of RPA flights colored by the years since fire. 30 Figure 7. (Left) Locations of RPA flights within study area. Areas of high disturbance wildfire are noted in grey, and biogeoclimate regions are in orange. Points are colored by year of RPA flight in years after wildfire. The inset map (right) highlights the study in purple. Stratified random sampling within areas of high-severity fire (based on dNBR thresholds) was used to identify lidar flight locations (Goodbody et al., 2022). Final flight areas eliminated areas either inaccessible to field crews or where road access made RPAs flights unfeasible. A total of sixteen different post-fire areas, ten in summer of 2022 and six in summer of 2024, were included in later analyses (Figure 7). Lidar flights used a DJI M300 and Zenmuse Lidar system. Flights from 2022 used the Zenmuse L1 and flights from 2024 used the Zenmuse L2. Flight perimeters were designed to achieve an estimated post-processing point cloud density > 250 points / m2. The average flight area was 212 ha for a total of 2300 ha flown. Overall, I flew 2300 hectares, but not all areas were above the high severity threshold for dNBR. Thus, when structural estimates are related to satellite, some areas of the lidar acquisitions are excluded. Each chapter notes the overlap between structural estimates and satellite metrics. 31 Within each lidar flight area, I established two or three field plots (n = 32 plots) to collect coincident field measures for calibrating lidar-derived forest structural measurements. Plots aimed to capture the diversity of forest structure present in the study site as identified via an RPA reconnaissance flight. Precise (5–30 cm) locations of the plot centers were collected by a Trimble Geo7X GNSS unit and corrected using the BC Active Control System base stations. Each fixed area circular plot had a 17 m radius (900 m2) to correspond to the area of a Landsat pixel. In each plot, I recorded the number of stems for all trees greater than 1.3 m tall, and the diameter at breast height (DBH) of all stems ≥ 4 cm DBH. Thresholds for height and DBH were based on provincial monitoring standards (BC Ministry of Forests, 2023b). Ground cover was estimated by averaging proportional cover for conifer, deciduous, and bare earth (which included downed woody debris) from 8 radial sections of the plot and selected measures captured forest structural attributes of interest, such as basal area, stem density, and composition. These metrics are prioritized because focus groups with stakeholders in government and industry highlighted their importance for young forest assessment. After field and initial lidar data collection, lidar processing followed standard workflows, including denoising, ground classification, height normalization, and calculation of point cloud metrics (White et al., 2017b). Progressive ground filtering (Klápště et al., 2020) was used to differentiate between the ground, low-lying vegetation, and coarse-wood. Progressive ground filtering classified returns between 10-20% of returns heights as ground, iterating over increased window sizes (0.25 m, 0.33 m, and 0.5 m). Obvious and abundant coarse woody debris, which appeared as a 32 linear feature when ground classification was poor, parametrized ground filtering. To address variability in point cloud densities, I decimated point clouds so that each 25-m grid cell was one standard deviation of the mean number of returns for the flight area. I derived lidar point cloud metrics, including dispersal (van Ewijk et al., 2011), dispersion, percentiles above varying thresholds (Roussel et al., 2020), and L moments (Karvanen, 2006) at a 30 m resolution, to match Landsat’s resolution, and for each field-plot at 20cm resolution. Field-plot lidar metrics served as explanatory variables in linear models of forest structure. Lidar point clouds were georegistered in DJI Terra (version 2.1.6) and exported to Lastools for denoising, classification, and normalization (Isenburg, 2014). For an example of a normalized point cloud, see Figure 8. Figure 8. Example of RPA lidar data pre- and post-processing. Pre-processed data displays topographic variability. Post-processed point cloud is normalized to heights above ground. 33 Spectral Recovery Linked to Post-Fire Forest Structure in Dry Sub-Boreal Forests1 3.1 Introduction Forest disturbances such as wildfires can dramatically alter forest structure and composition, increasing the likelihood of ecosystem changes. Methods that safely, efficiently differentiate silviculturally beneficial coniferous growth from barren ground or deciduous shrubs are necessary to inform post-fire forest management. Satellite imagery can detect burn patterns, but monitoring changes in forest structure post fire is challenging due to complex vegetation responses. Further, in-field measures of live and dead vegetation post-fire is challenging because areas impacted by wildfire may be remote, difficult to access, and/or dangerous to survey. The difficulties of post-fire monitoring are compounded by the global increase in the frequency and severity of disturbances, as expansion of disturbed areas also increases the number and size of areas requiring post-disturbance monitoring. The utility of Landsat and other satellite data for classifying burn-severity based on remaining forest structure post-fire is well-established – but the utility of Landsat for recovery modeling is less well-established. Remote sensing derived metrics of severity, such as dNBR, strongly correlate with in-field measures of burn severity (Chen et al., 1 The contents of this chapter are adapted from: Smith-Tripp, S. M., Coops, N. C., Mulverhill, C., White, J. C., & Axelson, J. (2024). Landsat assessment of variable spectral recovery linked to post-fire forest structure in dry sub-boreal forests. ISPRS Journal of Photogrammetry and Remote Sensing, 208, 121–135. https://doi.org/10.1016/j.isprsjprs.2024.01.008 34 2011; Miller & Thode, 2007). While differences in disturbance severity can describe post-disturbance forest structures (e.g., tree density; Cocke et al., 2005), severity metrics are influenced by prior ecosystem events (Harvey et al., 2019) and are not indicative of ecosystem recovery (Keeley, 2009). Other approaches, like the use of radiative transfer models or spectral mixture models can also be used to characterizing post-disturbance recovery. However, radiative transfer models often require extensive parameterization for meaningful inference (Fernández-Guisuraga et al., 2021; Solans Vila & Barbosa, 2010), and spectral-mixture models require additional assumptions on what comprises a spectral end-member (Lewis et al., 2017). Ideally, Satellite-based measures of ecosystem recovery should include a suite of structural metrics that capture recovering ecosystem structure, such as stem densities or biomass, among others Rapid deciduous recovery after fire can obscure the relationship between satellite spectral observations and conifer structure. To overcome the issue of deciduous ingrowth, recent work suggests that incorporating multiple spectral indices (i.e., combining indices like the Tasseled Cap, TC, measures with NBR) may better explain landscape dynamics (Cohen et al., 2020). Combinations of spectral indices have been used to update stand-level inventories for mature forest structures (Bolton et al., 2018), yet, of these multi-index models do not focus on capturing measures of early forest recovery – particularly relevant to silviculture. Spectral-mixture models also avoid index saturation and limit background effects (Adams et al., 1995). While they present a viable post-disturbance monitoring solution, past research does not capture a suite of silvicultural metrics, particularly forest structure metrics (Fernandez-Manso et al., 2016). 35 In this case, spectral-mixture models do not make sense for modeling structural metrics such as basal area or stem density because there is no clear spectral 'end-member'. Linkages between Landsat spectral observations of recovery and distinct states of post-fire structural development have been explored using field plot (White et al., 2019, 2023) and airborne lidar data (Chirici et al., 2020; Senf et al., 2019; White et al., 2018). However, less research has been undertaken to assess silviculturally focused metrics (introduced in Chapter 3) to satellite spectra. Given the expanding extent of wildfire areas, cost-efficient lidar data offers ‘ground-truthing’ of relevant forest structural estimates at scales coincident with satellite spectra (Pajares, 2015). To overcome the challenge of linking satellite to recovering forest structure, this chapter combines post-disturbance spectral trend metrics from a time series of historical Landsat imagery with field and RPA lidar acquired in post-fire lodgepole pine (Pinus contorta) sub-boreal forests of central British Columbia, Canada. I ask how Landsat time series data can inform the spatially variable return of forest structure following a wildfire. My objective was to determine whether distinct expressions of forest structure, particularly expressions important for silviculture, are discernible using temporal trends of spectral indices in locations of high burn severity. I focused on structural recovery metrics of interest to forest managers, including: basal area, stem counts, and species composition. Of these metrics, I hypothesized that different composition would have the strongest differences in spectral trends, as deciduous trees are spectrally distinct from conifers and deciduous forbs generally rapidly cover the ground post-fire (Vanderhoof & Hawbaker, 2018). I evaluated the relationship between Landsat spectral trends of recovery and forest structure metrics derived using field plot measurements combined 36 with high-density (> 200 pts/m²) RPA lidar data in an area impacted by wildfire that burned in 2006, 16 years before the field-data assessment. 3.2 Selected study sites The Quesnel Timber Supply Area (TSA) is located on the interior Plateau of British Columbia, a mid-elevation (500 -1200 m) geographic area with rolling terrain composed of dry interior forests. These dry interior forests are expected to increasingly experience high-severity fires due to both climate change and forest management legacies (Hessburg et al., 2005). The western TSA, where study sites are located, is generally drier than eastern regions, with relatively cool summers and short shoulder seasons (Steen & Coupé, 1997). Study sites are located in fires that burned in 2006, before a large-scale MPB outbreak affected many of the other lodgepole pine forests in the region. Two study sites were located in the same fire area, the Watlus Lake fire – which burned ~8,500 ha, and a third site in an unnamed fire that burned ~10,800 ha. 37 Figure 9. Top: Location of research sites in Quesnel TSA. Black areas are the disturbed areas (fire, harvest, road, etc) for the past 20 years. Blue dots show the study sites. Below: Outlines of study fires colored by dNBR fire severity (low = yellow, high = purple). Flight areas are outlined in blue. The inset map shows BC study site in reference to Canada. 38 Figure 10. Orthomosaics of selected study sites flown with RPA lidar. All mosaics are at the same spatial scale. 3.3 Methods To assess structural forest recovery over large scales, I created wall-to-wall estimates of forest structure for selected study sites using lidar data. These estimates were used to investigate the ability of temporal trends of satellite spectra to discern distinct expressions of forest structure. This chapter’s investigation is divided into three steps: cluster analysis, association with structure, and application of clustering to a larger fire area. First, I derived post-fire temporal trends of Landsat spectral measures for the seven selected indices (Table 1) from 2005 to 2021. Temporal trends were delineated into segments, henceforth referred to as spectral metrics. Metrics were used 39 as inputs into a k-means data-clustering algorithm that identified unique clusters of spectral trends. Second, I assessed the association between each spectral cluster and three wall-to-wall structural metrics derived from the RPA and field data: (1) BA, (2) stem counts, and (3) coniferous to deciduous ratio. Third, I applied the clustering algorithm across a burned landscape where there are no historical structural measures. 3.3.1 Wall-to-wall forest structure predictions From RPA lidar data, I estimated three silviculture measures of forest structure (BA, stem counts, and the coniferous to deciduous ratio) using generalized linear models (GLMs). GLMs correlated field measures with lidar metrics such as L-moments, dispersion, cover, and other standard height metrics as listed by Falkowski et al. (2009). The wall-to-wall approach was based on best practices outlined by White et al. (2017b). BA models used a two-step segmented model process, which first modeled the probability of measurable BA from a logit link function. BA models were augmented with plot measures in undisturbed forests to ensure that the model was accurate in regions with a higher BA. For stem count models, I used zero-inflated negative binomial regressions that estimated the number of stems >1.3 m for each Landsat pixel. This included a conditional model that calculated the likelihood that there were stems in the plot and then a model for the number of stems in each plot. Predicted stem counts represent the number of stems in the size of each Landsat pixel (900 m²; hereafter stems/pixel). Finally, I used a beta regression to model the coniferous: deciduous ratio. For all models, lidar metrics with strong inter-correlations (Spearman correlation > 0.7) were not used. Final reported model coefficients and accuracy were output from a k-folds algorithm with 100 iterations. Final model predictions across the study area were 40 built from model coefficients and represented spatially-explicit estimates of BA, stem counts, and percent conifer for the entire area of RPAS data at each site. Figure 11. Conceptual overview of study approach. First section (3. Data) describes data collection including satellite data and estimates of forest structure. Second section (4. Methods) shows model development in three parts: (1) cluster development and assessment, (2) cluster association, and (3) application of clustering to larger fire area. 41 3.3.2 Unique satellite spectral trends from cluster analysis 3.3.2.1 Clustering of temporal spectral metrics The clustering algorithm incorporated three spectral metrics computed for the seven Landsat spectral indices (NDVI, NBR, NDMI, and the tasseled cap transformations: TCB, TCG, TCW, and TCA) for each pixel within the study area. Spectral index selection was based on a review of the literature for the most pragmatic indices for both disturbance classification and subsequent forest monitoring (Hermosilla et al., 2015; Nguyen et al., 2020; Pflugmacher et al., 2012; Senf & Seidl, 2022; Vanderhoof & Hawbaker, 2018; listed in Table 1). Components of spectral trends were based on those of Nguyen et al. (2018) and White et al. (2017b) and included: (1) the magnitude of change between the year of disturbance and 2021 (recovery magnitude), (2) the Theil-Sen slope of the post-disturbance spectral trend, and (3) the spectral value for the index in 2021 (see Figure 3; Nguyen et al., 2018). Given the heterogeneous nature of fire severity, several patch-level metrics, including burn severity (dNBR) and a spatial average of burn severity, were included to better represent known ecological recovery patterns within a forest stand. In total, there were 23 separate spectral metrics. Given the high dimensionality of the data, I partitioned clusters using the Manhattan distance (Aggarwal et al., 2001). I selected an optimal number of clusters using the gap statistics, which suggested ten initial seeds. The gap statistics measure the increase in explanatory power between n and n+1 clusters (Kassambara & Mundt, 2016). Final cluster centers were outputs of a k-means algorithm with a maximum iteration of 200. Cluster similarities were tested using cluster bootstrapping with 100 iterations of the 42 Jaccard index (Kassambara & Mundt, 2016). Clusters that shared NBR recovery slopes were merged, which eliminated similar clusters. Spectral characteristics of each cluster were investigated using a Principal Component Analysis (PCA), including the coefficients associated with the PCA transformation or the PCA loadings. Following the PCA analysis, I investigated the average spectral metrics for each cluster. Spectral indices were related to the relative difference from a baseline period, defined for a stable forest period, 1996 – 2005. As I was most interested in associating structural characteristics with each cluster, initial clustering was constrained within the RPAs' lidar acquisition area. To then extend the k-means clusters to other areas within the fire perimeters, I applied a maximum likelihood classification using the same inputs as k-means, where classes were defined by initial clustering (Richards & Jia, 2006). The original k-means cluster area was the source for 80% training (N = 3615) and 20% testing (N = 1445) data. The final maximum likelihood accuracy was assessed by comparing the accuracy of the classifier with the test data. I also evaluated the contiguity of spectral clusters on the landscape using each cluster’s total and patch area size and proportions (where a patch would be a pixel with eight identical neighbors). The Landscape metrics R package was used to estimate cluster continuity on the landscape (Hesselbarth et al., 2019), with differences in forest structure assessed among spectral clusters using the FSA R package (Ogle et al., 2022). 3.3.2.2 Relating spectral trends to forest structure Spectral trends were characterized by their BA, stem counts (stems/pixel), and the ratio of coniferous to deciduous (coniferous: deciduous). Tests for differences in forest 43 structure among clusters used a Kruskal-Wallis nonparametric test and a two-sided post hoc Dunn test using the Sidak correction to control for error among multiple comparisons (Dinno, 2017). All modelling was completed using base R stats (R Core Team, 2019), ‘betareg’ (Cribari-Neto & Zeileis, 2010; Grün et al., 2012), and ‘glmmTMB’ packages (Brooks et al., 2017). 3.4 Results 3.4.1 Wall-to-wall forest structure predictions Linear models successfully described the relationships between lidar estimates and field measures (Table 3). Field-measured plots had an average BA of 6.8 ± 8.65 m²/ha, 0.08 ± 0.21 % coniferous, and an average stem count of 321 ± 294 (stems/pixel). For linear estimates of these models, BA had the strongest agreement with field measures (R2 = 0.95), but was variable due to the high proportion of plots with low BA (RMSE = 6.285). This was followed by predicting whether an area had measurable trees (area under the curve (AUC) = 0.79). The overall fit of the stem count model was moderate (R2 = 0.52, RMSE = 121). The coniferous:deciduous ratio had the lowest correlation to field data with a pseudo-R2 of 0.41 (RMSE = 0.11). Similarly, sites displayed variability in wall-to-wall estimates of structural recovery (Figure 14). Across all sites, 66% of pixels had a measurable basal area (> 0 m²/ha; Table 4). In general, estimates of the average basal area were variable within and across sites (average = 0.77 ± 0.81 m²/ha). Stem count estimates were also variable (596 ± 500 stems/pixel). Overall, the sites were primarily coniferous, with all the sites having more than 50% coniferous stems (Table 4). Figure 14 shows the spatial variation in the forest structure of all sites. 44 Figure 12. Structural estimates predicted from lidar compared to field measurements for each 900 m2 field plot. Panels, from left to right, are: basal area (m2/ha), ratio of conifer to deciduous stems (1 = 100% conifer), and # of conifer stems > 1.3 m in height for each 900 m2 plot. Final model details, including parameter estimates and overall accuracies, are included in Table 3. Figure 13. Density distribution of forest structure metrics for three study acquisition areas. Black lines are the median of the estimates. Total number of unique pixels is 5909 (site 1 – 2073, site 2 – 2065, and site 3 – 1335 pixels). Density distributions show the square root transformation of pixel count to display structural estimates variability better.Basal Area (m2 hectare) Coniferous :Decidious Stem Counts (stems pixel)1 2 3 1 2 3 1 2 30500100015000.000.250.500.751.000123Site NumberModel Estimate45 Figure 14. Three lidar flight areas colored by estimate of forest structure (basal area m2/ha, coniferous: deciduous, # stems / 900 m2) based on ABA models (Table 3). 46 Table 3. Outputs of general linear regression models generated using lidar data and field plot measures (N = 26). Names of beta slope estimates, standard errors are noted. For each model, a measure of fit appropriate for model type (R2 for continuous linear models and area under the curve (AUC)) is noted. Model fit values were computed as the average of all k-fold cross-validation estimates. Models Model Type Df Beta (slope) Estimate Standard Error of Estimate Measure of Fit RMSE P (Basal Area > 0) Binomial 25 Intercept 2.79 0.61 AUC = 0.75 L-Kurtosis -6.16 1.22 Basal Area Gamma 17 Intercept 12.50 1.49 R2 = 0.95 6.285 L-Kurtosis -16.08 1.55 % Of pts > mean 12.50 1.49 Mean absolute deviation from the median 0.37 0.04 Stem Count Zero-inflated Negative Binomial Intercept 7.94 0.94 R2 = 0.52 121 *Proportional Model 6 % Of pts > mean -0.06 0.01 Canopy Relief Ratio -4.70 1.42 L-Kurtosis -0.04 0.01 L-coefficient of Variation 0.01 0.00 *Precision Model 6 Intercept 1.41 1.21 Canopy Relief Ratio -26.37 8.74 Coniferous : Deciduous (ratio) Beta regression 6 Intercept 0.23 8.37 R2 = 0.41 0.11 * Mean model % Of pts > mean -0.02 0.13 Canopy Relief Ratio 28.47 14.61 L-Kurtosis 6.06 7.09 L-coefficient of Variation -11.40 3.76 *Precision estimate Precision 35.35 75.94 47 Table 4. Average structural metrics for each study site and averages for all study sites. +/- is the standard deviation. Site # Basal Area (m²/ha) +/- Coniferous: Deciduous +/- Stem Counts (stems/900m²) +/- Proportion w/ Basal Area 1 0.56 0.72 0.87 0.19 556 463 0.63 2 1.00 0.86 0.98 0.08 650 523 0.81 3 0.21 0.48 0.72 0.32 230 243 0.51 All Sites 0.62 0.78 0.87 0.23 509 472 0.66 3.4.2 Clusters of unique spectral recovery trends The clustering algorithm identified six unique spectral clusters that captured 81.6% of the variation in the input data for the study sites. Figure 15 shows a biplot of the first two principal components of a PCA on clustering analysis data. The first and second PCs collectively represented 68% of the total variability in the dataset. When scaled based on the proportion of variance, important metrics to describe dataset variable (principal component loadings) included the Thiel Sen slope of NBR, TCA, and NDMI, and the regrowth of TCA and NBRf. In the PCA plot, clusters 2 and 3 have the highest spectral overlap. Clusters 1 and 6 are the most distinct (Figure 15). 48 Table 5. Eigenvectors for the first and second principal component of the principal component analysis. The colored column is the scaled magnitude of PC1 and PC2 scaled using the Pythagorean theorem. Values for PC1 and PC2 are proportion of variance explained by the spectral metric for each component. 49 Figure 15. Principal component analysis of pixels based on spectral indices. Points are colored by spectral clusters. Ellipses show where 50% of the data plots. Loadings are the scaled proportion of variance in PC1 and PC2. Figure 16 shows spectral index trends following the fire, normalized to a pre-fire baseline (1996-2005). Most indices show similar trends of decreasing after disturbance, rapidly increasing, and plateauing to steady recovery at or above baseline values. Changes were most rapid for NDVI and TCB, where most TCB values surpassed the baseline less than a year after the disturbance (Table 6). Conversely, NBR and TCA increased at slower rates. In cluster 1, both NBR and TCA took > 10 years to surpass baseline values (Figure 16; Table 6). Notably, the majority of NDMI and TCW pixels did not recover to pre-baseline values in most clusters (Table 6). 50 Clusters generally exhibited similar temporal patterns of spectral recovery, but clusters were variable in their degree and magnitude of recovery. Clusters 2 and 3 had the greatest variability for within-year measures (shown by the line width of Figure 17) and often overlapped the range of values in other clusters. In general, clusters 5 and 6 had the highest values for most spectral indices (Figure 17). Cluster 1 had the largest decrease after the disturbance event and took the longest to surpass baseline values. Additionally, less than 10% of pixels in cluster 3 surpassed baseline values for TCA and NBR in 2021, 15 years after the disturbance. More than 50% and at least 10% of pixels in other clusters (1,2,4,5,6) surpassed baseline values for NBR and TCA, respectively (Table 6). Average spectral measures for measurement year (2021), regrowth magnitude, and Thiel-Sen slope differed by spectral cluster (Figure 17). For the current year measure, cluster 6 had the highest index measures and cluster 3 had the lowest, except for TCB (Figure 17 and Table 6). Regrowth magnitudes were highest for cluster 6, except for TCB, where cluster 3 is the highest (Figure 17). Cluster 6 generally had the lowest slope of recovery for all indices. Similar to regrowth magnitudes, the average Thiel-Sen slopes were highest for cluster 1, except the average TCB slope, which was highest for cluster 2. Clusters 2 and 3 had similar average magnitudes of recovery for both TCG and TCB (Table 6). Clusters 2 and 3 also had lower recovery slopes than cluster 1, except for TCB, where cluster 2 was higher. Slopes of recovery for NBR, NDMI, NDVI, and TCA were all significantly different (p < 0.1; Table 6). 51 Figure 16. Spectral trends of six (shown in color) spectral clusters. “Pre-fire baseline” based on observations of 10 years preceding the fire (1995-2005). Values are the difference (%) from the pre-fire baseline, where 0 (black line) is no difference from baseline measures. Yearly values are two-year moving averages, with the line width equal to the standard deviation by spectral cluster and band. The vertical dashed line is the year of the fire (2006). TCWTCB TCGNDVI TCANBR NDMI2005 2010 2015 20202005 2010 2015 2020 2005 2010 2015 20202005 2010 2015 2020 2005 2010 2015 20202005 2010 2015 2020 2005 2010 2015 2020-200-150-100-50050-80-40040-100-50050100-150-100-500-60-30030-50-250255075-300-200-1000100% Difference Pre-Fire BaselineSpectral Cluster123456 52 Figure 17. Average spectral metrics for each cluster for seven spectral indices colored by spectral clusters. 2021 Measure is the summer 2021 Landsat measure, regrowth magnitude is the overall change from 2006 to 2021, and the Thiel Sen slope is the estimate of the regression line between 2006 and 2021. Note: to allow metrics to plot on similar scales, NBR, NDMI, and NDVI are multiplied by 1000. 53 Table 6. Average spectral metrics by index and cluster (1-6). The proportion of each index that is greater than baseline in 2021 and the average number of years to surpass baseline are also listed. Statistically similar spectral components (based on a Dunn-Test p < 0.1) are noted with the adjusted P-value. Metric Index 1 2 3 4 5 6 Like Pairs (Dunn-test) Adjusted P-Value Regrowth Magnitude NBR 0.6 0.4 0.5 0.2 0.1 0.0 All Unique NDMI 0.4 0.2 0.3 0.2 0.1 0.0 All Unique NDVI 0.4 0.3 0.4 0.2 0.1 0.1 4 - 5 0.88 TCA 2.5 1.7 2.0 1.0 0.9 0.3 4 - 5 0.24 TCB 0.9 1.0 1.1 0.6 0.5 0.2 All Unique TCG 1.1 0.9 0.9 0.7 0.6 0.2 4 - 5, 2 - 3 0.21, 0.12 TCW 0.6 0.2 0.3 0.1 0.1 -0.1 4 - 5 1 2021 Measure NBR 0.5 0.4 0.3 0.4 0.5 0.6 1 - 4 0.41 NDMI 0.2 0.1 0.0 0.2 0.3 0.3 5 - 6 0.53 NDVI 0.7 0.7 0.6 0.7 0.8 0.8 5 - 6 1 TCA 2.9 2.6 2.1 2.8 3.1 3.2 5 - 6 0.99 TCB 2.3 2.5 2.4 2.4 2.4 2.5 4 - 5 , 2 - 5 1 , 0.17 TCG 1.3 1.2 0.9 1.3 1.5 1.5 1 - 4 0.66 TCW -0.9 -1.1 -1.3 -1.0 -0.8 -0.8 5 - 6 1 Thiel Sen Slope * 1000 NBR 30.1 16.0 23.8 9.7 5.6 0.7 All Unique NDMI 26.3 14.3 18.4 9.6 7.0 4.3 All Unique NDVI 18.7 11.2 13.2 8.6 7.5 4.2 All Unique TCA 121.6 71.8 91.4 52.0 43.8 19.4 All Unique TCB 32.3 48.2 47.3 33.2 16.7 9.2 1 - 4 , 2 - 3 0.84 , 0.43 TCG 62.3 47.9 47.9 37.4 28.3 13.6 2 - 3 1 TCW 29.0 -2.2 5.9 -0.5 2.1 3.2 2 - 5 , 2 - 4 0.12 , 1 Proportion > Baseline 2021 NBR 0.4 0.1 0.0 0.1 0.5 0.5 NDVI 1.0 0.8 0.6 1.0 1.0 1.0 TCW 1.0 1.0 1.0 1.0 1.0 0.9 TCB 1.0 1.0 1.0 1.0 1.0 1.0 TCG 1.0 1.0 0.8 1.0 1.0 0.9 TCA 0.8 0.5 0.0 0.7 0.9 0.8 NDMI 0.0 3.0.0 0.0 3.0 3.0 0.0 Years To Pass Baseline NBR 13.3 9.5 5.0 6.5 4.2 1.7 NDVI 6.1 4.8 6.9 2.8 1.2 0.8 TCA 10.1 7.5 7.3 5.4 2.2 1.0 TCB 1.3 1.4 1.6 1.3 0.9 0.2 TCG 6.7 5.3 8.1 3.6 1.9 0.7 TCW 5.8 6.0 3.9 8.0 7.2 7.2 NDMI 3.0 3.0 3.0 54 Table 7. Average structural metrics for each spectral cluster (+/- is the standard deviation). The study area refers to the total area covered by each cluster for the lidar flight acquisitions, and the Fire area is the entire 2006 Watlus Lake fire perimeter based on the maximum likelihood classification. Cluster Coverage (%) Study Area Mean Area (ha) +/- Fire Mean Area (ha) +/- Basal Area (m²/ha) +/- Stem Counts (stems/pixel) +/- Coniferous: Deciduous (%) +/- 1 32.7 7.31 1.52 6.67 1.05 0.85 0.71 623 428 96 9 2 27.8 5.39 0.89 6.80 0.87 0.42 0.61 503 352 89 15 3 8.2 5.91 1.00 5.33 0.79 0.42 0.57 750 402 82 16 4 11.2 3.48 0.28 4.52 0.47 0.52 0.63 421 343 84 20 5 10.3 4.11 0.27 5.13 0.63 0.42 0.60 356 303 78 22 6 8.4 5.72 0.65 6.27 0.90 0.22 0.47 222 266 56 32 3.4.3 Relating spectral clusters and measures of forest structure Spectral clusters represented significantly different measures of forest structure (Figure 18, Table 7, Table 8). BA estimates were highest for cluster 1 (0.85 ± 0.72 m²/ha) and lowest for cluster 6 (0.22 m²/ha). All clusters had unique counts of coniferous stems per Landsat pixel. Cluster 3 had the highest stem densities (750 ± 402), followed by Cluster 1 (623 ± 428), and Cluster 6 had the lowest (222 ± 266, Table 7). Areas with high stem counts and/or BA were spatially distinct from regions with a high deciduous composition (i.e., clusters 1 and 3 are distinct from cluster 6). For composition estimates, cluster 1 had the highest proportion of coniferous stems (96%), followed by cluster 2 (89%). Cluster 6 had the lowest coniferous to deciduous ratio (56 %). 55 Figure 18. Distribution of structural metrics: basal Area (m²/ha), stem count (stems/pixel), and the coniferous:deciduous ratio for each spectral cluster. The boxes note the interquartile region, and the black lines are the median values. Asterisks note spectral clusters with statistically similar structural estimates (Dunn test p < 0.05). 1 Basal Area (m2 hectare) Coniferous :Deciduous Stem Counts (stems pixel)1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 60500100015000.000.250.500.751.000123Spectral ClusterLidar Model EstimateSpectral Cluster123456* * * * * * * * * 56 Table 8. Outputs of the Chi-Squared test and the Dunn test for each structural metric. Only statistically similar pairs are listed (based on a P-value < 0.05). Variable Chi-Squared Test Like Pairs (Dunn-test) Adjusted P-Val Basal Area Χ 2(5, N = 6492) = 781.23, p = 0 2 - 5, 2 - 3 1 Stem Counts Χ2 (5, N = 6492) = 1194.69, p = 0 All Unique Composition Χ 2 (5, N = 6492) = 1515.42, p = 0 2 - 4 0.59 3.4.4 Spectral clusters applied to fire landscape Figure 19 shows a maximum likelihood classification of the spectral clustering of the 2006 Watlus Lake fire perimeter, which expands from the original clusters constrained to areas with forest structure measures. The overall classification accuracy was 0.83 % (Table 9). Cluster 1 was the most commonly occurring cluster, covering 33% of the land area (Table 9). Cluster 3, the least common cluster, accounted for 8.2 % of the land area and had the fourth-largest average patch size (5.33 ha), where a patch denotes an area of at least 8x8 pixels. Cluster 4 had the smallest average patch size (4.52 ha). In the classification, cluster 1 was the most accurately classified (user accuracy = 0.91; producer accuracy = 0.88, Table 9), and cluster 6, which represented the second smallest proportion of the landscape, was the least accurately classified (user accuracy = 0.7; producer accuracy = 0.68). Clusters 5 and 6, which were generally spatially grouped (Figure 19), were frequently misclassified as one another (Table 9). In general, areas of high BA (clusters 1,2,3, and 5) were grouped and proximal to areas with a high number of conifer stems, like cluster 3 (cluster 1 and is adjacent to cluster 3 Figure 19). 57 Figure 19. Spectral classification 2006, Watlus Lake fire recovery. Sites covered by lidar are outlined in orange. White areas did not have enough spectral coverage for classification. 58 Table 9. Confusion matrix of maximum likelihood classification for the Watlus Lake fire using spectral clusters (pixel # = 1445). Overall accuracy shown in bold. Predicted Values Reference Values 1 2 3 4 5 6 User Accuracy 1 445 53 9 1 0 0 0.88 2 46 348 10 18 5 3 0.78 3 11 17 140 0 0 0 0.88 4 3 24 0 127 13 1 0.78 5 2 5 0 16 101 15 0.78 6 0 0 0 0 10 22 0.54 Producer Accuracy 0.88 0.81 0.83 0.76 0.73 0.69 Overall Accuracy 0.82 59 Figure 20. Overview of research methods and findings for research question 1. Left: Data inputs from lidar and Landsat highlighted in black diamonds. Middle: Landsat spectral trends and lidar cross-sections of associated structural forest expressions. Right: Classified outputs of spectral trends for the 2006 Marquis lake fire. 60 3.5 Discussion This chapter used recovery trends of various Landsat spectral indices to identify distinct types of post-fire spectral recovery, which indicated different variants of post-disturbance forest structure (Figure 20). Such research is critical because the increasing frequency and severity of wildfires make ground assessments a difficult singular monitoring strategy (Huang et al., 2019), and satellite monitoring approaches struggle to capture distinct and important silviculture measures (Chu & Guo, 2014). The variable forest spectral types observed within this chapter corresponded with variable predictions of forest structure derived from integrated field plot measurements and high-density RPA lidar data. Specifically, some clusters (e.g., cluster 1) demonstrated a higher proportion of coniferous recovery than spatially adjacent clusters (namely, cluster 3). Cluster 3 had high coniferous stem densities, but also a higher proportion of deciduous species. Spectrally distinct locations of poor conifer recovery, like cluster 3, may represent areas transitioning from coniferous to mixed forest types (Epting & Verbyla, 2005; Hayes & Buma, 2021). Cluster 3 also returned to baseline values of NBR eight years before cluster 1. The unique spectral trends identified with k-means clustering showcase the capacity to monitor structural differences, particularly those relevant to forest management, using spectral measures. With large-scale and intense fires in northwestern North America, recovery monitoring must capture spectral signatures that signal a transition from pre-disturbance stands (e.g., those dominated by lodgepole pine) to those with a higher proportion of deciduous trees (Hayes & Buma, 2021; Kiel & Turner, 2022; White et al., 2023). 61 Relevant benchmarks for strong coniferous recovery relate to historical work in similar xeric subalpine fire-prone forests. A recent BC study surveyed carbon sequestration and stem densities 10 to 60 years post-fire and found young regenerating forests had highly variable stem densities, anywhere from 100 – 10000 stems/ha (Clason et al., 2022), or 90 to 900 stems per Landsat pixel. My results were similarly variable; many clusters had high stem counts (average stem counts are all greater than 200 stems per pixel), but estimates of stem counts were highly variable. A meta-analysis of post-fire recovery in dry coniferous forests of Colorado found BA was generally greater than 0.2 m²/ha twenty years after a fire event (Donnegan & Rebertus, 1999). In this investigation, all clusters had an average BA higher than 0.2 m²/ha, but, similar to stem counts, responses were variable. It should be acknowledged that many studies of forest recovery opt to include trees below breast-height in BA calculation (e.g., Clason et al., 2022). However, the BA variability I observed has also been found in other models of early forest recovery. For example, a Canada-wide study that included post-disturbance basal area modeled a negative BA up to 10 years post-disturbance, but most forests were greater than 3.3 m²/ha 20 years after a disturbance (Bartels et al., 2016). The dramatic change in BA in-between estimates at 10 and 20 years by Bartels et al. (2016) and the variability in BA within and among spectral clusters suggest that 10-20 years after a fire event, the forests undergo a period of rapid, but spatially variable BA growth. Spatial variability could result from site-level differences, such as soil moisture. The study by Clason et al. (2022) found BA was highly variable based on moisture dynamics. I did not consider the variability in BA 62 associated with soil moisture dynamics, and support that soil moisture is an important area of future research to understand recovery dynamics better (Talucci et al., 2019). 3.5.1 Clusters derived from spectral trend metrics capture unique post-disturbance recovery The six distinct spectral recovery clusters derived from spectral trends of Landsat indices represent a range of young post-fire forest recovery responses. The most frequently occurring cluster (cluster 1) covered 33% of the area within the fire perimeters and had the greatest BA and the greatest coniferous proportion. Regions with strong coniferous recovery, such as cluster 1, were spectrally defined by a relatively slow but consistent spectral recovery from 2006 to 2021. Such recovery patterns are consistent with other landscape studies, which show dense coniferous forests have longer recovery times and a greater change magnitude or slope of recovery than mixed and broadleaf forests (Epting & Verbyla, 2005; White et al., 2022, 2023). I assessed the degree of recovery as a measure of return to a 10-year baseline, longer than some other studies looking at post-fire recovery (Gómez et al., 2014; White et al., 2017a). Disturbances in the baseline period would influence recovery dynamics because landscapes recover differently when there are multiple disturbances (Kiel & Turner, 2022; Nguyen et al., 2018). However, before the fires burned the research sites, the landscape did not undergo any major disturbances, including harvest or wildfire (Zhu et al., 2020). While I argue a 10-year baseline was acceptable for research purposes, the overall influence of the baseline period on spectral recovery is important, as the variability of ecosystems’ spectral values is highly site-specific (Pasquarella et al., 2016) 63 The spectral responses for areas with low coniferous ingress behaved similarly across all indices. Clusters where deciduous growth was more pronounced (clusters 3, 5, and 6) had rapid spectral recovery of NDVI, NBR, NDMI, and TCG that then plateaued at or slightly above historic baseline values. Cluster 6, which had the lowest proportion of BA, stem counts, and coniferous ingress, exemplified this swift spectral recovery. A review of RPA orthophotos collected coincident with lidar data indicated that cluster 6 was located in wetlands; this was verified by the negative regrowth in TCW as a result of water reflectance (Crist & Cicone, 1984). A study that assessed the utility of various indices to differentiate between deciduous, harvested, and closed canopy stands found TCW best identified deciduous forest types (e.g., Populus spp), because deciduous leaf water concentration dominated TCW reflectance (Czerwinski et al., 2014). Rapid recovery of cluster 6 was likely associated with deciduous willows or trembling aspen (Salix spp or Populus tremuloides), either not consumed in the fire or that regrew or resprouted rapidly (Bartels et al., 2016; Dymond et al., 2002). Clusters 4 and 5 exhibited similar rapid spectral recoveries and eventual plateaus, but were generally less pronounced than the spectral recovery patterns of cluster 6. Clusters 3-6 rapidly recovered to pre-baseline values and had higher 2021 spectral index values compared to clusters 1-2, suggesting regions with high deciduous growth coincide with accelerated spectral recovery (for NBR, TCA, and TCG, these clusters surpassed baseline early on post-disturbance compared to clusters 1-3). The rapid spectral recovery associated with strong deciduous growth is consistent with past research, including a multi-year study of boreal forests where fast spectral recovery also corresponded to deciduous growth (White et al., 2023). Further, the rapid recovery of 64 TCG and TCA corresponds with a Canada-wide study reviewing different spectral indices for post-disturbance monitoring, which found that measures like TCG, which rapidly recovered and then stagnated, were advantageous for monitoring deciduous ingrowth (Pickell et al., 2016). However, the same study found that measures such as NBR were slower to stagnate and, thereby, better for monitoring structural non-deciduous recovery (Pickell et al., 2016). In this study, NBR and TCG, when analyzed in combination, were both informative of rapid deciduous recovery, a rapid recovery that stalls several years after disturbance. Select indices were particularly useful for differentiating between distinct spectral trends and structural recovery. TC indices (namely TCW) use visible, NIR, and SWIR, transformed into a singular value for landscape brightness, wetness, and greenness. These indices helped differentiate between areas of similar greenness, where one is coniferous and one is deciduous (Horler & Ahern, 1986). For example, all clusters had smaller TCW slope estimates except cluster 1, which was 25% greater than the other metrics (Table 6). The significant change of TCW slope for conifer-dominated cluster 1 could result from strong evaporative control by conifers, which would increase TCW (Moreno-Fernández et al., 2021). Cluster 2 had a similar conifer proportion to cluster 1, but had a lower basal area and stem counts. The minor change in TCW of cluster 2 was likely due to a decrease in conifer density. Another helpful index to differentiate between clusters 1 and 2 was TCB. TCB was the only index where the magnitude and slope of recovery of clusters 2 and 3 were higher than cluster 1. TCB values are likely higher because clusters 2 and 3 had lower basal area estimates than cluster 1. Higher TCB values in clusters 2 and 3 could relate to increased soil reflectance, which would 65 increase at lower basal areas (Horler & Ahern, 1986). This is confirmed by another study that estimated basal area from a single year of Landsat data in dry forests of eastern Oregon, where the authors found TCB had a strong negative correlation with basal area (Pflugmacher et al., 2012). Similarly, in the current study, areas of higher TCB growth corresponded to areas with lower basal areas (Figure 18). 3.6 Conclusion Monitoring forest structural recovery must identify persistent impacts of forest disturbance and novel ecosystem developments. This chapter presents data and methodology that repeatably identified unique clusters of spectral recovery, which aligned with variable structural recovery. Further, when clusters were applied to the perimeter of the Watlus Lake fire, most of the area showed strong coniferous growth. However, nearly one-tenth of the areas within the fire perimeter were spatially clustered areas of lower conifer recovery and higher deciduous growth. Spectral trends thus identified areas that forest managers may prioritize for post-fire salvage logging, silvicultural prescriptions, or hotspots to prioritize mitigation. Given the ability to identify variability in structural recovery in a repeatable manner, future research should capitalize on the decreasing costs of RPA lidar alongside the extension of ALS data to expand such investigations to the increasing extent of disturbed ecosystems. Expanding RPA lidar data and continued analyses on multi-index spectral trends will better develop the association of spectral trends and variable measures of structural recovery. These spectral trends describe areas of low stem and basal area recovery and high deciduous ingrowth, which may be linked to novel forest structures. For those interested in future 66 forest structure, spatially and structurally distinct pathways of forest regeneration can be highlighted using satellite-derived rates of spectral recovery. 67 Early Spectral Dynamics are Indicative of Distinct Growth Patterns in Post-Wildfire Forests2 4.1 Introduction Pervasive wildfires and subsequent recovery of forests in a warmer climate may change forest structure by altering stand density (Hoecker et al., 2023), shifting dominance of coniferous tree species (Jorgensen et al., 2023), or even triggering ecosystem transitions such as forest to grassland (Hamilton & Burton, 2023). To characterize these possibly novel recovery patterns, forest managers require spatially and temporally continuous assessments of regeneration early in the recovery period (first 5- 10 years), which defines long-term forest structure (Seidl & Turner, 2022). To address widespread calls to manage forests to maintain carbon storage (Clason et al., 2022; Metsaranta et al., 2023), mitigate future fire risk (Hessburg et al., 2021), and ensure future timber supplies (North et al., 2019), new techniques need to identify spatially variable structural recovery within the time window that defines the onset of forest recovery (henceforth, recovery onset, which can be as short as 5 years; Frazier et al., 2018). One approach to capture recovery onset is to substitute space-for-time, using similar sites at different stages of post-fire recovery and projecting how a site at an earlier stage may develop to a site at a later stage (Pickett, 1989). For example, Ye et al. (2021) use Landsat data to show that distinct decreases in spectral 2 The contents of this chapter are adapted from: Smith-Tripp, S., Coops, N. C., Mulverhill, C., White, J. C., & Gergel, S. (2024). Early spectral dynamics are indicative of distinct growth patterns in post-wildfire forests. Remote Sensing in Ecology and Conservation. https://doi.org/10.1002/rse2.420 68 reflectance were linked to the likelihood and severity of future insect disturbance. The same logic suggests that areas with similar spectral responses at the onset of recovery may also have similar pathways of structural recovery. Linking spectral trends to temporal structural recovery pathways relies on field campaigns measuring conifer growth. However, resource demand for field sampling campaigns increases when more samples are needed to capture diverse spectral or structural responses for extensive burns in diverse forest types (Bartels et al., 2016; Holsinger et al., 2022). RPAs can augment structural data at the scale of satellites (Coops et al., 2021). Using spatially continuous estimates of forest structure from RPAS, measures of structural recovery can be linked to distinct spectral trends. However, it remains to be investigated whether distinct spectral trends identified early on (i.e., 5 years after a disturbance) are indicative of later structural recovery patterns. This chapter investigates whether Landsat spectral trends identified five years after a wildfire event are indicative of distinctive structural forest recovery. Overall, this study had two main objectives. First, distinct spectral responses are identified from Landsat metrics derived five years after the fire event (within the recovery onset period). Secondly, spectral trends are grouped based on trends and patterns of structural recovery identified from a space-for-time substitution in forests 5–16 years after fire. I used RPAs to acquire structural measures prioritized within the provincial vegetation resources inventory (VRI) system, including: stem counts, BA, composition, and the proportion of bare ground (British Columbia Data Catalogue, 2022). RPAs' data used a space-for-time approach to describe temporal recovery and then grouped spectral trends based on distinct structural recovery. By grouping spectral trends, I aimed to 69 investigate distinct structural recovery in burned landscapes of the interior of British Columbia. 4.2 Methods To understand recovery pathways, I combined distinct satellite spectral trends with unique patterns and trends of forest structure. Structural measures from RPAS data captured structural recovery multiple years post-fire (5, 8, 12, and 16 years). Reflecting the study objectives, my methodological approach was divided into two parts: (1) unique spectral cluster development and (2) relation of the spectral clusters with distinct groupings of structural recovery based on spatiotemporal structural data from RPAS. The first objective used K-means clustering to identify unique spectral trends within high burn severity areas. For the second objective, I related these clusters of unique spectral trends with distinct forest structural characteristics identified using space-for-time sampling of the lidar data. I also describe the spatial distribution of recovery groupings across the central Interior British Columbia. An overview of the workflow is described in Figure 21. 70 Figure 21. Description of Landsat Data Processing, Spectral Clustering, and Spatio-temporal Analyses with RPAs Lidar Data. 4.2.1 Objective 1: identifying unique spectral clusters This chapter calculated spectral metrics based on a five-year window after the fire disturbance. Spectral metrics included the magnitude of regrowth between the minimum post-disturbance value and the value five years later (“regrowth magnitude”), the median rate of change after the fire (Theil-Sen Slope or “slope”), and the index measure five years after the fire (index + 5 yrs; see Figure 21). I also included the magnitude of 71 the disturbance (“dist mag”) for a total of 22 variables that described trends of the seven selected spectral indices. To calculate spectral metrics, pixels had to have more than three post-disturbance cloud-free images within 5 years after fire. Spectral clusters were generated using the 22 spectral metrics (Figure 21). Clustering used an augmented two-step k-means++ clustering algorithm, where the k-means algorithm uses informed seeds to select the (a) optimal cluster number and (b) approximate cluster centroids (Kapoor & Singhal, 2017). In this algorithm, initial cluster centers were identified using the distortion criterion, which minimizes the distance between observations and the associated cluster centroid (Kodinariya & Makwana, 2013). Using initial centroids, I applied a fuzzy k-means clustering to calculate the probability surface of each centroid (Sanderson & Curtin, 2016). For points where cluster likelihoods were less than 0.7, I used additional clustering to identify the ideal number and approximate location of additional centroids. Centroids from both clusters seeded the final clustering matrix. The final clustering matrix was used to calculate the similarity of centroids, where centroids with shared similarities, based on the distortion criterion, were combined to produce clusters that together describe unique spectral trends. A PCA explored the underlying variability in spectral variables used in clusters. I also investigated the longer-term variability across unique spectral trends compared to pre-fire measures for years 0-20 post-fire. 4.2.2 Objective 2: characterizing distinct structural recovery To understand temporal trends and spatial patterns of structural recovery, I built a space-for-time structural dataset of RPAS lidar data collected in forests 5-16 years after forest fire. In total, I acquired 1380 hectares of lidar data with an average point density 72 of 374 points/m² (Figure 22). I used standard computed standard metrics (Roussel et al., 2020) to use as inputs for GLMs applied to validation data from 26 field plots collected in the same year of lidar acquisition. Final selected forest structural predictions included: bare ground (%), basal area (BA, m²/ha), the ratio of coniferous to deciduous (coniferous:deciduous), and stem counts (stems/Landsat pixel). Bare ground was not used in Chapter 3. For completeness, section 4.3.2 includes results for this additional metric. For more details on data processing and structural model development, see section 3.3.1. 73 Figure 22. (Left) Location biogeoclimate zones (SBS: Sub-Boreal Spruce, SBPS: Sub-Boreal Pine Spruce) in study area. Areas disturbed 1984 – 2022 colored in grey. Blue dots represent lidar flight locations. The inset map shows the location of British Columbia and the study site (black box) in Canada. The yellow star noted the location of lidar structural estimates shown at right. (Right) Examples of the lidar structural estimates including the proportion of bare ground, basal area, coniferous to deciduous ratio, and stem counts. I used RPAS lidar structural estimates to identify distinct structural recovery groupings from spectral clusters. Specifically, I combined spectral clusters where RPAS lidar supported similar structural measures among sample years (5, 8, 11, 12, and 16 years). For example, two spectral clusters would collapse if they had significantly different stem counts but similar basal areas across sample years. To determine structural similarities, I used a permutation multivariate analysis of variance 74 (PERMANOVA) and post-hoc pairwise comparison (Anderson, 2001). A PERMANOVA measures the significance of the distance between groups in n-dimensional space, where n is equal to the number of structural metrics (n = 4, the four modeled structural attributes). Significant (p < 0.05) PERMANOVA results supported structural differences among spectral clusters. The PERMANOVA was built on a Euclidean distance matrix of the four structural metrics. I used the distance matrix to run a PERMANOVA with 999 permutations using the distance matrix of structural measures with year treated as a factor (Anderson, 2001). Post-hoc comparisons, using a Wilks test for nonparametric data, then identified which clusters could be combined based on structural similarities. I tested merged structural groups for differences in estimates of specific structural measures, like BA, with a nested ANOVA. Nested ANOVAs were fit using year as a factor; thus, comparisons were limited groupings that had observations for all years. Final outputs of the PERMANOVA and post-hoc tests identified distinct structural recovery. For the ease of the reader, I gave groupings ecologically relevant names aligning with their pathways of structural recovery. 4.2.3 Software packages Clustering of spectral metrics was performed in R (R Core Team, 2023) using the ClusterR package (Mouselimis, 2023) for clustering, the fpc package (Cohen et al., 2003) to assess cluster similarity, and the factoextra package to calculate PCA loadings (Kassambara & Mundt, 2016). Later, distinct structural types were calculated in R (R Core Team) using the vegan package (Oksanen et al., 2022) with post-hoc tests run using the RVAideMemoire package (Herve, 2023). Post-hoc tests of a nested ANOVA used the rstatix package (Kassambara & Mundt, 2016). 75 4.3 Results 4.3.1 Objective 1: unique spectral clusters 4.3.1.1 Spectral cluster prevalence K-means clustering and the combination of similar clusters resulted in 8 unique spectral clusters that explained 52% of the variance in spectral metrics. Dominant determinants of variance in clustering were spectral metrics of TCA and NBR (loading magnitudes for NBR regrowth = 0.25 and NBR slope = 0.27, Figure 23B). Cluster 5 had the greatest average distance from other cluster centers (0.91), while cluster 3 had the smallest distance to other clusters (0.84; Figure 23B). Cluster 1 was the most frequently observed (37 % of the landscape), followed by cluster 3 (16 % of the landscape). Cluster 2 was the least frequently observed (1 % of the landscape). 76 Figure 23. (A) Principal Component Analysis of spectral metrics used in clustering. Points represent a sample of the total dataset (10,000 random samples weighted by cluster frequency). Ellipses define where 40% of the data for each cluster is included. Black-labeled arrows that the variable loadings of the PCA. Colors denote associated spectral cluster (B) Variable loadings in order of importance. Values are scaled relative to the importance of each principal component. 77 4.3.1.2 Spectral trends of clusters Post-fire clusters were derived from spectral metrics at five years, but clusters showed variability in spectral recovery throughout the early recovery period (+ 20 years, Figure 25). Across all clusters, indices dropped immediately post-fire and slowly recovered, with many clusters recovering to pre-fire values within 20 years. Conversely, TCB increased for most clusters after fire. Of the indices that decreased, TCG was the first to recover to pre-disturbance values. Overall rates of recovery differed among clusters and indices. Of the clusters, cluster 3 had the highest average recovery for the first five years after fire for all indices except NDMI and TCA. Cluster 5 had the lowest slopes for all indices except NDMI and TCA (Figure 24). For cluster 6, the onset of recovery generally occurred later compared to the other clusters. This pattern is most apparent in NBR, NDMI, and TCA (Figure 25). 78 Figure 24. Average values of each spectral metric input in Kmeans ++ clustering for the eight identified spectral trends. All spectral metrics are multiplied by 100. 79 Figure 25. Mean trends for each spectral cluster for all indices for year -3 to +20 after fire. Mean values are relative to a 3-year pre-disturbance baseline period for all clusters. The label above the panel notes the respected index. The black dotted line at year = 0 is year of the fire. 80 4.3.2 Objective 2: characterizing distinct structural recovery In addition to stem count, basal area, and the ratio of conifer to deciduous stems RQ2 also estimated the proportion of bare ground using the same generalized linear modeling described in 3.3.1 with a zero-inflated negative binomial model. The overall fit of the final bare ground model was moderate (R2 = 0.64), which was similar to the stem-count model described in section 3.4.1. Figure 26 compares field observations of bare ground to modeled estimates. Table 10. Metrics of linear modeling from field plots and RPAS lidar metrics for additional bare ground model used in Chapter 4. Model Family Df Parameters est stdev R sq CRR 10.04 7.22 % Bare Ground Zero-Inflated Negative Binomial 21 zentropy -15.13 5.97 0.64 Lcoefvar 4.35 1.35 phi 11.73 2.52 Figure 26. Model graphs comparing field measurements (measured) to model predictions (predicted) for proportion of bare ground built from RPAS lidar. The black line shows the 1:1 line. 81 4.3.2.1 Structural data for each spectral cluster Figure 27. Number of pixels for each spectral cluster with associated RPA lidar data. The total number of pixels for each cluster is noted by column. Colors denote the proportion of pixels by years since fire for each cluster. RPAS lidar data covered 722 ha of the area incorporated in spectral clustering (0.12% of the area included in spectral clustering, Figure 27). The greatest overlap with space-for-time lidar samples was areas five years post-fire (40.1%), coinciding with burns from 2017 (Figure 27). Areas 8- and 16-years post-fire were the second and third most commonly captured (23.2 % and 21.3 % respectively). Of spectral clusters, cluster 1 had the largest area with RPAS structural data (304 ha). Cluster 2 had a very low proportion of area covered (~1 ha or 25 pixels). Results focus on clusters where the number of structural samples within and across years was consistent (i.e., clusters 1, 3,5,7, and 8). In clusters where the number of structural samples was low within and across years, I treat differences in structural estimates with caution. 82 4.3.2.2 Spectral clusters demonstrate forest succession with distinct trends Spatial patterns and temporal trends of structural development were variable among the different structural metrics investigated. Results of a pairwise PERMANOVA supported differences in structural metrics over time among clusters (F(7, 7882) = 173.91, p < 0.001, Table 11). Using a post-hoc Wilks' test, spectral clusters 3, 4, 5, and 8 had similar structural estimates both within and across years (p-values > 0.05, Figure 28). Therefore, these clusters were grouped under one structural label. For simplicity, the ecologically relevant names assigned to structural groupings were as follows: (1) Emergent conifer: representing cluster 1. This group showed a strong deciduous response (71% deciduous in year 5) in early years that was replaced by relatively large coniferous stems (BA 0.47 m²/ha in year 12 and BA 0.82 m²/ha in year 16) (2) Mixed forest regrowth: representing clusters 3,4,5, and 8. In this group, a high number of relatively small coniferous stems (553 stems/ha in year 12 and 866 stems/ha in year 16) were accompanied by a mix of woody deciduous shrubs (10% deciduous cover in year 16). (3) Residual canopy to late growth: representing cluster 2. This class had a live canopy after the fire that senesced within the first 8 years (BA 2.14 in year 5 and BA 0.27 in year 8) (4) Stem exclusion to stem loss: representing cluster 6. In this group, a high number of coniferous stems in years 8 and 12 was not observed in year 16 (820 stems/ha in year 12 and 384 stems/ha in year 16). 83 (5) Dense mixed cover: representing cluster 7. Generally, this group had ground cover of both deciduous and coniferous (coniferous:deciduous ratio 0.89 in year 12) while the proportion of coniferous stems was temporally variable. Similar to the pattern above, this pattern also had a large number of stems in year 12. Table 11. Results of MANOVA applied to unique spectral clusters to identify structural patterns of recovery. Permutations = 999. Df Sum Sq F P-value Spectral Cluster 7 2235.07 173.98 << 0.01 Residual 7,882 14465.45 Total 7,889 16700.51 Figure 28. Post-hoc significance values for analysis of dissimilarities for each cross-comparison of clusters. Colors rank similarity of spectral clusters. In the structural grouping process, clusters were collapsed when post-hoc p-values > 0.1. For the five named groups, I compare their temporal patterns of structural recovery for every sample year (Figure 29). Of the structural patterns, emergent conifer and mixed forest regrowth were the only patterns that did not have years with missing data. Emergent conifers had the highest BA 16 years after fire (0.82 ± 0.002 m² / ha, Table 12). Comparatively, the residual canopy of the late growth class had the lowest BA (0.42 ± 0.15 m²/ha). Stem counts showed an inverse pattern between these two groups, 84 16 years after fire, low-density areas had the lowest stem count estimates (520 ± 2 stems/900 m²), and the residual canopy group had the highest stem count estimates (841 ±128 stems/900 m²; Figure 29). However, the number of observations for the residual canopy group in year 16 was low (n = 4). Of the spectral groupings with more data in year 16, the stem exclusion to stem loss had the highest stem count estimates (652 ±1 stems/900 m², n = 384). Yet, the high density of stems in this group was a decrease from observations at 12 years after fire, where, for the same group, stem count estimates were 820 (±10) stems/900 m². Average structural estimate for each structural group for each year after wildfire (± standard error of the mean). The number of observations is the number of 30x30 m pixels with RPAS lidar data. 85 Table 12. Average structural estimate for each structural group for each year after wildfire (± standard error of the mean). The number of observations is the number of 30x30 m pixels with RPAs lidar data. Note stem counts exclude areas where ABA stem count estimates were above field observed values (> 8000 stems / 900 m2). Group Name Years Post fire #Obs Estimate ± Emergent Conifer 5 Coniferous:Deciduous 2313 0.39 0.00 Stem Counts (stems/900 m2) 2308 130 0 Basal Area (m2/hectare) 2313 0.25 0.00 Bare ground (%) 2313 72 0.00 8 Coniferous:Deciduous 479 0.73 0.00 Stem Counts (stems/900 m2) 473 214 0 Basal Area (m2/hectare) 479 0.10 0.00 Bare ground (%) 479 55 0.00 11 Coniferous:Deciduous 2 1.00 0.00 Stem Counts (stems/900 m2) 2 278 109 Basal Area (m2/hectare) 2 0.22 0.11 Bare ground (%) 2 28 0.14 12 Coniferous:Deciduous 292 0.78 0.00 Stem Counts (stems/900 m2) 238 474 2 Basal Area (m2/hectare) 292 0.47 0.00 Bare ground (%) 292 50 0.00 16 Coniferous:Deciduous 327 0.95 0.00 Stem Counts (stems/900 m2) 270 519 1 Basal Area (m2/hectare) 327 0.82 0.00 Bare ground (%) 327 38 0.00 Residual canopy to late stem-growth 5 Coniferous:Deciduous 15 0.87 0.01 Stem Counts (stems/900 m2) 15 201 5 Basal Area (m2/hectare) 15 2.14 0.05 Bare ground (%) 15 75 0.01 8 Coniferous:Deciduous 6 0.98 0.01 Stem Counts (stems/900 m2) 6 158 12 Basal Area (m2/hectare) 6 0.27 0.11 Bare ground (%) 6 15 0.02 16 Coniferous:Deciduous 5 0.99 0.00 Stem Counts (stems/900 m2) 4 841 128 Basal Area (m2/hectare) 5 0.42 0.14 Bare ground (%) 5 32 0.02 Stem exclusion to stem loss 8 Coniferous:Deciduous 398 0.94 0.00 Stem Counts (stems/900 m2) 392 289 1 Basal Area (m2/hectare) 398 0.35 0.00 Bare ground (%) 398 46 0.00 12 Coniferous:Deciduous 54 0.96 0.00 Stem Counts (stems/900 m2) 45 820 9 Basal Area (m2/hectare) 54 1.02 0.01 Bare ground (%) 54 38 0.00 16 Coniferous:Deciduous 440 0.93 0.00 Stem Counts (stems/900 m2) 384 652 1 Basal Area (m2/hectare) 440 0.70 0.00 Bare ground (%) 440 40 0.00 5 Coniferous:Deciduous 61 0.33 0.01 Stem Counts (stems/900 m2) 60 110 1 Basal Area (m2/hectare) 61 0.07 0.01 Bare ground (%) 61 57 0.00 Table 12. (Cont) Average structural estimate for each structural group for each year after wildfire (± standard error of the mean). The number of observations is the number of 30x30 m pixels with RPAs lidar data. Note stem counts exclude areas where ABA stem count estimates were above field observed values (> 8000 stems / 900 m2). 86 Group Name Years Post fire #Obs Estimate ± 8 Coniferous:Deciduous 3 1.00 0.00 Stem Counts (stems/900 m2) 3 281 39 Basal Area (m2/hectare) 3 0.79 0.31 Dense Mixed Cover Bare ground (%) 3 16 0.01 12 Coniferous:Deciduous 390 0.89 0.00 Stem Counts (stems/900 m2) 321 879 2 Basal Area (m2/hectare) 390 0.90 0.00 Bare ground (%) 390 43 0.00 16 Coniferous:Deciduous 1 1.00 NA Stem Counts (stems/900 m2) 1 80 NA Basal Area (m2/hectare) 1 0.00 NA Bare ground (%) 1 10 NA 5 Coniferous:Deciduous 784 0.45 0.00 Stem Counts (stems/900 m2) 784 127 0 Basal Area (m2/hectare) 784 0.36 0.00 Mixed Forest Bare ground (%) 784 66 0.00 8 Coniferous:Deciduous 948 0.81 0.00 Stem Counts (stems/900 m2) 936 259 0 Basal Area (m2/hectare) 948 0.03 0.00 Bare ground (%) 948 38 0.00 11 Coniferous:Deciduous 93 0.99 0.00 Stem Counts (stems/900 m2) 90 428 4 Basal Area (m2/hectare) 93 0.24 0.01 Bare ground (%) 93 39 0.00 12 Coniferous:Deciduous 385 0.83 0.00 Stem Counts (stems/900 m2) 300 553 2 Basal Area (m2/hectare) 385 0.39 0.00 Bare ground (%) 385 42 0.00 16 Coniferous:Deciduous 1029 0.90 0.00 Stem Counts (stems/900 m2) 866 633 1 Basal Area (m2/hectare) 1029 0.75 0.00 Bare ground (%) 1029 42 0.00 87 Figure 29. The difference between the mean for each spectral pattern and the mean for each year since fire (5, 8, 11, 12, and 16 years). The horizontal black line is the mean for that year. For bare ground percentage, values above the line have more bare ground. For coniferous: deciduous, points above the line have a higher proportion of coniferous cover. For both BA and stem counts, values above the black line are greater than the mean for the year. Groups with fewer than three observations are omitted from the graph. 88 4.3.2.3 Spatial distributions of distinct structural patterns Over 80% of the study area was covered by mixed forest (45.5 %) and emergent conifer (36.9 %). The skewed distribution of structural patterns is highlighted in Figure 30, which shows the dominant structural group averaged over 10,000 ha hexagons. The least common pattern was residual canopy to late growth (1.18 %). In most areas, all distinct structural patterns were observed (5 distinct observations per hexagon). Emergent conifers or mixed forest regrowth groups were generally the dominant structural pattern, but dense mixed cover was more common in the southern part of the landscape. 89 Figure 30. (Top) The most common structural groups are represented in 10,000 ha hexagons. (Bottom-left) number of distinct structural patterns in each hexagon. (Bottom-right) proportion of the hexagon covered by high-severity wildfire. The background for all maps is a hillshade. 90 I highlighted the differences in structural development for the two most common patterns, emergent conifer and mixed forest, which both had consistent structural measures within and across years (Figure 31). I directly compared the structural estimates of emergent conifer and mixed forests, as well as how these structural estimates varied compared to the overall distribution of the structural data for a given year. Areas identified as emergent conifer had significantly different estimates of forest structure compared to areas identified as mixed forest regrowth (Table 13). For emergent conifers, early years (5-12 years) were generally more deciduous and had a lower proportion of bare ground compared to mixed forest areas. From years 5 through 16, emergent conifers trended less deciduous than other regions (Figure 31). By year 16, mixed regrowth had a higher proportion of deciduous and less bare ground than the emergent conifer class. In year 16, the mixed regrowth class had the highest ratio of deciduous cover (~1 m² deciduous for every 10 m² of coniferous cover), while the emergent conifer had half as many deciduous (Figure 31). For most years, emergent conifers also had higher BA estimates compared to mixed forest regions (Figure 31). 91 Figure 31. (A) Estimates for each structural measure for years after the fire. All data (gray) are estimates across all groups. Emergent conifer (red) and mixed forest growth (orange) are subsets for visualization. Asterisks indicate when ANOVA and post-hoc Dunn test identify significant (< 0.05) differences among the emergent conifer and mixed forest groups. (B) Exemplar lidar cross-sections for emergent conifer and mixed forest regrowth groups. 92 Table 13. Post-hoc Dunn tests for nested ANOVA comparing emergent conifer and mixed forest structural groupings. These groupings are highlighted as they have data across all years. P-values are adjusted via the Holm method. Years where structural estimates group are different are starred. Metric Year Emergent Conifer (n) Mixed Forest (n) statistic Adjusted P Significance Basal Area 5 2,313 784 1.62 0.10 ns m2/ha 8 479 946 -4.83 0.00 **** 11 2 93 -1.77 0.08 ns 12 277 367 -2.14 0.03 * 16 276 921 -3.31 0.00 * 5 2,313 784 -0.30 0.76 ns Stem Counts 8 479 946 0.48 0.63 ns Stems / 900 m2 11 2 93 0.57 0.57 ns 12 277 367 1.16 0.25 ns 16 276 921 3.02 0.00 ** Coniferous: Deciduous 5 2,313 784 3.21 0.00 ** (%) 8 479 946 6.27 0.00 **** 11 2 93 -1.63 0.10 ns 12 277 367 3.66 0.00 *** 16 276 921 3.02 0.00 ** Bare Ground 5 2,313 784 6.86 0.00 **** (%) 8 479 946 13.22 0.00 **** 11 2 93 -0.83 0.41 ns 12 277 367 5.71 0.00 **** 16 276 921 -4.84 0.00 **** 93 Figure 32. Research methods and outputs for research question 2. Top panel describes methods to develop spectral responses (left) and a distribution of unique spectral responses across the study area (right). Bottom panel is example lidar cross sections highlighting structural growth over time for the two most common structural pathways – emergent conifer and mixed forest growth. 4.4 Discussion Larger and more severe wildfires across much of western North America underscore the need for efficient monitoring capable of capturing the development of 94 possibly novel forest structures (Figure 32). This research chapter builds on prior Landsat studies investigating early spectral responses (Pickell et al., 2016), linking these unique responses to distinct groupings of early forest structure and composition, distinctions that will likely persist in future forest composition (Johnstone, et al., 2010). Using Landsat time series, I show that it is possible to identify unique spectral responses and group them by distinctive longer-term structural recovery. Spectral clusters based on temporal metrics of multiple spectral indices characterized unique spectral trends early on after wildfire. I relate these spectral trends to forest structure measures, collected via RPAS lidar, from sites at different stages of structural recovery post-fire. Overall, sites at different post-fire stages were typical for early structural forest development, including stand initiation and stem exclusion (Bartels et al., 2016; Oliver & Larson, 1996). However, structural recovery varied across spectral clusters. This research chapter study highlights two structural recovery groupings, which dominated the majority of the post-fire landscape. These groups are: (1) mixed forest, where the bare ground was gradually infilled with a mix of deciduous and coniferous stems, and (2) emergent conifer, associated with a rapid deciduous response followed by ingrowth of coniferous stems. A minority (20%) of the landscape was covered with other structural pathways of growth. These alternative pathways included areas with remaining live canopy (residual canopy to late stem growth), strong stem exclusion (stem exclusion to stem loss), or covered by a mixed herbaceous and coniferous vegetation (dense mixed cover). For less common pathways, capturing temporal structural growth with space-for-time sampling was difficult. 95 4.4.1 Multiple spectral indices help differentiate unique spectral dynamics Multiple indices in spectral clustering were necessary to delineate unique spectral responses. NBR and TCA captured the most variability of spectral responses. I expected NBR and TCA to be influential as these indices capture soil reflectance and vegetation growth in young and dynamic forest types (Cohen et al., 2020; Gómez et al., 2014). Similar to prior research, I found that TCB was the least informative index for clustering (Smith-Tripp et al., 2024a). The importance of TCB is likely minimal as TCB saturates rapidly after a disturbance due to the ingrowth of deciduous vegetation (Bolton et al., 2018). In this investigation, NDMI was not informative for cluster development. In general, the use of NDMI for monitoring forests is variable in the literature. Some suggest NDMI is a preferred index (Fornacca et al., 2018; Ochtyra et al., 2020), while others have shown it to be a poor indicator of structure (Cohen et al., 2020; Storey et al., 2016). The value of multiple spectral indices for structural assessment was further highlighted when clusters were associated with distinct structural groups. Despite the unique spectral responses of clusters 3, 4, 5, and 8, these clusters merged into a single distinct grouping of mixed forest recovery. Among these spectral clusters, cluster 3 had the highest NBR recovery rate and cluster 5 had the lowest, but other spectral responses were similar, such as the relatively low values of TCW growth. TCW reflectance has a greater sensitivity to stand structure, such as BA; therefore, low values in years 8-16 for this class could be indicative of lower BA growth compared to the emergent conifers group (Viana-Soto et al., 2020). 96 4.4.2 Structural recovery varies across time and space Different environmental attributes, such as soil moisture, may explain the variable distribution of distinct structural recovery groupings across the study region (White et al., 2019). In the drier southwest portion of the study area, closer to the Chilcotin Mountain range, mixed forest regrowth and dense mixed cover were more common than emergent conifers. Precipitation is widely acknowledged to impact coniferous stem growth (Bright et al., 2019; White et al., 2023; Young et al., 2019); thus, the increased frequency of structural responses with distinctly low conifer values in the southern regions may be associated with drier conditions. Further, spectral indices themselves probably captured some environmental differences in southern regions. For example, the stem growth to stem loss grouping was also common in drier southern areas and had particularly low TCW values (a measure of landscape wetness; Pontone et al., 2024). Differences in precipitation, site aridity, and productivity may also explain why heterogeneity of structural recovery increased when a high proportion of the landscape burned in high-severity fire. In areas where a high proportion is burn-impacted, structural responses may be more impacted by differences in site-level factors (e.g., site aridity, Hamilton & Burton, 2023; Talucci et al., 2019). Investigating environmental controls on structural recovery is outside the scope of the current investigation, but it is possible using my research methodology. 4.4.3 Early spectral dynamics linked with incipient recovery as a tool for land management Early spectral recovery was indicative of longer-term differences in forest structural development. For example, emergent conifer areas had the highest TCG regrowth of 97 the first five years, which was likely influenced by their rapid deciduous ingrowth. The importance of TCG in capturing this deciduous growth has been shown in previous post-disturbance studies (Pickell et al., 2016). Early spectral reflectance also captured the senescence of live canopy in the residual canopy group between years 5 and 8. The residual canopy group had the lowest TCB regrowth values, indicating a steady increase in duff and litter as the live canopy fell to the ground (Banskota et al., 2014). By combining Landsat time series with RPAS lidar data, I found that distinct early spectral responses were associated with different distinct structural recovery, including patterns and trends of deciduous growth and stand density. Structural recovery varied spatially, suggesting that environmental conditions may influence structural development across the study area. Findings of RQ2 support a low risk of failed conifer establishment in the SBS and SPBS (Clason et al., 2022), but many regions also have a substantial proportion of deciduous stems. Although increasing deciduous cover may be undesirable for timber-based management objectives, it may be advantageous for increased resilience to warming climates (Morin et al., 2018) and decreased fire risk (Prichard et al., 2021). In areas with numerous residual live trees, canopy senescence can contribute to deadwood fuel buildup, which increases future fire risk (Prichard et al., 2021), but the remaining canopy can increase the diversity of regenerating tree species (Clason et al., 2022; Meddens et al., 2018) and provide numerous other non-timber values. Balancing the risk associated with post-fire fuel build-up with the benefits of increased diversity requires nuanced knowledge of site dynamics; while my approach does not directly capture these dynamics, the methodology can identify priority areas for further investigation and more intensive management (Pettorelli et al., 2018). 98 4.5 Conclusion This research chapter leveraged the legacy of Landsat alongside the expanding availability of RPAS lidar data to identify patterns and trends of structural recovery at a landscape scale. British Columbia’s increasingly disturbed area requires monitoring that projects expected shifts in forest structure early on after fire events (Crausbay et al., 2022). By quantifying the relationships between early post-disturbance spectral responses and forest structural attributes at a critical recovery juncture (five years), the methodology presented herein allows forest managers to make strategic decisions that capitalize on shifts in climate and species ranges (Hessburg et al., 2021; Pettorelli et al., 2018) to manage for forests of the future (Larson et al., 2022). 99 Post-Fire Structural Forest Recovery Associated with Anomalous Climate Conditions in Dry Sub-Boreal Forests3 5.1 Introduction The spatial and temporal variability in post-fire conifer growth (henceforth – recovery) is controlled by ecological drivers. Resolving the association between coniferous recovery and these ecological drivers can be difficult because responses vary across spatial and temporal scales (Lewis et al., 2017; Wimberly & Spies, 2002). Yet, identifying landscape-level drivers of recovery is key to adapting to landscape-level fire (Crausbay et al., 2022). Drivers can be split into categories including: fire impacts (Taylor et al., 2021), site-level topography (Peeler & Smithwick, 2021), pre-fire conditions (White et al., 2023), climatic regime (Chu & Guo, 2014), and post-fire climate conditions (Davis et al., 2023). Fire impacts, such as remaining post-fire canopy, alters seed availability, germination, and post-fire microclimate (Peeler & Smithwick, 2021; Smith-Tripp et al., 2022; Turner et al., 1999). Post-fire seed availability, from the soil or remaining canopy, is essential for conifer establishment (Stewart et al., 2021; Turner et al., 1999). The canopy that remains after can protect conifer germinants from climate extremes (Davis et al., 2023). Some populations of lodgepole pine (Pinus contorta) are also serotinous, which means 3 The contents of this chapter have been submitted and are under review as follow: Smith-Tripp, S., Coops, N. C., Mulverhill, C., White, J. C., & Gergel, S., Post-Fire Structural Forest Recovery Associated with Anomalous Climate Conditions in Dry Sub-Boreal Forests. Under review at Landscape Ecology 100 that seeds from cones require fire to open and germinate (Lotan, 1976). Yet the importance of fire impacts on recovery varies across regions. Serotiny in lodgepole pine is more common at high elevations and in the north (Lotan, 1976). Environmental and site-level conditions, such as elevation or slope, alter conifer recovery independent of species (Stevens-Rumann & Morgan, 2019). Across the Greater Yellowstone Ecosystem, Kiel and Turner (2022) found higher elevations and steeper slopes caused sparse conifer growth (< 1,000 stems / ha) more than 30 years post-fire. Comparatively, in the Blue Mountains of Oregon, Downing et al. (2019) found a positive relationship with elevation and conifer stem density. Pre-fire conditions, including productivity and composition, also drive conifer recovery. High pre-fire conifer density and/or basal area can increase the rate and density of conifer recovery (Harvey et al., 2016; White et al., 2023). Conversely, a higher deciduous proportion pre-fire can result in rapid deciduous growth post-fire. (Haire & McGarigal, 2008). This rapid post-fire deciduous growth can be facilitative or competitive. Downing et al. (2019) found deciduous growth negatively impacted recovery of most conifers, but protected young ponderosa pines (Pinus ponderosa) from climate extremes. The importance of extreme post-fire climate, such as drought or extreme heat, on conifer recovery is heavily emphasized by recent literature (Stevens-Rumann et al., 2022; Vanderhoof et al., 2021). For example, Talucci et al. (2019) studied lodgepole pine recruitment following wildfire and bark beetle infestation in interior BC. They found that post-fire climate moisture deficit (CMD) was negatively correlated with seedling density, but sample size limited study conclusions. Talucci et al. (2019) is one of few 101 post-fire studies in lodgepole pine dominant sub-boreal forests of Interior BC, demonstrating the limited number of investigations and research data for this region compared to research in other western NA conifer forests (Davis et al., 2023; White et al., 2023). To understand recovery drivers, landscape-level approaches are helpful as they capture a gradient of ecological drivers. Exemplars such as the meta-analysis of Davis et al. (2023) help clarify the impact of different drivers on ecosystem recovery. However, building such landscape-scale field datasets is costly, labour-intensive, and challenging to update. Further, the sub-boreal of interior BC, despite encompassing over 12 million hectares, has had relatively few post-fire studies and lacks the extensive field sampling used in other meta-analyses (e.g., Davis et al., 2023). The lack of post-fire monitoring and research in the extensively burned sub-boreal (Parisien et al., 2023) could preclude us from capturing possibly novel landscape transitions, such as conifer forests transitioning to conifer grasslands (Hamilton & Burton, 2023). This chapter capitalizes on the link between spectral responses and structural recovery (described in 4.4.3) to investigate the relative impact of recovery drivers at a landscape scale. I ask which different ecological drivers are associated with different patterns and trends of structural recovery. Research objectives were three-fold: (1) align forest structural recovery with spectral responses of recovery, (2) determine the relative effect that different categories of drivers (e.g. post-fire climate or pre-fire conditions) have on structural recovery; and (3) across all recovery drivers, understand which drivers were the most important across and within structural groupings. The research 102 approach highlights recovery drivers with the greatest impact on post-fire structural patterns and trends in the western sub-boreal forests of British Columbia 5.2 Methods This investigation fused spectral Landsat satellite data with a chronosequence of RPA lidar-derived forest structural data (5-21 years post-fire) to describe structural recovery following high severity fire. Building on the link between unique spectral recovery responses and structural recovery of chapters 2 and 3, I created post-fire structural recovery groups using a combination of early spectral responses from Landsat and a space-for-time dataset of forest structure from RPA. I then investigated how these observed structural groupings were predicted by ecological drivers (climate, post-fire climate anomalies, fire-impacts, and pre-fire landscape characteristics). An overview of the methodology is included in Figure 33. 103 Figure 33. Overall research framework including development of structural groupings. Boxes beneath ‘drivers of recovery’ are the categories of drivers used in individual random forests as well as the global random forest. The overall impact of drivers of recovery on structural recovery (research objective) is accentuated in dark blue. 5.2.1 Structural recovery groupings Spectral clusters were merged into distinct structural groupings using a chronosequence of forest structural measures captured with RPAS lidar. Clusters representing less than 1% of observations were excluded from further processing. The 104 forest structure sampling covered 1,300 ha (an additional 500 ha compared to RQ2) of forests 5-21 post-fire. Area-based estimates for sampled areas used the linear relationships between ground plots and Lidar point cloud-derived metrics, including basic, dispersion, L-moments, and percentile metrics calculated with the LidRmetrics package (Roussel et al., 2021). Composition and bare ground models added a random effect of disturbance year or acquisition, as these models were sensitive to the season of data collection and differences in the lidar acquisition parameters—final model accuracies based on k-fold evaluations of 100 models. Models relied on distributions and the formulas available in the mgcv and glmmTMB packages (Brooks et al., 2022; Wood, 2021). All models were assessed for normality and heteroskedasticity. Model values were transformed and weighted when necessary. When models were applied at the landscape scale, I excluded areas where model estimates were greater than in-field observations. Results of lidar models, as updated from RQ2 to RQ3, are described in the results. These updated models used RPAS lidar structural estimates to merge unique spectral trends into distinct recovery groups. Structural grouping followed the methods outlined in 4.2.2. To improve temporal continuity, I grouped samples into post-fire “epochs” of one-to-two-year periods: 5-7, 8-9, 11-12, 15-16, and 21 years post-fire. 5.2.2 Drivers of forest recovery To investigate how recovery drivers impact structural recovery, I considered both individual drivers, such as elevation, and drivers by category, such as environment. Driver categories are as follows: (a) environment, (b) pre-fire and fire-impacts, (c) climate, and (d) post-fire climate anomalies. Driver selection was based on research findings from studies in similar ecosystem types (Table 14). 105 Environmental conditions Elevation data was derived from a DEM with a 30 m spatial resolution from the Aster Global Digital Elevation Model (GDEM product, Tachikawa et al., 2011). The DEM was also used to calculate additional indices, including topographic position index (TPI) in a 3x3 window, flow direction, and transformed aspect (TRASP). Flow direction is a unitless index that describes the drainage of an area, indicative of site-level moisture dynamics (Metcalfe et al., 2015). TRASP transforms aspect to range from 0-1, where 0 is on northern aspects and one is on hotter southern aspects (Roberts & Cooper, 1989). Soil types were obtained from a provincial digital soil map, classified using a random forest algorithm with validation plots and remotely-sensed climate and vegetation data (Heung et al., 2022). Pre-fire conditions and fire impacts Pre-fire site conditions used provincial data layers and satellite-based models. Site type was the BEC sub-zone. Land cover type, species composition, and BA were derived from NTEMS data (Hermosilla, Bastyr, et al., 2022; Hermosilla, Wulder, et al., 2022; Matasci et al., 2018). Land cover and species composition was based on the most common class five years before the fire. Pre-fire BA was based on the mean BA (m²/ha) of the fire years before the fire. Fire impacts were calculated using the NTEMS disturbance attribution data (Hermosilla et al., 2016b). I acknowledge that fire severity is an important driver to consider. However, fire severity was excluded from models because initial clustering included fire severity. Instead, I grouped burned pixels by study year to calculate fire distance to live-edge (m) and patch size (ha) for fire impact drivers. 106 Climate and Post-fire Climate Anomalies Climate data were generated from ClimateNA software, which uses elevation models to downscale data from >10,000 meteorological stations across North America via bilinear interpolation and dynamic local downscaling (Daly et al., 2008; Wang et al., 2016). First, 1981-2010 annual and seasonal climate normals of ClimateNA were downscaled using the 30 m DEM. The climatic variables chosen were based on those used in prior research (Bright et al., 2019; Hankin et al., 2019; Hoecker et al., 2020; Littlefield, 2019; Meng et al., 2015; Petrie et al., 2016). The 30 year period (1981-2010) is commonly used as the reference period for climate conditions across different sites (Wang et al., 2016). Climate normals, or site averages, were used to calculate standardized z-scores of climate extremes. Specifically, the maximum and/or minimum climate measures for five years post-fire were standardized using the mean and standard deviation of the climate normals. This five year period was selected based on past work that supported anomalous climate conditions of the first five years post-fire significantly impacted recovery (Davis et al., 2023). Table 14 lists the climate variables and selected climate anomalies (anomalies are italicized). 107 Table 14. Data sources or calculations citations for drivers of forest recovery. Abbreviation and units for each variable. References note data source. For each driver, a reference for both the data source, and prior reference support is noted to the right. Note: climate anomalies used in models are italicized. Type Explanatory Variable Abbreviation /Unit Data Source Data Reference Driver Reference Site/Environmental Conditions Elevation Elev (m) Global Digital Elevation Model Tachikawaa et al., 2011 Bright et al., 2019 Topographic Position Index TPI Elevation Data Littlefield et al., 2019 Soil Type Soil Terrestrial Ecosystem Mapping Heung et al., 2022 Baltzer et al., 2021 Flow Direction Flow Direction Elevation Data Kopecký et al., 2021 Harvey et al., 2016 Aspect Index TRASP Elevation Data Roberts and Cooper, 1989 Littlefield et al., 2019 Ecosystem Subzone Subzone Ecosystem Classification Pojar et al., 1987 BC Forest Practices Board, 2020 Pre-Fire Conditions Pre-fire BA Pre-fire BA (m²/ha) NTEMS Matasci et al., 2018 White et al., 2023 Pre-fire landcover class Pre-fire Landcover NTEMS Hermosilla et al., 2022 Meng et al., 2015 Pre-fire Species Type Pre-fire Species NTEMS Hermosilla et al., 2024 Fire Impacts Patch Size Patch (ha) Derived from NTEMS burned area (Hermosilla et al. 2016) Kemp et al., 2016 Distance to Live-Edge Edge (m) Littlefield et al., 2016 Climate (& Post-fire Climate Anomalies) Mean Annual Temperature MAT (°C) Warmest Year Bright et al., 2019 Mean Annual Precipitation MAP (mm) Driest Year Bright et al., 2019 Precipitation As Snow (mm August – July) PAS (mm) Min Snow Talucci et al., 2019 Hargreaves Climatic Moisture Deficit CMD (mm) Max CMD Davis et al., 2023 Summer Average Temperature Summer Avg. T Avg Coldest Summer T ClimateNA Wang et al., 2016 Guz et al., 2021 Maximum Summer Temperature Summer Max T Max Warmest Summer T Guz et al., 2021 Minimum Summer Temperature Summer Min T Min Coldest Summer T Hansen and Turner, 2018 Minimum Winter Temperature Winter Min T Min Coldest Winter T Meng et al., 2015 Average Spring Precipitation Spring Precip Min Spring Precip Hankin et al., 2019 Average Summer Precipitation Summer Precip Min Summer Precip Guz et al., 2021 Average Autumn Precipitation Fall Precip Min Fall Precip Harvey et al., 2022 108 5.2.3 Modeling recovery drivers’ impact on structural recovery groups I investigated the association between recovery drivers and recovery groupings using random forests (RF) modeling, where the response variable was the structural recovery group. For model building, I selected a stratified random sample of 1% of burned pixels (1985-2017; n =48,766) stratified by the four structural groups. Sampled pixels were split into training (70%) and testing (30%) sets. Sampled pixels were a minimum of 90 m apart to limit spatial autocorrelation. RF modeling used a tiered approach. First, RFs were built for each driver category (e.g., environmental conditions or climate). The overall accuracy estimates of the initial four RFs indicated which driver category best predicted structural groups. The variable importance of initial RFs also identified the top five individual drivers for each category. The final RF combined top drivers. When categories had less than five variables (e.g., fire impacts), I included drivers whose permutational variable importance estimate was greater than 0.05. This tiered RF approach eliminated redundant variables. A total of 34 recovery drivers were used in four initial RF models (Table 15), which consolidated to 19 recovery drivers in the final RF. For initial RFs, the number of variables tested in each split (mtry) was 2, and the number of decision trees (ntree) was 500. The final RF models tested five variables at each split (mtry), the number of decision trees (ntree) was 500, and the minimum terminal node size (nodesize) was five. As RFs modeled classes, the split rule used extra trees, and the performance measure used the misclassification rate. Accuracy of the RF model was evaluated using both out-of-bag (OOB) error with a training dataset and with validation data external to the RF model (sample size = 14,440). To understand the importance of recovery drivers 109 within and across structural groups, conditional variable importance was calculated for the overall model and by structural group. The relative importance of recovery drivers was determined using the Boruta algorithm. The Boruta algorithm measures the mean importance of all drivers included in the model compared to random noise in the data when fit multiple times (Kursa & Rudnicki, 2010). I calculated partial dependence by structural group to understand the likelihood of observing structural groups for different recovery driver values. All models were built in an R environment (R Core Team, 2023). RF modeling used the ranger package (Wright and Ziegler, 2017). The Boruta algorithm from the Boruta package calculated conditional variable importance (Kursa & Rudnicki, 2010). Partial dependence was calculated using the pdp package (Greenwell, 2017). 5.3 Results 5.3.1 Updated wall-to-wall structural metrics after 2024 RPA flights This research chapter expanded lidar data acquisition with six additional flights and field plots. For completeness, updated model coefficients are noted in Table 15. 110 Table 15. Model coefficients and model type for the four modeled structure variables. Model type includes the model family. Df = degrees of freedom. Measure of fit (R2 or AUC) is included for each model. Model Model Type Df Beta Estimate Standard Error of Estimate R2 Basal Area Binomial (log-link) (Intercept) -0.22 0.76 AUC = 0.79 29 L2 35.65 17.30 L3 -49.52 24.23 (Intercept) 8.76 1.67 Gamma log-link Pzabovemean -0.21 0.04 L3 2.44 0.78 0.92 17 L4 -3.27 1.00 Lkurt -11.61 2.02 (Intercept) 5.95 2.23 Coniferous: Deciduous (Intercept) -11.88 7.14 Beta AR log-link Pzabovemean 0.11 0.09 CRR 21.32 9.24 Lkurt 10.65 4.18 0.46 Lcoefvar 3.23 5.35 betadjustment 0.54 0.64 theta 0.68 0.86 Disturbance Year 1.50 *Random Effect (Intercept) 5.94 2.10 % Bare Ground Beta AR log-link Zcv 0.00 0.00 Zkurt 0.00 0.00 Pzabovemean -0.08 0.03 11 Pzabove2 0.05 0.02 CRR 3.84 2.12 Lkurt -3.81 1.71 0.59 Vertical Complexity Index -6.00 1.10 Lcoefvar -1.92 1.77 Sample Plot* *Random Effect (Intercept) 2.45 1.57 Stem counts Zero inflated negative binomial Lkurt -3.30 3.09 28 (intercept) -11.88 0.44 0.69 Ziqr 0.11 0.41 CRR 21.32 4.00 Ttops 10.65 0.00 VCI 3.23 2.31 111 5.3.2 Identifying structural groups Structural groupings described variability in forest structural development through time and across structural variables (Figure 38, Figure 36). Data clustering resulted in 8 unique spectral clusters that captured 47% of the variance in spectral metrics. For the eight unique clusters, results of PERMANOVA and post-hoc analysis identified four unique structural groupings within and across years (F (1, 14,385) = 40.62, p < 0.01; Figure 34). I describe and name these groups in the following paragraph. Figure 35 are true color composites captured by RPAs for groups fifteen years post-fire. Table 16 includes average values for the groups within and across years. Figure 34. Post-hoc description of differences in spectral clusters based on structural variables input into MANOVA analysis. Spectral grouping combined spectral clusters based on similarity (p > 0.1). Values are adjusted using the p-holm method for non-parametric data. Cluster 7 was similar across all clusters, but was combined with cluster 1 because this had the highest degree of similarity (p = 0.89). Groupings are as follows: Mixed Growth = cluster 1 and cluster 7, regrowth delay = cluster 3 and cluster 4, Regenerative Conifer = cluster 5, 6, and 8, Coniferous dominant mixed growth = cluster 9. 112 Figure 35. Orthophotos from RPA flights displaying distinct structural recovery groups 15-16 years post-fire. Values are the average values for each structural group 15-16 years post-fire. All images are at the same scale. 113 Table 16. Average values for each structural group (+/- standard deviation). Colors are the scaled values for minimum (dark blue) and maximum (yellow) of structural estimates across all years. 114 Descriptions of structural groupings: Mixed Growth: strong deciduous tree and shrub dominance early on (average in year 5-7 post-fire = 48% deciduous) with a low proportion of bare ground in later years (avg. = 7.9%). Regrowth Delay: a high proportion of bare ground for the first decade (average in years 8-9 = 75%) replaced by dense coniferous stems (average = 10,500 stems/ha in years 15-16). Regenerative Conifer: Strong early coniferous regrowth (average coniferous cover in years 8-9 = 84%) with high-stem densities in later years (highest stem density estimates in year 21 avg. = 15,035 stems/ha). Coniferous Dominant Mixed Growth: Generally lower stem densities (average in year 15-16 = 5,500 stems/ha) that correspond with lower BA (avg. in year 21 = 0.07 m²/ha). The most frequently observed grouping was regenerative conifer (38%), followed by regrowth delay (27%), while coniferous dominant mixed growth and mixed growth were less commonly observed (19.7-15.3%). Across the study area, mixed growth and coniferous dominant mixed growth were more common in the north, and regrowth delay was more common in the southern region (Figure 37). Mixed growth was common in small fires – dominant in the northern part of the study region (Figure 37). Regrowth and coniferous dominant mixed growth are common in large fires, concentrated in the southwest of the study area. 115 Figure 36. Structural estimates by group for each post-disturbance epochs (or years post-fire). Black lines are median structural value, boxes span the interquartile range and 95 % confidence values. # pixels included in calculations noted in Table 16. Note: mixed growth was not sampled 21 years post-fire. 116 Figure 37. Spatial distribution of structural groupings across the sub-boreal spruce and sub-boreal pine spruce Ecozones – inlay map shows ecozone location within BC. Hexagons are the most common structural group for each 10,000-ha area. Subsets (labeled A & B on map) left of the map are the groups at a 30 m resolution for two large fires (A - Chelaslie River Fire 2014, B- Plateau Complex Fire 2017). 5.3.3 Spectral differences among merged structural groups Recovery groups varied in metrics of spectral recovery. Generally, regrowth delay and conifer dominant mixed growth had lower slopes of spectral recovery (Figure 38). Regrowth delay had the lowest slope of recovery for both TCW and NBR (scaled averages -0.72 and -0.95). Conversely, mixed growth and regenerative conifer had 117 higher slopes of recovery. Regenerative conifer had the highest slope of recovery for the majority of indices, while mixed growth recovery had the highest slope of recovery for NBR (average = 0.73). Regenerative conifer also had the highest magnitude of regrowth for NBR, NDVI, TCA, TCB, TCG, while mixed growth had the highest regrowth for TCG and NDMI. Comparatively areas that were identified as conifer dominant mixed growth had the lowest regrowth magnitudes across all indices (Figure 38). However, average spectral measures were similar between conifer mixed growth and regenerative conifer five years after the fire. Finally, burn severity varied across structural groups (H(3, n = 39,553) = 9,608.7 (p < 0.001)). Conifer dominant mixed growth and regenerative conifer had the greatest burn severities (dNBR -0.65 and -0.55 respectively). While regrowth delay and mixed growth had lower estimates of burn severity (avg. dNBR of -0.43 and -0.48). 118 1 Figure 38. Average values of spectral metrics included in clustering for spectral groups. 2 Values are the normalized scaling applied before clustering. 3 119 4 Figure 39. Boxplots of variable importance from the Boruta implementation of the 5 overall random forest model. Colors denote driver type, and distribution is 6 the Boruta variable importance for 500 random forest trees. Importance 7 range 0-100, where 0 is no impact.8 120 9 Figure 40. Partial dependence plots for the top 6 most important drivers from the full random forest (19 selected drivers). The X-axis represents the predictor variable range, and the y-axis is the likelihood for a given structural group when all other variables in the random forest are held constant. Variables are listed in decreasing importance within the overall random forest. The labels above the plots note the units for each driver. “Z” is the z-score standardized value based on climate data 1980-2010 for each pixel. 5.3.4 Structural groupings are predicted by recovery drivers The predictive power of different categories of recovery drivers was derived from four individual RFs for (1) environment and site conditions, (2) pre-fire conditions and fire impacts, (3) climate conditions, and (4) post-fire climate anomalies. For these individual models, the two RFs built using drivers of post-fire climate anomalies and typical climate conditions had the highest overall accuracy (out-of-bag (OOB) accuracy 83 and 84% respectively, Table 17). Site-level/environmental conditions had an overall accuracy of 54% and RFs using pre-fire conditions and fire impacts drivers had an 121 accuracy of 66%. The final RF, which used the dominant individual drivers, included 19 predictor variables. The OOB accuracy was 84%, and when tested on a validation dataset, the overall accuracy was 82%, supporting that the model was not overfit. Regenerative conifer was predicted with the most accurately (mean precision and recall, F-score of 89%, Figure 41) while conifer dominant mixed growth was predicted with the lowest accuracy (balanced accuracy of 85%). Both conifer dominant mixed growth and regenerative conifer were frequently misclassified as mixed growth (12% of samples). Across all recovery drivers, cold and dry summers were the most important predictors of observed groupings (minimum summer precipitation and average coldest summer temperature, Figure 40). Annual climate anomalies, such as the warmest year and minimum snow, were also important (relative importance of 54 and 58, respectively). Typical climate conditions, including minimum winter temperature, average fall and summer precipitation, were second to climate anomalies in overall importance. Fire impacts (patch size and distance-to-edge) and site-level/environmental conditions (soil type, subzone, TRASP, and elevation) had similar relative importance values (41- 45%). Topographic position had a notably lower importance than other variables (relative importance = 12%). 122 Figure 41. Confusion matrix for overall random forest model classifying structural recovery from validation data (n = 14440) colored by the % of correctly classified pixels for each structural grouping. 123 Table 17. Random forests built for different driver types. For each random forest the overall accuracy, variables, and associated variable importance is noted. The overall random forest selected no more than five variables of each recovery driver type and no driver with a variable importance < 0.01. Anomalies are italicized. Model Type (Overall Accuracy) Ecological Drivers Variable Importance Environment and Site Conditions 0.54 Elevation (m) 0.04 Soil 0.03 TRASP 0.02 Flow Direction 0.02 Topographic Position 0.01 Pre-fire 0.66 Subzone 0.12 Patch (ha) 0.11 Pre-Fire BA 0.05 edge (m) 0.02 Pre-Fire Species 0.01 Pre-Fir Land Cover 0.00 Climate 0.83 Winter Min T 0.18 Summer Min T 0.18 Fall Precip 0.17 Summer Precip 0.17 Avg Precip 0.16 CMD 0.15 Spring Precip 0.15 Avg Summer T 0.14 Summer Max T 0.14 PAS 0.14 MAT 0.14 Anomalous Climate 0.84 Min Summer Precip 0.18 Warmest Year 0.17 Min Fall Precip 0.16 Avg Coldest Summer T 0.15 Min Snow 0.15 Min Summer T 0.15 Min Annual Year 0.14 Max Summer T 0.13 Min Coldest Winter T 0.13 Hottest Summer T 0.13 Max Warmest Winter T 0.13 Driest Year 0.12 Overall 0.84 Min Summer Precip 0.69 Avg Coldest Summer T 0.64 Pre-Fire BA 0.58 Min Snow 0.58 Warmest Year 0.54 Min Coldest Winter T 0.54 Min Fall Precip 0.50 Summer Precip 0.46 Soil 0.45 Patch (ha) 0.45 CMD 0.45 Min Coldest Summer T 0.44 Subzone 0.43 Fall Precip 0.43 Elevation (m) 0.42 TRASP 0.41 edge (m) 0.41 Flow Direction 0.33 Topographic Position 0.12 124 5.3.5 The relative effects of dominant ecological drivers on structural groupings Partial dependence plots compared the marginal effects of the six most important drivers. This comparison demonstrated that structural group likelihoods were associated with different ecological driver values (Figure 40). In some cases, only one structural group had different responses to an ecological driver, such as minimum summer precipitation. Regenerative conifer was the only structural group not negatively affected by minimum summer precipitation (Figure 40). Comparatively, particularly warm years were more common in areas with coniferous dominant mixed growth, and less common in areas of mixed growth. Mixed growth areas also had a lack of dry summers. Additionally, minimum snow deposition had a slightly positive effect on regenerative conifer, and a somewhat negative association with coniferous dominant mixed growth. The effect of pre-fire BA varied across structural groups. High pre-fire BA was positively associated with groups that had a higher proportion of deciduous stems after fire (both mixed growth and coniferous dominant mixed growth had higher pre-fire BA). 125 Figure 42. Boxplots of structural groupings for (left) Patch size calculated from NTEMS dNBR data and (right) Pre-fire mean (n = 5 years) BA in m²/ha. Boxes are colored by structural group. 5.3.6 Differences in pre-fire conditions and fire impacts across structural In the final RF, pre-fire BA was the third most important predictor (Figure 42). Pre-fire BA estimates was highest in mixed growth (average BA = 24.60 m²/ha), while lowest pre-fire BA estimates were in regrowth delay areas (12.5 m²/ha). The lower significance of pre-fire BA in the overall RF (Figure 42) is likely because pre-fire BA was similar between conifer dominant mixed growth and regenerative conifer (Figure 42; pre-fire BA = 16.2 vs 16.0 m²/ha, respectively). Patch size was the most important variable for fire impacts (compared to distance-to-edge). Similar to pre-fire BA, conifer dominant mixed growth and regenerative conifer 126 had similar patch size (Figure 42, patch size = 5.36 vs 6.4 ha). Comparatively, mixed growth had the smallest patch sizes (average patch size = 4.3 ha) and the highest pre-fire BA estimates (average BA = 24.60 m²/ha). Finally, regrowth delay had the highest average patch size (average = 6.2 ha). 5.4 Discussion Post-fire trends and patterns of structural recovery were strongly impacted by a single year of cold or dry summers within the first five years after a fire. Of the four categories of recovery drivers (environment, climate variables, post-fire climate anomalies, fire impacts, and pre-fire conditions), post-fire climate anomalies best predicted structural recovery. Specifically, anomalous cold and dry periods impacted regeneration timing and the ratio of deciduous stems. Areas of delayed coniferous regeneration experienced particularly cold summers. In contrast, areas with a higher proportion of deciduous cover (coniferous dominant mixed recovery) had fewer very cold summers and generally higher summer precipitation. 5.4.1 Early spectral responses described longer-term differences in structural recovery When the eight spectral clusters were merged into four unique groupings of structural recovery, the structural groups remained unique in spectral responses (Figure 38). Thus, these early spectral dynamics characterized longer-term differences in structural recovery, specifically differences in the rate of coniferous establishment, proportion of deciduous trees, and stem densities. This research builds on the connection between unique post-fire spectral responses and differences in structural recovery (Guz et al., 2022; Kiel and Turner, 2022) but also raises important 127 considerations when using spectral trends to identify structural recovery, and implementing a space-for-time study. The four observed structural groups have similarities with recovery patterns and trends observed in other similar environments (Kiel & Turner, 2022; Littlefield, 2019; Menick et al., 2024). In particular, the regrowth delay group had relatively low stem densities and a high proportion of bare ground 8-12 years post-fire, supporting slow conifer establishment. Both Littlefield (2019) and Menick et al. (2024) observed slow conifer establishment in other conifer forests of western NA, suggesting new coniferous stems can take 10 years to establish and/or detect. Interestingly, the group with the highest conifer stem density in the early post-fire (+ 5) years was mixed growth. However, high stems densities in mixed growth could also reflect residual canopy– a response also noted for the residual canopy group described In RQ4. The loss of residual canopy, common in the first decade post-fire; (Bolton et al., 2015; Frazier et al., 2018) would also explain the drop in stem-density in years 8-9. In conifer forests, high-severity fire is often associated with post-fire dominance of shrub or aspens (Lee, 2004; Paudel & Markwith, 2023). However, burn severity was not indicative of a deciduous response; the group with the highest burn severity (conifer dominant mixed growth) had the highest proportion of deciduous cover in years 5-7 (Figure 36), and maintained a conifer dominant composition throughout the period of study. Future research in dry coniferous forests should be cautious to associate “fast” spectral recovery to an increase in regeneration of deciduous tree species. The spectral responses of structural groups aligned with prior work supporting the trend that a fast 128 spectral recovery could indicate a greater deciduous component (Vanderhoof et al., 2021). However, the fast spectral recovery for the groups with higher deciduous composition was only true for NBR. Specifically, mixed growth had the highest NBR recovery rate while regenerative conifer, which had the highest proportion of conifers and highest stem densities, had a faster NDVI spectral recovery rate. Thus, in dry coniferous forests, a faster rate of spectral recovery for an index such as NBR or NDVI may not necessarily be indicative of a greater abundance of deciduous tree cover. This conclusion is inline with findings of Celebrezze et al. (2024). They delineated areas of “fast” and “slow” spectral recovery and found that the rate of spectral recovery (both NBR and NDVI) distinguished shrubs and conifers from grass, but could not distinguish shrubs among conifers. In this study “slow” spectral recovery rates were consistently associated with the regrowth delay group. Slow spectral recoveries are likely a result of a high proportion of bare ground in the early post-fire years. Thus, I posit that spectral recovery rates are then best descriptors of general vegetative recovery. Although responses of spectral for conifer-dominated mixedwoods did not differ markedly from conifer-dominant responses based on single indices, multiple indices separated mixed-growth responses from areas of regenerative conifer. Past research in dry conifer forests has struggled to differentiate deciduous from conifers using spectral data (Blanco-Rodríguez et al., 2023). To address these difficulties, Blanco-Rodríguez et al. (2023) suggest monitoring approaches that combine climate, site-level conditions, and spectral data. While integrating environmental conditions increases the accuracy of recovery monitoring from satellite data (Pérez-Cabello et al., 2021), my approach, combining multiple spectral responses, identifies areas where there was similar 129 structural recovery despite different site-level conditions. For example, regrowth delay was predominant in cold-dry sites, but regrowth delay was also observed in areas typically wet and productive (Figure 40). If the initial identification of structural recovery included site-level variables, identification may omit typically wet and productive areas experiencing unexpected regrowth delay. In all cases, it is important to validate spectrally identified recovery with field data because spectral responses, while useful to identify overall rates of vegetation growth, cannot universally distinguish shrubs and trees. Additional forest structural data from lidar acquisitions, supplemental to the dataset of RQ2, clarified trends and patterns of recovery. For example, RQ2 identified some areas with remaining residual canopy and delayed regrowth, but the sample size for these areas was low (25 pixels). Additional sampling (3,778 pixels) confirmed these areas, classed as regrowth delay, had high ground cover and stem densities five years post-fire, which may be from residual canopy. Conifer establishment in the regrowth delay group was high in years 15-16, which reflects findings of Kiel et al. (2025), who found conifer recovery could take multiple decades to establish. The approach herein lacks the precision to compare temporal trends within groups. Consider the regrowth delay class, which had extremely high stem densities 15-16 years post-fire, and lower stem densities in year 21. The drop in stem density between years 15 and 21 could signify the start of stem-exclusion (Bartels et al., 2016), but it could also be a result of site differences not considered in the space-for-time sampling. Fortunately, BC is currently conducting a province-wide lidar acquisition (White et al., 2025). This acquisition will provide high-quality, spatially continuous lidar data allowing future work 130 to test how site-level differences influence recovery, such as the onset of stem exclusion. In addition to capitalizing on lidar data, future work should incorporate prior information on tree species composition using remote sensing and/or field data (White et al., 2023). This information could clarify how observed structural recovery aligns or does not align with management goals and historic ecosystem composition (Johnstone et al., 2016). For example, mixed growth responses may align with pre-fire composition (Jorgensen et al., 2023) and/or historic deciduous forest composition altered by 20th century forest management (Baron et al., 2022; Brookes et al., 2021). 5.4.2 Structural group variability was associated with anomalous post-fire climate In this study, structural recovery was best predicted by anomalous post-fire temperatures and precipitation, echoing research findings in coniferous dry Western US forests (Table 17, Guz et al., 2021; Young et al., 2019). Prior research suggests that drought conditions, measured via CMD, negatively impact coniferous recruitment (Davis et al., 2023; Stevens-Rumann et al., 2022). This study suggests that colder summers were more important than typical CMD. The decreased importance of CMD may be because lodgepole pine, the dominant conifer of the study area, is less impacted by drought than other conifers (Harvey et al., 2016). Further, lodgepole pine establishment and growth can be limited at low temperatures (Hansen & Turner, 2019). The positive association of a single particularly cold summer and the slow establishment rates of regrowth delay suggests lodgepole pine establishment in sub-boreal may be temperature limited. The relationship between delayed lodgepole pine 131 establishment and lower temperatures has also been found in subalpine environments in southern Colorado (Guz et al., 2021) and post-fire forests of the Greater Yellowstone ecosystem (Hansen & Turner, 2019). The study area’s the average mean summer temperature from 1980-2010 was 12.7 C˚, below the 14 C˚ threshold Hansen and Turner (2019) identified as the temperature at which lodgepole pine recruitment is more limited by temperature than moisture. The two structural groups with a higher deciduous proportion had different responses to extreme post-fire temperatures and precipitation (Figure 40). For example, mixed growth was negatively impacted by dry summers and warm years. However, conifer dominant mixed growth was positively associated with extremely warm years. These differences could reflect availability of underground resources – as resprouts, deciduous (both aspen trees (Populus tremuloides) and shrubs) can rely on unburned below-ground resources to buffer impacts of extreme climate (Johnstone et al., 2020; Young et al., 2019). Importantly, these unburned below-ground resources are inaccurately captured with satellite estimated burn severity (Loboda et al., 2013). Satellite measures of burn severity characterize remaining above ground vegetation post-fire (Frolking et al., 2009; Miller & Thode, 2007). To better understand the relationship between climate extremes and deciduous regeneration post-fire, recovery models should also consider how unburned soil organic material (1) facilitates post-fire deciduous sprouting (Shenoy et al., 2011), (2) promotes competition between deciduous and conifer establishment (Stark et al., 2006), and enables resprouts to endure climate extremes (Young et al., 2019). 132 The association between lagged coniferous establishment and cold summers in the sub-boreal environment has important implications given observed and projected warming in BC (MacKenzie & Mahony, 2021). While increasing temperatures may decrease areas with delayed regrowth, drought conditions could accompany rising temperatures. Prolonged droughts can independently cause regeneration lag (Guz et al., 2021) or even coniferous regeneration failure (Stevens-Rumann et al., 2022). As multi-annual climate conditions have also been linked to differences in coniferous recovery (Kemp et al., 2019; Shuang & Christopher, 2012), future research should consider the impact of prolonged (> 1 year) droughts and mean climate conditions multiple years post-fire. 5.4.3 Site-specific conditions had minimal impact on individual structural groupings Pre-fire BA was the third most important driver across all structural groupings. This finding may partially result from the similar pre-fire BA between regenerative conifer and conifer-dominant mixed growth groups. Prior work in the boreal forests found that greater pre-fire BA was associated with slower NBR recovery rates (White et al., 2023). The authors found that high pre-fire BA was associated with areas that did not spectrally recover (surpass the 80% of the pre-fire NBR value; White et al., 2023). However, areas of high pre-fire BA also had higher pre-fire NBR, meaning that these sites had a higher pre-fire NBR to return to. In this research, higher pre-fire BA was associated with a faster spectral recovery rate, but they required a longer time period to achieve spectral recovery (80% pre-fire NBR). Thus, it is essential to consider which spectral metric (rate vs. years to recovery) is considered for recovery. Further, drivers of spectral recovery 133 may also differ by study region. In the sub-boreal areas, soil nutrients often limit productivity, and areas with high pre-fire BA would also be concentrated in regions with high soil nutrient density (DeLong et al., 2003). Contrary to the research hypothesis, I observed a minimal impact of elevation on structural recovery trends. Prior studies using spectral data have found that elevation has a strong impact on spectral trends. White et al. (2023) found that a greater proportion of high-elevation field plots had not yet spectrally recovered by the end of the time series (25 years post-fire). Similarly, Kiel and Turner (2022) found elevation had the greatest impact on spectral recovery and stem densities 30 years post-fire, followed by slope and distance-to-seed. However, Kiel and Turner (2022) did not consider post-fire climate conditions and White et al. (2023) did not directly test the impact of elevation against climate conditions. Thus, differences between my findings and those of Kiel and Turner (2022) as well as White et al. (2023) suggest that the importance of recovery drivers likely depends on which drivers are considered, how recovery is defined, and which spectral metrics are used for recovery assessments. Additionally, Kiel and Turner (2022) measured recovery of a single fire year (1988). This suggests that recovery from a single fire year, where post-fire climates are similar, recovery may be more driven by site-level differences such as elevation, but over multiple years, recovery may be more impacted by post-fire climate conditions, such as variable post-fire precipitation. 5.5 Conclusions Effective forest management in a rapidly changing climate and intensified fire regimes requires understanding what drives post-fire forest recovery. This investigation combined satellite and RPA lidar data to observe strong post-fire conifer recruitment 134 across BC's expansive burned subboreal. Secondly, post-fire anomalous summertime climates and pre-fire BA best predicted variability in structural recovery groupings. Abnormally cold and dry summers delayed conifer establishment, while warm years and/or higher pre-fire BA increased post-fire deciduous recruitment. To extend the use of this work, future research should investigate how longer-term climate conditions (e.g., multiple years) or better soil burn severity classification (which captures available below-ground resources) improve detection of post-fire structural recovery. Ultimately, these structural recovery groupings and associated ecological drivers help forest managers prioritize action and non-action areas. Possible non-action areas, such as conifer dominant mixed growth, post-fire recovery restores the historic composition, and/or mitigates future fire risk. Comparatively, possible action areas, such as regrowth delay, result in heavy post-fire fuel loads and/or barren landscapes prone to landslides (Davis et al., 2024; Larson et al., 2022; Lau, 2022; Swanson et al., 2023). Importantly, the likelihood of an action area, such as regrowth delay, is associated with post-fire climate extremes. Thus, monitoring and management should capture the initial fire event and adapt as conditions evolve during the crucial early recovery period. 135 Synthesizing Spectral and Field Observations of Post-fire Conifer Recovery in Dry Forests4 6.1 Introduction Pervasive fires have burned dry conifer forests that dominate the Western Cordillera throughout the Holocene (Hessburg et al., 2005). Recently, hot and dry summers have promoted declines in post-fire conifer establishment and growth (Davis et al., 2019; Hansen & Turner, 2019). Post-fire observations suggest some historically dominant conifer forests are on post-fire pathways towards open grassland (Hamilton & Burton, 2023) or mixed forest ecosystems (Kulakowski et al., 2013). Recovery studies often use point-in-time metrics of conifer stem density or cover to describe pathways distinct in composition, structure, and growth rate (Figure 43). Importantly, what pathways are identified depends on measurement metric (e.g., stem density), measurement timing (e.g., years’ post-fire), and measurement frequency (e.g., single observation versus repeated). Measures from a single time-point could mask a recovery pathway that initially lags in conifer establishment (Figure 43, Stevens-Rumann et al., 2022). Therefore, the identification of recovery pathways is both time- and space-dependent; a study that measures conifer growth rates may identify different pathways compared to a study measuring stem density (Figure 43). 4 The contents of this chapter have been adopted from a manuscript submitted and under review as follows: Smith-Tripp, S., Coops, N. C., Meyer, J., White, J. C., & Gergel, S., Axelson, J., Roeser, D., Synthesizing Spectral and Field Observations of Post-fire Conifer Recovery in Dry Conifer Forests. Under review at Ecosystems 136 Recovery pathways vary due to differences in ecological drivers, such as climate and/or disturbance history (Drever et al., 2006). Ecological drivers alter temporal trends in conifer establishment and influence stem density or species composition patterns (Baltzer et al., 2021; Littlefield, 2019; Stevens-Rumann & Morgan, 2019). Studies use different methods to relate recovery with ecological drivers. These methods include benchtop or greenhouse experiments, observational field surveys, and large-scale remote sensing methods. Benchtop or greenhouse experiments control the variability of drivers and then extrapolate the relationships between drivers and conifer growth to the landscape. Field studies are less common than greenhouse or benchtop studies; in a 2016 review on the impact of temperature and moisture on ponderosa and lodgepole pine establishment only 25% of studies were field-based (Petrie et al., 2016). Although greenhouse studies are crucial, it is difficult for a greenhouse study to compare the relative importance of temperature, easily manipulated in greenhouses, with a driver like fire severity or herbaceous competition, challenging to replicate in a greenhouse setting (Petrie et al., 2016). Opportunistic post-fire field surveys are the most common approach to associate forest recovery with ecological drivers. Importantly, the method used to measure the driver, the variability associated with the driver, and the other drivers considered all influence the relative importance of a driver in a model. Further, the scale of analysis also impacts the observed effects of ecological drivers (Peeler & Smithwick, 2021). Thus, regional investigations such as Davis et al. (2023) should synthesize insights from landscape-level analysis with locally impactful drivers (Peven et al., 2024). When using 137 field plot data for regional insights, the sampling error associated with plots and the substantial effort required to collate plots must be acknowledged (Persson et al., 2022). Spatially and temporally continuous observations of land surface spectral reflectance (Figure 43) can indicate general vegetation recovery (both patterns and trend) after fire (Kasischke & French, 1997). Observations minimize cost and time investments (Frolking et al., 2009), but are not direct measures of forest structure. Thus, relationships between spectrally identified recovery and drivers may differ from those identified in the field (Senf, 2022). Thus, spectral measures require validation using ground-truthing (White et al., 2022) or association with trends or values from the established literature (White, 2024). Capitalizing on the advantages of spectral data requires first understanding how spectral data identify recovery pathways and how associated ecological drivers complement and/or contradict field-based findings (White et al., 2023). This knowledge requires compiling the spectral and field-based recovery pathways identified in the literature and, for these pathways, determining the relative impact of different ecological drivers. This research chapter systematically synthesizes how early rates of conifer recovery (measured from establishment, density, basal area, composition, and spectral responses) are associated with different ecological drivers at landscape-scales. The systematic review specifically asks: What drives early recovery pathways in conifer-dominant forests of the Western Cordillera? Research objectives are to (1) synthesize observed pathways of early forest recovery for dry coniferous forests of the Western Cordillera, including pathways identified from spectral responses, and (2) synthesize the relative impact of different drivers on spectral and field recovery metrics. 138 Review takeaways describe the current state of knowledge for early conifer recovery and characterize how the study method (field versus spectral) or metric (trend versus pattern) affects the findings. This research synthesis also outlines the knowledge and methodological gaps that frame key questions and recommendations for future post-fire research. While concentrated on the Western Cordillera, research approach and findings are broadly applicable for synthesizing ecological findings across field and spectral domains. 139 Figure 43. Overview of forest recovery pathways, definition, and the importance of measurement timing and measurement metric in recovery monitoring. 140 6.2 Methods 6.2.1 Systematic search This research chapter used a systematic review approach to identify studies investigating pathways and associated drivers from SCOPUS, Web of Science, and the Agricultural & Environmental Science Database (AESD) before November 11th, 2024. The protocol followed the RepOrting standards for Systematic Evidence Syntheses in environmental research (ROSES) and the Preferred Reporting Items for systematic Reviews and Meta-Analyses (PRISMA, Appendix A, Collaboration for Environmental Evidence, 2022). The search query used a SPIDER framework (Sample Size, Phenomenon of Interest, study Design, Evaluation, and Research type; Cooke et al., 2012), built and tested in SCOPUS and then expanded to the Web of Science Core Collection, AESD, and technical reports hosted by databases of Canadian and US institutions (Table 18). To develop an effective literature search, I used a set of benchmarking articles selected by field experts. Additional studies were added from prior literature reviews as well as snowball searching (Wohlin et al., 2022). Table 18. List of Databases and Collections included in systematic literature review Exclusion criteria required the study to concentrate on dry-coniferous forests. Type of literature Name of Database / Collection Bibliographic SCOPUS, Web of Science, AESD Grey Literature Treesearch NRCan Open S&T Repository (OSTR) J.T. Natural Resource Library (BC Specific) Theses Canada Previous Reviews Stevens-Rumman et al., 2019 Bartels et al., 2016 Waldron et al., 2023 Additional Methods Citation searching Expert recommendations 141 All included studies investigated conifers regrowing after high-severity fire (Table 19). Specifically, studies were located in the Western Cordillera based on the Level Three Ecoregions (Omernik & Griffith, 2014) with a continental climate (Kottek et al., 2006). For studies that relied only on field data, the study also had to model field observations of recovery with three or more ecosystem drivers (this allowed comparison of relative effects across drivers). To capture the emergent literature using spectral monitoring, spectral studies were included even if they did not model the association between recovery and ecological drivers. Out of 2148 unique studies imported for screening, 1949 were excluded based on abstracts and 134 based on full text. Of the 66 studies that passed full-text exclusion, eight studies were excluded from quantitative synthesis based on quality appraisal (Figure 44). Quality appraisal addressed the core components of internal and external study validity; studies that passed quality appraisal are focused, extensive, applied, and transparent (Burford et al., 2013; Frampton et al., 2017). A total of 58 studies, 32 field-based and 26 spectral, published from 1996 to 2024, were included in data extraction (Figure 45, Figure 46). A complete description of the review protocol following the ROSES framework is included in 7.5Appendix A (Collaboration for Environmental Evidence, 2022). 142 Table 19. Inclusion and Exclusion Criteria for both title and abstract and full-text screening steps of literature review. Key Element Inclusion Exclusion Types of Study Studies that include quantitative data on coniferous and/or spectral recovery across a gradient of abiotic effects Commentary or Framework Reviews Language English Population Western Cordillera Forests interior forests with a coniferous component in western North America. Western north America includes: Western North America, Western US, Western Canada, Provinces and/or states in either Canada or US No explicit references to a post-fire coniferous response Boreal forest Concentrated on non-British Columbia Species (e.g. Bristlecone Pine, White fir (abies concolor), Manzanita, Ceanothus) Study only measures a tree species not included in the dominance species list Forests located in Mediterranean climate Exposure Fire High-severity wildfire Areas planted after fire Prescribed fire Reburned Comparator Drivers of Recovery Forest with differing ecological factors including: Pre-Fire composition, Fire Severity, Site-dynamics, Post-fire weather trends. Must include a consideration at a least two-or more comparators Studies that did not consider specified ecological factors Focused on invasive encroachment Studies which only consider fire severity metrics (i.e. no topography and/or climate characteristics) Outcomes Post-fire coniferous growth Rate of coniferous establishment (years to establishment), growth rate for coniferous treed Conifer presence can include: Volume, basal area, height, density, proportion on landscape Studies where post-fire responses did not consider a coniferous forest response Simulated responses Projected responses Studies where ecological response is a measure biomass or carbon sequestration AND division between live conifer is not given Measures of post-fire conifers that were alive pre fire (i.e. post-fire mortality focus) 143 Data extraction For each field and spectral study, I extracted methods and results describing recovery metrics, pathways, and associated drivers. Data extraction noted forest type, measure of fire severity, and whether the study used spectral or field methods. Extracted information also noted reported recovery trends. For studies that used spectral data to report trends, I extracted the approach to identify recovery (e.g., linear modeling or spectral threshold), the index and/or spectral bands, the number of years to determine pre-fire spectral baseline, the number of years to detect trend(s), and if and how distinct trends were associated with field observations. For all studies that reported a distinct recovery pathway, extraction included: metric type (e.g., composition versus stem density), year of post-fire observation, the pre-fire forest type, and the post-fire forest type. I extracted post-fire conditions with unique temporal trends and different patterns of composition or structure. For composition, I categorized post-fire growth as (a) mixed-conifer, (b) mixed-growth that includes deciduous and conifer species, or (c) single-species. For structure, I noted differences in the density of growth compared to the assumed pre-fire composition or if there was a temporal delay. 6.2.1.1 Ecosystem drivers For studies that associated recovery metrics with ecological drivers, extracted information included the recovery metric, model approach, model accuracy, and tested drivers. When studies built multiple models for different species or forest types, model results were extracted for each species and forest type. For each driver, extracted information included direction of effect on the metric of conifer recovery, the parameter estimates, how effect was measured (e.g., variable importance versus slope 144 parameter), a range for driver significance (e.g., very high to not based on p-values or variable importance scores), and degrees of freedom. The standard errors or the precise p-value of each driver were extracted to calculate the effect size. When drivers did not note a parameter value, I determined associations with recovery from textual reference. When studies tested transformed aspect, I maintained cross-study consistency by ensuring northern aspects were associated with lower values (e.g., 0 for a Beers aspect transformation; Ferguson et al., 1989). I also noted when drivers were tested as a part of model selection but not included in the final modelling due to model parsimony. Where available, I extracted estimates of model accuracy (adjusted R2 or AUC). When necessary, I contacted the corresponding author for additional information on the coefficients, approaches, or results. All studies were subject to critical evaluation to minimize bias from confounding factors or study-site selection (see 7.5Appendix A for more details). To allow for inter-study comparison despite variability in selected ecological drivers, drivers were arranged into groups and organized under broader Categories. For the ease of the reader, “Categories” are capitalized throughout the text. The eight described Categories are: (1) Physical-site Characteristics, such as elevation, slope, soil composition (2) Abiotic-site Characteristics, such as the terrain wetness index ( a measure of site aridity), average annual temperature, average site-level precipitation. (3) Pre-fire Biotic Characteristics, such as pre-fire basal area 145 (4) Fire Impacts, such as fire severity or distance to the nearest unburned area (5) Post-fire Weather, such as average post-fire precipitation, or average summer temperatures post-fire. (6) Anomalous Post-fire Climate, which includes the same metrics of post-fire weather, but metrics are normalized to the historical average climate conditions. (7) Management, such as post-fire salvage logging (8) Interaction terms and/or random effects, such as the interaction between aspect and precipitation Categorizations separates post-fire climate measures from anomalous post-fire climate (Post-fire Weather and Post-fire Anomalous Climate, respectively). Post-Fire Anomalous Climate normalizes to site-averaged climate, typically using z-score transformation (Davis et al., 2023). For details on associated groupings and categories for individual drivers, see Appendix B.3: Supplemental Table 3. Tallies for the total number of models noting a positive, negative, or neutral effect on the associated metric of conifer recovery were calculated by group. Calculations also included the frequency that a driver from any given group was both tested and included in the final models. To assess the relative impact of driver groups across studies, parameters were normalized within and across models. Normalization was as follows: the driver with the largest effect or importance was 1, and the least important driver was 0. Then these normalized values were multiplied by the overall accuracy of the model and the direction of model effect. For these normalized values, a value closer to one describes a driver 146 that positively impacted the metric of recovery and was associated with a highly accurate model (generally R2 > 0.7). Comparatively, an important driver included in a model with low accuracy would be closer to zero. Finally, a value close to -1 describes a driver that negatively impacted conifer recovery and was associated with a highly accurate model. Normalization allowed comparison between statistical approaches, such as random forest and linear regression. 147 Figure 44. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram for systematic review, including the identification, screening, and inclusion process. Final number of distinct studies is 58. 6.3 Overview of results This meta-analysis and narrative review synthesized findings from 58 studies investigating forest recovery pathways and drivers in the Western Cordillera of North America (see Appendix Supplemental Table 1 for a list of included studies). First, I begin with a summary of the included literature, highlighting metrics and methods used to identify pathways, years of post-fire assessment, and methods for determining fire 148 severity. I then address objective (1), identifying key recovery pathways from the included literature, with an emphasis on the application of spectral data to discern these pathways. Then, I address objective (2), assessing the relative importance of ecological drivers of recovery. Recovery models rarely link a pathway, which is both a trend and a pattern, to ecosystem drivers. Thus, results highlight how ecological drivers relate to recovery metrics measured, such as stem density, seedling/sapling presence, and spectral recovery associated with conifer growth. First, I outline how individually measured drivers are categorized into broader driver groups, as well as the frequency at which groups are assessed. Subsequently, for each driver group, I consider its overall importance across models, considering how the specific metric measured (e.g., stem density or spectral response) influences the relative importance of an ecological driver. 6.3.1 Studies included in review One-third of the total burned area in the western Cordillera is in Canada, but studies are disproportionally located in the United States (Figure 46). Research is predominantly field-based, but 26 studies (44% of extracted) use spectral data to monitor and/or model recovery (Figure 45). Half of the extracted studies, both field and spectral, describe distinct recovery pathways related to species composition, forest structure, or temporal trends. Forty-one studies (70%) also modeled variable recovery responses associated with different drivers; of these, 13 (32%) investigated how different ecosystem drivers impacted spectral metrics of conifer recovery. The most common pre-fire forest type for extracted studies is mixed conifer (41%), followed by ponderosa pine and Douglas-fir dominated forest (13%). The time frame for 149 post-fire assessment ranged from 1-60 years, with an average of 15 years. When studies include a metric of field-based recovery, the most common metric is stem density (41%), followed by seedling presence (13%). All studies assess high-severity the fire severity, but methods to determine fire severity varied. Most studies used relativized dNBR (RdNBR; 14%), followed by a combination of dNBR and MTBS (13%, Table 21). 150 Figure 45. Included studies (n = 58) ordered by year of publication (left to right) and colored by approach: field-based (black) or spectral remote sensing (blue). Lines are citation links. Studies colored in yellow are associated with other relevant publications that were ultimately excluded in the final data extraction due to duplicate field plots. Spectral studies also note whether the study uses a threshold, temporal, or machine learning/clustering response to identify recovery pathways (legend top left). 151 Figure 46. Map of the Western Cordilleran eco-region subset to areas that experience a continental climate. Areas with wildfires in this eco-region are shown in black. Total fires (in km2) from 1984-2022 by latitude band shown at left. Fire locations identified using the Landtrendr algorithm (Kennedy et al., 2018), validated with the USGS MTBS (Finco et al., 2012) and the Canadian National Fire Database (Skakun et al., 2024). Points are the center of the bounding box for studies included in data extraction. Studies that appear outside the Western Cordillera are landscape-level studies. Color denotes if the study is spectral (uses satellite data) or field-based (uses only plot-level data) 152 6.3.2 Objective 1: distinct recovery pathways of the western cordillera From the extracted literature, the most commonly observed pathways suggest self-replacement (Figure 47). Alternative pathways predominantly shift in composition, where the pre-fire dominant conifer species lose dominance to deciduous trees and shrubs, grass, or bare ground. Additionally, pathway identification is more common with spectral methods than with field-based studies. The review identified a total of 31 different pathways from 28 studies. Of these studies, 75% used spectral data to identify pathways. Overall, spectral studies identified conifer forests that returned to conifer forests 32% of the time, pine forests to pine forests 10% of the time, and lodgepole pine to lodgepole pine forests 5% of the time (Figure 47). Less fire-adapted forests, such as fir-spruce or mixed forests, occasionally transition towards serotinous lodgepole pine or larch. State-changes, including transitions from coniferous to non-coniferous mixed forest and/or shrubs, are described an approximately equal number of times (n = 8 and n = 7, Figure 47). Interestingly, studies include pathways of ponderosa pine transitions to shrubs or grass (n = 4), but no study includes transitions of pine forests to mixed forests (Figure 47). When studies did not observe recovery during the observation window, recovery outcomes were described as binaries between ‘recovered’ or ‘not-recovered’ (Davis et al., 2019; Kiel & Turner, 2022; Menick et al., 2024; Vanderhoof & Hawbaker, 2018). Self-replacement pathways included areas with variable recovery rates or recovery delays. Among field-based studies, Kulakowski et al. (2013) considered differences in recovery rates and initial stand dominance in stands of different ages and disturbance histories. They describe two pathways of self-replacing lodgepole pine, which differed 153 based on pre-fire stand age. After the fire, lodgepole pine stands that were younger pre-fire rapidly regenerated at high densities. Comparatively, lodgepole pine stands that were 200 years or older pre-fire had lower post-fire stem densities. Kulakowski et al. (2013) also considered lodgepole resiliency, finding that young lodgepole stands subject to compound disturbances (blowdown or beetle outbreak, followed by fire) were more likely to have state-changes where lodgepole forest transitioned to aspen post-fire. In spectral studies, areas with low rates of spectral recovery were associated with 'delayed' conifer recovery. For example, Menick et al., (2024) used snow-season NDVI to differentiate between areas with successful conifer establishment 29-34 years after fire in northwestern Montana. They found that after fire, post-fire canopy mortality caused spectral responses of snow-cover NDVI to decrease for the first 10-20 years. Positive NDVI trends were delayed from the original fire event, which they attribute to an associated delay in conifer recovery. The pathway of recovery delay is also noted in other spectrally based (Epstein et al., 2024; Smith-Tripp et al., 2024a; Smith-Tripp et al., 2024b; Vanderhoof & Hawbaker, 2018). In addition to pathways of delayed recovery, Vanderhoof & Hawbaker (2018) used pre-fire spectral values to support changes in conifer density. They suggest that a decrease in the magnitude of NDVI change between winter and summer pre- and post-fire baselines corresponded to a structural change pathway, dense conifers transitioned towards sparse conifers as a result of fire. 154 Figure 47. Coordinate plot representing the literature identified pre-fire forest types, post-fire forest types, and notable changes in conifer density or time to establishment (i.e., delay). Lines are colored by the distinct recovery pathway. Line width represents the frequency the pathway was extracted from studies (total observations = 107). 155 6.3.2.1 Objective 1 (a): discerning recovery pathways with spectral data Approaches to identify pathways were diverse among spectral studies, complicating interstudy comparison. Differences lie in data processing, methodology, and associated field observations (see Table 20). Most of the studies included in the review used data acquired from the Landsat satellite series (23 of 26). Older studies, such as Jakubauskas (1996), Mitchell & Yuan (2010), Chen et al. (2011), used single-date Landsat acquisitions. More recent studies created seasonal composites (21 out of 26), but compositing approaches and definition of season varied (Table 20). For example, Menick et al., (2024) used maximum and median composites for the growing season, May-October, to assess canopy recovery in northwest Montana. In eastern Oregon, Washington, and Idaho, Celebrezze et al. (2024) defined the growing season as June-August. Most studies transformed satellite reflectance to surface reflectance, but Kiel & Turner (2022) found top-of-atmosphere reflectance (TOA) better correlated with coniferous recovery for the Greater Yellowstone Ecosystem. Methods for pathway identification were equally variable as those for satellite data processing. First, studies used different spectral indices: NDVI was most frequent (12 of 26), while NBR or index combinations were equally frequent (8 of 26). Second, studies varied in the number of years used to establish pre-fire spectral baselines to gauge post-fire change. Baseline periods vary from immediately before the fire (Buma, 2012) to 10 years prior (Celebrezze et al., 2024; Smith-Tripp et al., 2024a), with a median of 3 years. Some studies exclude the year before the fire due to pre-fire droughting. This is important as drought-stressed conifers have lower spectral reflectance (Vanderhoof & Hawbaker, 2018; Vanderhoof et al., 2021). 156 After fire, different spectral trends and associated pathways were identified from (1) temporal slopes (n = 9), (2) relative or absolute change (n = 8), (3) classification or clustering (n = 5), and spectral brightness thresholds (n = 2). Twelve studies validated spectral differences using field data, light detection and ranging (lidar) imputed structure estimates, or high-resolution imagery. Observations used to validate forest structure include combinations of canopy cover, stem density, basal area, or canopy height. Trends were identified from spectral satellite observations taken 1 and 30 years after the fire event, averaging 9 years. Interestingly, Epstein et al. (2024) found no accuracy differences when calculating spectral recovery rates using different observation windows (5, 10, or 15 years). Comparing spectral trends across studies was challenged by the diversity of data processing, selected spectral indices, and methodologies. Instead of a quantitative comparison, I include a narrative synthesis of how different spectral trends describe structural recovery pathways. First, these studies generally show that areas with higher spectral reflectance have more vegetation cover (Buma, 2012; Kiel & Turner, 2022; Menick et al., 2024; Smith-Tripp et al., 2024b; Vanderhoof & Hawbaker, 2018; Vanderhoof et al., 2018). Additionally, higher spectral recovery rates indicate more vegetation regrowth, but not necessarily coniferous regrowth (Smith-Tripp et al., 2024b). Vanderhoof & Hawbaker (2018) found high NDVI values in the growing season indicated deciduous species, while wintertime NDVI was better suited for characterizing coniferous recovery. Similarly, Celebrezze et al. (2024) found that the recovery rate of NDVI and NBR did not distinguish between shrub and conifer dominant areas, but grassy areas had lower recovery rates. Grass was not universally associated with 157 slower spectral recovery – both Chen et al. (2011) and Mitchell & Yuan (2010) found that grass had higher initial recovery rates compared to dense Ponderosa pine stands after the 2002 Jasper fire in South Dakota. Chen et al. (2011) also considered which index best described differences in land cover postfire and suggested that the integrated forest index (IFI) and the NBR displayed the most variability across ponderosa pine, mixed conifer, and grassy study sites. Although Chen et al. (2011) is the only study to test the integrated forest index (IFI), the value of NBR for differentiating among distinct trends is noted by several different studies (Celebrezze et al., 2024; Menick et al., 2024; Smith-Tripp et al., 2024a, 2024b). 158 Table 20. Description of methods used to process satellite data and identify spectral trends for studies included in the systematic review (n = 26). Variable Value Number of Papers (n =26) Landsat Spectral Composite Approach Maximum 8 Mean 2 Median 4 Median, Maximum 1 Other 11 Spectral Metrics All Landsat Bands 1 Combination of Bands/Indices 7 EVI 2 MODIS Summer LAI 1 NBR 8 NDVI 9 NDVI (Growing Season) 1 NDVI (Snow cover) 2 SAVI 1 SWIR (band 5) 1 Years to Establish Baseline 1 2 2-4 15 5+ 5 Other 4 Observation Window 1-5 6 11-20 6 21+ 2 6-10 11 Other 3 Method to Assess Spectral Recovery Change 5 Cutoff 2 Other 1 Phenology 1 Relative Change 3 Slope 9 Supervised Classification 2 Unsupervised classification 3 Field or Lidar Verified Structural Attribute Combination of Measures 6 Composition 9 Density 3 No Field Assessment 8 Seedling Presence 1 159 6.3.3 Objective 2: the relative influence of recovery drivers Models linking recovery patterns and trends to ecosystem drivers vary by measurement metric and statistical approach. Many studies include multiple models for specific forest types (e.g., lodgepole pine and Engelmann spruce; (Littlefield 2019) or regions (e.g., southwest Washington versus Montana Rockies; (Wilson et al., 2021). In total, the directional association of ecosystem drivers and recovery metrics was extracted from 150 distinct models. Modeling details, including tested drivers and effects, are noted in Table 21. Two-thirds of models are derived from field-based studies (98 out of 150, Table 21). Across all models, the most common statistical approach is generalized linear regression (40%), while random forest and boosted regression are the second and third most common (11.4% and 10.7%, respectively, Table 21). Metrics of stem density, spectral response, and seedling/sapling presence are the most common response variables in models (42, 35, 13 %). Other metrics, such as tree species composition, height, or seedling establishment rate, are less common. For the most common recovery metrics, spectral and stem density, model accuracies are similar (average stem density model accuracy = 0.54, average spectral model accuracy = 0.56). The presentation of results focuses on the most tested recovery metrics: stem density, spectral response, and seedling/sapling presence. As models of recovery patterns and trends assess many different drivers, drivers were grouped by ecological similarity. For the more than >1000 drivers extracted, drivers were placed into 28 groups and 8 Categories (see Appendix B.3: Supplemental Table 3). Notably, while the investigation focuses on naturally recovering forests, select studies include comparison to planted or salvaged areas (Andrus et al., 2021; Clason et 160 al., 2022). Studies are included for completeness (Post-Fire Management), but the discussion focuses on drivers present in unmanaged stands. Of the eight Categories, Fire Impacts represented almost half (n = 452) of the tested drivers, while Post-fire Weather and Post-Fire Management were the least common (Figure 48). Within the category of Fire Impacts, the post-fire canopy group (including drivers such as distance from the forest edge) was the most frequently tested (n = 220). Physical-site Characteristics (n = 295), including elevation and aspect, were the second most commonly modeled drivers (Figure 48). 161 Table 21. % of studies that measure distinct trends, forest-type, fire severity measure, structural measure, modeling method, and study type (spectral of field based). Proportions calculated from total number of unique models (n = 150). Variable Value % Count Measure of Fire Severity Aerial Imagery 2 3 Aerial imagery, SAVI 1.3 2 Measure of Fire Severity Metric Measured CBI 2.0 3 No direct measure 4.0 6 MTBS 24.0 36 MTBS, Other - Field assessed tree canopy 7.3 11 Other - Field assessed tree canopy 6.0 9 Other - Field assessed tree canopy / ground scorch 6.0 9 Other - Field assessed tree canopy, mortality > 90% 2.7 4 RdNBR 14.7 22 RdNBR, MTBS 0.7 1 RdNBR, MTBS, Other - Field assessed tree canopy 1.3 2 dNBR 12.0 18 dNBR, MTBS 13.3 20 dNBR, MTBS, Lidar Imputed Canopy Cover Loss 0.7 1 dNBR, Other - Field assessed tree canopy and scorch 7 dNBR, dNDVI 0.7 1 Composition 1.3 2 Height 2.0 3 Metric Measured Modeling Approach Live Carbon 0.7 1 Regeneration Intensity (Spatial Clumping of Stems) 2.0 3 Relative Seedling Germination Success 0.7 1 Seedling / Sapling Presence 13.3 20 Seedling Establishment 1.3 2 Spectral Response 34.7 52 Stem Density 41.3 62 Stem Density, Composition 2.0 3 Stem Density, Seedling / Sapling presence 0.7 1 ANOVA 2.0 3 Boosted Regression Trees 10.7 16 Modeling Approach Paper Type Canonical Correspondence Analysis 0.7 1 Correlation 1.3 2 Cox Process Models 0.7 1 Generalized Additive Models 4.0 6 Generalized Linear Models 40.0 60 Compare groups 0.7 1 Inhomogenous Cox Process 1.3 2 Logistic Regression 5.3 8 NMDS 0.7 1 Non-Parametric Multiplicative Regression 0.7 1 Partial Least Squares 0.7 1 Random Forest 11.4 17 correlation 0.7 1 spatial pattern density models 2.0 3 Did not Model 14.7 22 Spectral 34.7 52 Structural 65.3 98 162 Figure 48. Frequency of different groupings (arranged by Category) of ecological drivers across all studies. Labels are the groupings, and numbers are the total number of times a driver within the group was extracted. 163 The frequency of different driver groups included in models varied by measurement metric, including stem density, seedling/sapling presence, and spectral response. For the 150 distinct models included in data extraction (see Appendix B.4: Supplemental Table 4) spectral methods included more drivers (parameters) than field methods (median number of drivers for spectral = 9 versus 6 for field). Among categories of driver groups, field methods more frequently considered Abiotic-site Characteristics and Physical-site Characteristics. Conversely, spectral methods more frequently considered Pre-fire conditions, Post-fire Weather, and Post-fire Anomalous Climate. Post-fire canopy was the most frequent driver grouping included in both models of stem density and seedling/sapling presence. Fire severity and elevation were more frequently included in spectral recovery models (Figure 48). The likelihood that a driver was included in a model varied by structural metric and/or study method. For example, when studies employed model selection, aspect was included in 91% of final stem density models, but only 65% of spectral models (Figure 48). Further, the effect of aspect differed by metric: densities were generally lower on southern aspects in field-based studies (Hoecker et al., 2020), whereas neutral effects were common in spectral recovery models (Wilson et al., 2021; Vanderhoof et al., 2021b) or non-significantly negative (Bright et al., 2019). Overall, the three groups of drivers most frequently considered and included in final models were aspect (92% of stem density models), elevation (92% of spectral models), and post-fire canopy (65% of seedling presence models). Other drivers, such as pre-fire composition, post-fire heat, and anomalously wet conditions post-fire, were generally important in models but are less commonly considered. 164 Most stem density and spectral models (>50%) that test pre-fire composition and site moisture included these drivers in final models. These two ecological drivers consistently positively impacted all recovery metrics (Figure 48). Conversely, drivers such as post-fire canopy (e.g., distance to the forest edge and remaining live basal area, Appendix B.3. Supplemental Table 3) skewed negative, with some examples of positive impacts. To provide an example of the variable impact of the post-fire canopy, Povak et al. (2020) found that distance-to-edge was negatively correlated with post-fire stem densities, but live post-fire basal area was positively correlated with the seedling densities of lodgepole pine, ponderosa pine, western larch, and Douglas-fir. The majority of stem density and seedling/sapling models included post-fire canopy (59% and 60%, respectively). Spectral methods both tested and included post-fire canopy less frequently (29% of models). When spectral models include post-fire canopy, relationships were often negative, including the relationship between spectral recovery and distance-to-edge (Bright et al., 2019), or patch size and patch area to perimeter ratio (Menick et al., 2024). In the category of Fire Impacts, spectral models generally included fire severity (82% of final models) instead of, or in addition to, post-fire canopy. Fire severity differs from post-fire canopy as it includes continuous or categorical measures and/or satellite data. Comparing spectral and field methods, a greater proportion of spectral models included fire severity (82% versus 49% of stem density models, respectively), but effects were neutral and/or variable (Figure 49). Bright et al., (2019) found a consistent negative relationship with fire severity and spectral recovery across forest types, while Shrestha et al. (2021) support a variable relationship by forest type. Across the western US, the spectral recovery of lodgepole pine forests was 165 negatively correlated with fire severity (Shrestha et al., 2021), but in field-based models, post-fire lodgepole pine stem densities were positively associated with fire severity (Coop et al., 2010; Hoecker & Turner, 2022; Turner et al., 1997). Overall, a greater proportion of field-based metrics positively correlated with fire severity. However, positive associations were not universal, even among models of the same species (Figure 50). For example, Douglas-fir stem densities were positively associated with fire severity in eastern Oregon (Downing et al., 2019) while fire severity was negatively associated with the presence of Douglas-fir seedlings in plots across the western US (Davis et al., 2019). 166 Figure 49. Counts of ecological drivers colored by associations with the metric of recovery (stem density, seedling presence, spectral) for interior dry conifer forests. Colors denote when driver association with recovery is negative (red) or positive (blue) for the recovery metric (note scales on the x-axis vary) with neutral (grey) offset. % is the percentage that the driver was tested and included in the final models. The y-axis labels are colored by driver Category (see legend to the left). 167 Figure 50. Counts of ecological drivers colored by associations with the metric of recovery for common conifer species (lodgepole pine, ponderosa pine, Douglas fir). Colors denote when driver association with recovery is negative (red) or positive (blue) for the recovery metric (note scales on the x-axis vary) with neutral (grey) offset. % is the percentage that the driver was tested and included in the final models. The y-axis labels are colored by driver Category (see legend to the left). 168 In some cases, the effect of drivers varied by forest type and/or region (Figure 50). Stem density positively correlates with fire severity, but correlations were often negative for Douglas-fir and ponderosa pine forests (Figure 50). Similarly, elevation had a variable impact on recovery by species and region. For Pacific Northwest conifer forests, Littlefield (2019) found that alpine species presence, such as Engelmann spruce and western larch, positively correlated with elevation, but elevation was negatively correlated with Douglas-fir and ponderosa pine. Across the northwest US region, Wilson et al. (2021) found that the effect of elevation varied by region. Elevation was the most important driver for spectral recovery for Douglas-fir forests in central Idaho, but in western Montana, post-fire precipitation anomalies had a greater relative impact (Wilson et al., 2021). 6.3.3.1 Objective 2 (a) relative effect of driver group on recovery by structural metric and/or approach Standardized models showed consistent positive impacts of pre-fire composition and post-fire moisture on recovery, but impacts varied between field and spectral studies (Figure 51). Pre-fire composition had a greater positive impact on stem density than spectral recovery (weighted effect of 0.32 versus 0.11 for spectral recovery). Conversely, post-fire moisture had a greater positive impact on spectral recovery (weighted effected of 0.41 versus 0.36). Droughting, soil composition, and post-fire understory negatively impacted stem densities. Many models included elevation and post-fire canopy, but their effect on coniferous recovery was variable. The weighted effect of drivers varied for stem density and spectral recovery models. For example, site-moisture had a consistent and positive influence on models 169 of spectral recovery but had little to no influence among the eight stem density models that included site-moisture (weighted ranking spectral = 0.32 versus weighted ranking stem density = 0.00). For some driver groups, such as wetting and post-fire heat, spectral recovery studies suggested a strong positive influence. However, none of the included studies based on field data included parameter estimates for wetting or post-fire heat. 170 Figure 51. Relative importance of drivers with parameters normalized by model and model accuracy between -1 to -1. Values of -1 or 1 describe a driver with the largest effect size or greatest importance in a model that captures 100% of the variability. Negative values have a negative effect and positive values have a positive impact on the associated conifer recovery metric. Driver groups are shown only when N>3. (A) Diamonds and bars are the median and interquartile ranges for spectral- and field-based approaches. (B) Boxes show interquartile ranges and median values for each driver group across all approaches. Boxes are colored by driver Category (see legend at right). A B 171 6.4 Overview of findings I systematically investigated the distinct early (<20 years) recovery pathways and associated ecological drivers after high-severity fire in dry conifer forests. I merged findings of different disciplines: field-based studies as well as studies that use spectral responses from satellite image time series data. Based on the included studies, distinct pathways identified by spectral and structural literature are generally similar and reflect those of prior syntheses (Baltzer et al., 2021; Stevens-Rumann et al., 2022). These pathways described distinct spatial patterns and temporal trends by species composition, forest structure, and establishment rate. However, as observing establishment rates requires repeat visitation, pathways of delayed recovery (i.e., where coniferous dominance was achieved, but could take >10 years) were more frequently identified by spectral studies. Overall, recovery pathways were best defined by studies with both pre-fire and multiple post-fire observations – namely, studies that used time series of spectral data. After synthesizing pathways, I investigated which ecological drivers impacted observed recovery patterns and trends. Recovery trends, such as the rate of spectral recovery, and recovery patterns, such as variability in stem density, were predominantly modeled using generalized linear models. Results support that number of ecological drivers included in the literature is diverse (more than 1000 unique drivers were tested across 58 studies, Figure 51). Importantly, more recent literature incorporated post-fire weather and climate conditions (Bright et al., 2019; Davis et al., 2023; Guz et al., 2021), addressing a limitation noted in the last systematic review of coniferous recovery (Stevens-Rumann & Morgan, 2019). Pre-fire conditions and site-level moisture are 172 positively associated with post-fire stem densities, seedling establishment, and spectral recovery. The relative importance of ecological drivers varied by method (spectral versus field-based), metric (stem density versus seedling presence, Figure 51), species (lodgepole pine versus Douglas-fir, Figure 50), and region (Oregon versus Montana). Further, spectral and field models focused on different drivers: spectral studies were more likely to include post-fire measures of climate conditions. In contrast, field studies were more likely to include post-fire measures of soil and understory condition. 6.4.1 Recovery pathways limited by knowledge gaps in pre-fire composition and post-fire growth trends Field-based and remote sensing scientists predominantly define recovery pathways by composition, including conifer self-replacement or state-shifts (where composition shifts to different tree species, shrubland, or grassland, Figure 47). Fewer studies described pathways of density change and/or temporal delay, likely because identifying these pathways requires estimates of pre-fire composition and multiple observations post-fire. Dendrochronological or field-based space-for-time approaches have identified delayed recovery, but validating these trends took >30 years post-fire (Stevens-Rumann et al., 2022). Additionally, pre-fire field data is rarely available, and studies seldom have the financial and logistical capacity for repeat sampling campaigns. As a result, structural change pathways are identified infrequently. Secondly, recovery models do not generally incorporate pre-fire conditions. Pre- and post-fire observations are available from satellite data; however, the accuracy of spectral data for characterizing the pre-fire conditions and post-fire trends is not well established. 173 Spectral observations pre-dating fire could provide estimates of pre-fire composition and structure. Field-based studies often estimate pre-fire structure in the field after fire, but high fire severity results in less structure to measure Harvey (Downing et al., 2019; Hansen & Turner, 2019; Harvey et al., 2013). This means pre-fire spectral observations are instrumental when the pre-forest stand is completely combusted. Vanderhoof & Hawbaker (2018) compared pre- and post-fire growing season values of NDVI, and suggest areas with increases in the magnitude of change between winter and summer NDVI may be areas with an increased deciduous composition. Given variability between spectral response and structural estimates, assessing the accuracy of pre-fire spectral observations to capture pre-fire conditions is also essential (Celebrezze et al., 2024; Ireland & Petropoulos, 2015; Wilson et al., 2021). Such research should also consider which pre-fire measures to quantify: identifying density changes requires pre- and post-fire density estimates, but both pre-fire density and cone abundance may impact conifer growth (Turner et al., 2019). Time series of post-fire field observations are rare. Thus, field-based approaches rarely quantified distinct temporal trends such as regrowth delay. High-quality spectral time series help identify distinct temporal trends because they monitor post-fire forest structure. Two studies that combined field-based patterns observed 15– 30 years after a fire with spectral responses of the early post-fire period (Menick et al., 2024; Smith-Tripp et al., 2024b). Both of these studies initially labeled slow rates of spectral recovery and later measured high stem densities in the field at these same locations as delayed coniferous establishment (Menick et al., 2024; Smith-Tripp et al., 2024b). However, neither of these studies validated temporal trends with time series from plots at the 174 same location. Such validation is likely important, as research in other ecozones suggests spectrally identified trends may not coincide with those identified in the field. For example, a study in the US Great Basin that aligned spectral and field-based trends of post-fire recovery found that spectral trends did not capture the field-observed trend of post-fire exotic vegetation encroachment (Barker et al., 2019). As reliance on spectral data for post-fire monitoring grows, it is important to investigate if spectral and field-based trends align. These investigations should also consider the spatial scale associated with trends – a region with slow-conifer establishment at the scale of a Landsat (900 m2) pixel could encompass smaller field plots with rapid conifer establishment. 6.4.2 Effect of recovery drivers varies by metric and method Recovery processes were most commonly associated with Fire Impacts, including post-fire canopy and/or fire severity (Figure 48). The second most frequently tested driver group of included studies was post-fire understory and soil measures, but no spectral studies tested understory or soil measures. Overall, the frequency at which a driver was tested and included in a model varied by metric (stem density versus composition) and method (spectral versus field). For example, post-fire canopy consistently negatively impacted post-fire stem densities, while fire severity better explained spectral responses associated with conifer recovery (Figure 51, Figure 48) Generally, site-level moisture positively impacted recovery metrics, moister sites recovered at faster rates with higher conifer densities (Brullsauer et al., 1996; Dodson & Root, 2013; Haire & McGarigal, 2008; Malone et al., 2018). The importance of Abiotic Site Conditions was consistent across stem density and spectral models; measures 175 such as the topographic wetness index (TWI) predominantly positively influence both stem densities and spectral recovery rates (Donato et al., 2016; Kiel & Turner, 2022; Littlefield, 2019). Comparatively, the relative impact of aspect differed by method: stem densities were consistently lower on southern aspects, but aspect was neutral for spectral recovery. Differences likely reflect data sources; a spectral investigation by Buma (2012) correlated in-field stem density with thresholds of NDVI and found that areas where NDVI corresponded to high-stem density did not correlate with aspect. Buma (2012) suggested that the influence of aspect on conifer recovery was muted because aspect is used when converting atmospheric reflectance to surface reflectance. Estimates of annual site warmth also differed between field and spectral methods. While annual site warmth negatively impacted post-fire stem densities of dry conifer forests in Washington (Povak et al., 2020), spectral recovery was positively associated with aspect across the western US (Shrestha et al., 2021). Spectral models highlighted some ecological drivers rarely considered by field-based studies. For example, field-based studies highlighted the negative impact of drought on post-fire seedling establishment (Davis et al., 2019, 2023) and stem density patterns (Clark-Wolf et al., 2022). However, spectral studies that consider both post-fire moisture and temperature suggest that post-fire temperature can have a larger impact on recovery than moisture (Bright et al., 2019; Guz et al., 2021). For example, Vanderhoof et al. (2021b) found winter NDVI recovery rates, which indicated conifer density, did not correlate with summer aridity, but cool summer temperatures had positive correlations. 176 6.4.3 Standardizing recovery drivers across spatial scales Spatial scale of measurement and analysis impacts research outcomes for both recovery pathways and associated ecological drivers. Yet, few studies test how spatial scale alters relationships between recovery metrics and ecological drivers. Peeler & Smithwick (2020) found that the spatial extent used to quantify seed source altered model accuracy. Specifically, models of sub-alpine fir regeneration density were less accurate when the distance to the seed source was quantified within a 50-100 m radius of the field plot. The importance of spatial scale may explain the variable effects of specific ecological drivers, such as shrub cover. In some studies, post-fire shrub cover had the greatest negative impact on post-fire regeneration (Littlefield et al., 2020), but other studies found a positive effect (Boag et al., 2020; Downing et al., 2019) or neutral effect (Andrus et al., 2021). The scale at which cover is quantified could mediate its impact as a recovery driver, as cover immediately around a plot may provide a microclimate conducive to seedling establishment (Downing et al., 2019), while extensive shrub cover throughout an area may out-compete coniferous growth (Laughlin et al., 2023). Future research could leverage spectral data to create spatially continuous estimates of ecological drivers, using estimates to investigate the spatial scale drivers, like shrub cover shift from being facilitative to competitive. Spatial scale and the other drivers included in models likely alter the observed effect of an ecological driver. Pre-selection of ecological drivers, such as post-fire drought, may obscure the impact of other drivers. In this investigation, droughting had the most consistent negative association with recovery metrics, while post-fire moisture had the strongest positive association. Post-fire drought is frequently attributed to 177 decreases in post-fire stem densities, failed seedling establishment, and decreased rates of spectral recovery (Braziunas et al., 2023; Guz et al., 2022; Rodman et al., 2020; Schapira et al., 2021). However, some studies that tested both temperature and moisture found a greater impact of post-fire temperature (Clark-Wolf et al., 2022). Further, when studies normalized precipitation to historical climate (i.e., tested “wetting”), the impact of moisture was more variable. This finding raises another critical question: Are there consistent and standardized sets of ecological drivers that should be incorporated into recovery models? For example, a standard set of climate variables, including climate anomalies, would improve knowledge synthesis efforts and thus understanding of recovery patterns. 6.4.4 Considering the limitations of selected studies The methods used to identify, select, and extract data from studies include limitations that must be considered in the context of the results. First, as I focused on research <20 years post-fire, recovery pathways identified in synthesis were skewed towards those identified by spectral studies. I acknowledge the extensive research using longer time series from dendrochronology or field-based space-for-time to understand recovery pathways (Brullsauer et al., 1996; Gendreau‐Berthiaume et al., 2018; Johnson & Fryer, 1989). The value of spectral studies is their capacity for post-fire monitoring amidst rapidly changing climate conditions. Yet, extracting and synthesizing findings of spectral studies can be challenging due to the diversity of approaches prevalent in the literature. Generalizing across studies of spectral monitoring of recovery pathways is challenged by diversity in conifer recovery metrics. While the challenge to define 178 recovery is not isolated to remote sensing (Baltzer et al., 2021; Stevens-Rumann et al., 2022). The variability of recovery definitions complicates the already challenging process of linking field observations to the spatial scale of spectral responses (Bartels et al., 2016; Frolking et al., 2009). For example, Celebrezze et al. (2024) found that the spectral recovery rate of grass was lower than shrubs and conifers. But, across the western US, Vanderhoof et al. (2021b) grouped grass and shrubs and found spectral recovery was higher than that of conifers. The different outcomes of spectral recovery rates among Vanderhoof et al. (2021b) and Celebrezze et al. (2024) underscore that the definition of recovery impacts study outcomes. Beyond challenges defining recovery, spectral monitoring for recovery pathways is also challenged by diverse satellite source data, image compositing methods, and growing season definitions. The diversity in processing means findings are not always generalizable. For example, faster rates and/or greater amplitudes of spectral recovery after fire are often linked to post-fire grass, forbs, or deciduous shrubs dominance over conifers (Mitchell & Yuan, 2010; Vanderhoof et al., 2018). Yet, this relationship is not universal; spectral recovery is slower in some grassy areas (Celebrezze et al., 2024). Additionally, different phenologies (both spatial and species-based) produce unique spectral responses that can highlight conifer dominance, but phenology differs regionally; peak conifer spectral response for Colorado is in June (Buma, 2012), whereas August is the peak spectral response in Arizona and New Mexico (Crockett & Hurteau, 2024). Variability in spectral phenology, amplitudes, and rates, could relate to different forest regions or types, but could also reflect variability in underlying data processing (Crockett & Hurteau, 2024; Wilson et al., 2021). Slow spectral recovery rates 179 identified as grass by Celebrezze et al (2024) used growing season NDVI and NBR. However, in the dry Western Cordillera, grass senesces (turns brown) in summer months, causing drops in NBR and NDVI reflectance. This senescence causes a lower growing season mean NDVI value compared to shrubs, deciduous, and conifer trees (Walker & Soulard, 2019). Thus, in grassy areas, NDVI compiled from mosaics of mean brightness would have lower rates of spectral recovery compared to mosaics compiled from maximum brightness values. In addition to the limitations of spectral studies, a large proportion of studies identified in the systematic review included linked datasets and were ultimately excluded from later syntheses. For example, the synthesis included Stevens‐Rumann et al. (2018). They used data from more than ten previously published studies to investigate the role of post-fire climate on post-fire stem densities. Studies that combined datasets could only test variables that were measured across all plots. As a result, they excluded post-fire understory vegetation and soil composition (Stevens‐Rumann et al., 2018). These understory and plot-level variables had large effects within the individual studies that were excluded based on data duplication (Rother & Veblen, 2016, 2017). Stevens‐Rumann et al. (2018) was selected over the other linked datasets because the spatial extent of their study better complemented the spatial scale of spectral studies. However, including the published studies linked to Stevens‐Rumann et al. (2018) may have increased the frequency of testing and relative importance of other drivers, such as post-fire shrub cover. For another example of such linked data, Davis et al. (2020) and Davis et al. (2019) both tested the impact of post-fire climate on seedling establishment. Davis et al. (2020) also tested the effect of seasonal climate and 180 identified an important “lagged” climate response, where the prior years' climatic conditions impacted establishment in the proceeding years. This same lagged response was also noted by (Littlefield et al., 2019, 2020). I included Davis et al. (2019) because a greater proportion of studies tested annual climate variables (comparably, Davis et al. (2020) tested seasonal climate variables), which improved the capacity for comparison. Across all studies, these linked datasets further underscore the need for a standard set of ecological drivers in recovery models, including post-fire understory, soil conditions, and a standard set of post-fire climate variables. 6.5 Future research priorities: key questions and recommendations This research synthesis framed key questions for future recovery investigations. Addressing these questions will improve the capacity to characterize recovery pathways and their associated impacts of ecological drivers across scales. Summary tables of both questions and recommendations are highlighted in Table 22 and Table 23. Following these recommendations will improve knowledge synthesis of recovery dynamics across field and spectral disciplines. Characterizing Recovery Pathways: Future research that uses spectral data to characterize recovery pathways must also determine how accurate both pre- and post-fire forest structure estimates are when derived from spectral data. Comparing recovery patterns and trends is difficult because metrics are inconsistent across both field and remote sensing-based studies. Spectral studies often combine in-field observation and spectral data to develop binary spectral thresholds for areas of ‘high’ and ‘low’ post-fire stem density. However, the magnitude of these spectral responses will vary based on pre-processing approaches and study area (Buma, 2012; Kiel & Turner, 2022). Instead 181 of classification, spectral data could provide continuous estimates of forest structure, such as pre-fire basal area (White et al., 2023) or post-fire canopy cover (Menick et al., 2024). Yet, are these pre-fire spectral estimates of recovery metrics such as basal area or overall productivity (Vanderhoof et al., 2018) accurate enough to characterize areas where forest composition or structure change as a result of fire? Secondarily, after a fire, are unique trends in vegetation consistent between field and spectral approaches? (Table 22). Synthesizing the outcomes of studies that test these questions requires establishing more consistent pre-processing and compositing approaches across spectral studies. Recommendations for characterizing recovery pathways: A first step for establishing spectral data to estimate pre- and post-fire forest structure is the use of consistent source satellite data such as Landsat ARD-1 (Dwyer et al., 2018) or the more recent harmonized Landsat-Sentinel dataset (Claverie et al., 2018). These sources correct for atmospheric and sensor effects to produce consistent surface reflectance measures required for space and time analyses (Song & Kim, 2001). For estimating forest structure with spectral data, data processing should highlight the spectral phenology response, including time-series analyses based on the amplitude of change between winter and summer spectral indices (Menick et al., 2024; Shrestha et al., 2024; Vanderhoof & Hawbaker, 2018). For estimating forest structure, one could leverage the spatially complete structural estimates of lidar data to circumvent the often-mismatching spatial resolutions between field plots and satellite pixels (Senf, 2022). For example, Menick et al., (2024) used lidar data estimates across a large region of the US northwest to model temporal trends in post-fire canopy cover, validating their model with 182 publicly available field plot data such as the US forest inventory plots (Wiener et al., 2021). In boreal forests, airborne lidar helps characterize post-fire recovery (Bolton et al., 2015) and validates spectral models of recovery (White et al., 2022; White et al., 2018). While the accuracy of lidar-satellite imputed forest estimates can vary (Hermosilla et al., 2024), temporally and spatially complete remote sensing data layers can contextualize field observations. Further, repeat lidar acquisitions can help bound the accuracy of data layers (Montesano et al., 2023). These spatially and temporally continuous recovery metrics can then be linked to ecological drivers, which can operate at different spatial scales (Davis et al., 2020). Quantifying Ecological Drivers: Fusing field and spectral approaches helps bridge spatial and temporal scales of both recovery pathways and ecological drivers. Across all studies, landscape-level drivers such as post-fire climate conditions had the strongest impact in recovery models. However, I did not investigate whether the spatial scale of the study impacted observed outcomes. Are measures of post-fire climate the most important drivers across scales? Or, are estimates of post-fire canopy more important at a local level? An essential question to address alongside these research questions is which drivers are important to consider in models of recovery across scales? Finally, as drivers such as climate anomalies will differ based on the definitions of “normal” climate, these investigations should also consider the best, or at least consistent, methods of deriving ecological drivers (Table 22). Recommendations for quantifying ecological drivers: Acknowledging the difficulty of questions around ecological drivers, future investigations should start with a driver with existing standardization efforts across scales and methods. One good example is 183 soil burn severity. Few studies, field or spectral, consider metrics of soil burn severity, even though soil burn severity mediates resprouting of post-fire shrubs and deciduous trees (Baltzer et al., 2021; Pausas & Keeley, 2021). Typical satellite-based estimates of burn severity, such as dNBR or RdNBR, poorly capture the spatial variability or depth of soil burn (Kasischke et al., 2008). Recent efforts underscore that integrating additional landscape estimates, such as topography and surrounding vegetation, could improve the accuracy of soil burn severity derived from both field and spectral approaches (Wilson & Prentice, 2024). The soil depth a wildfire burns to depends on the weather during the fire event, slope, aspect, and vegetation, which can all be estimated from remote sensing datasets (Seydi et al., 2024). As a first step to answer the above questions, future work could test how improved spatially continuous soil burn severity maps alter conclusions across scales: local to landscape and across methods: field or remote sensing. 184 Table 22. Key outstanding research questions associated with each review objective. Review Objective Key Outstanding Research Questions Framed by Review Characterizing Forest Recovery Pathways What pre-fire metrics are needed to characterize and compare pre- and post-fire conditions? What accuracy is sufficient for comparison? Are pre-fire estimates of forest composition and structure sufficiently accurate to characterize areas where forest composition and structure change as a result of fire? Is there consistent alignment between the field and the spectrally observed trends of post-fire vegetation development? How do methodological inconsistencies in data processing influence the assessment of recovery outcomes? For spectral methods, how do the techniques of pre-processing data, the selected spectral index, and the method to identify trends influence recovery measures? Quantifying Ecological Drivers of Forest Recovery How does spatial scale alter the association between recovery metrics and ecological drivers? What ecological drivers are important to consider in models of recovery across spatial scales? How can we standardize approaches to derive and report ecological drivers? 185 Table 23. Considerations and recommendations for future research to enable synthesis and meta-analysis. Component of Study Considerations and Recommendations Spectral Data Processing Methods to process spectral data must be consistent in data source (such as analysis-ready datasets) and metrics measured. Measures of spectral change between summer and winter help capture composition. Structural estimates based on models of lidar-satellite fusion help capture structure and facilitate upscaling field observations. Measuring Ecological Drivers Methods to assess post-fire conditions, such as canopy, understory, and soil, must be standardized. Climate anomalies must be explicit in definitions of a "normal" climate and climate extremes (e.g., heat events, drought). Measurement accuracy should consider the impact of changing spatial scale. Collaborations between field and remote sensing scientists will improve understanding of ecological drivers across spatiotemporal scales. Study Reporting Reporting of the method to measure recovery metric and drivers must be consistent to enable synthesis. Model statistics, including accuracy and standard errors, must be explicit to enable meta-analysis. 186 6.6 Conclusions This systematic review synthesized observed pathways of forest recovery and associated ecological drivers in dry forests of the Western Cordillera. Studies included in the review were field and spectrally based, with both forms of inquiry concluding that self-replacement was the most commonly observed pathway. Alternative pathways were primarily shifted in vegetation composition. As the scale of fire disturbance has increased, remote sensing methods are increasingly relied on to identify distinctive pathways. Unfortunately, the diversity of recovery definitions and underlying spectral responses means identifying these distinct recovery pathways is difficult. Yet, remote sensing plays an essential role in identifying pre-fire conditions and monitoring post-fire temporal trends. However, to synthesize the value of remote sensing as a bridge between site-level patterns and landscape-level trends, processing satellite data must be standardized, and the accuracy of structural forest estimates from spectral data must be better quantified. Spectrally identified trends and patterns facilitate identifying recovery pathways early on post-fire across large regions. Similarly, incorporating spatially continuous estimates that fuse spectral and field observations helps understand how underlying ecological drivers vary across scales. Overall, this research synthesis, combining spectral and field methods, improves prediction and management of post-fire ecosystem changes across western North America. 187 Conclusions 7.1 Research objectives The overall objective of this dissertation was to capture forest recovery pathways across the expansive burned landscapes of BC and to understand how ecological drivers influence these pathways. Four research questions (RQs) addressed individual research objectives outlined in Chapter 1. Table 24 restates these RQs and summarizes key takeaways by chapter. 7.2 Research synopsis This thesis demonstrates that long-term spectral data aids monitoring recovery after fire in interior British Columbia (BC) and the broader region of western North America. Results also emphasize the significant influence of post-fire climate conditions on recovery patterns and trends. In this synopsis section, I summarize the key takeaways from each research question (RQ) and how these insights inform future research and management practices. In RQ1, I supported the value of RPA lidar data to sample forest structure at spatial scales coincident with satellite data. Secondarily, I found spectral trends could delineate areas of distinct structural recovery. These findings are important for forest managers, as they indicate that RPA lidar can effectively sample regions of interest and that these samples can subsequently link to spectral responses. This process helps locate areas of similar structural recovery across the landscape. RQ2 built upon the use of satellite data to differentiate recovery, demonstrating that early spectral responses can help identify longer-term structural recovery. The findings of RQ2 support that there is generally strong post-fire conifer recovery across BC; however, 188 some regions experience slow conifer establishment or high deciduous ingrowth. The findings and methodologies from RQ2 help isolate distinct recovery for further research or management action by forest researchers and managers. RQ3 explores ecological drivers of variable recovery, supporting that variability in recovery across the region was mainly related to post-fire climate conditions. Thus, while early spectral responses can identify distinct recovery (RQ2), managers can also prepare for likely responses based on post-fire climate conditions (RQ3). Collectively, these research findings support the development of an adaptable monitoring and comprehensive management framework for BC. RQ4 extends the findings of RQ1-RQ3 beyond BC, supporting the unique capability of spectral data to monitor temporal trends of recovery. The research synthesis of RQ4 also confirmed a limitation of RQ3: spectral monitoring cannot capture some important ecological drivers, such as soil burn severity. While combining lidar and satellite data is crucial for spatially and temporally continuous landscape-scale monitoring, in-field validation remains needed. When prioritizing areas for post-fire research, monitoring, or management, both managers and researchers must account for the site-level factors that may be omitted by landscape-level monitoring. Landscape-level monitoring, which combines lidar and satellite, can isolate areas of structurally distinct recovery alongside important ecological drivers. The distinct structural recovery groupings identified from the methods developed within this dissertation can help stratify future study design and/or help managers prioritize management actions that maintain key ecosystem services. 189 Table 24. Questions and key findings associated with research dissertation. Question Key Findings RQ 1 What is the relationship between satellite spectral trends and distinct expressions of post-fire structure in a dry conifer forest? • Landsat spectral clusters capture recovery patterns relevant for silviculture at spatial scales difficult to capture with field-based measures alone. • For most indices, spectral recovery rates were lower in areas with a higher proportion of deciduous trees, but overall spectral reflectance was higher. Deciduous regions had higher rates of TCB spectral recovery. • Combining spectral indices improved characterization of compositional and structural differences. RQ 2 Are unique spectral trends of recovery indicative of longer-term forest structural recovery? • Higher rates of spectral recovery did not consistently correlate with areas of higher deciduous regrowth • Spectral trends identified five years post-fire indicated longer-term patterns and trends of structural recovery. • Two forest recovery pathways were spatially dominant in the study area. The differences were mainly compositional (conifer vs. deciduous). • Early spectral trends effectively identified longer-term recovery (up to the first 20 years). However, inconsistent sampling of spectral trends highlighted limitations of space-for-time sampling RQ 3 Which ecosystem drivers are associated with different recovery pathways? • Cold and dry climate anomalies following a fire significantly influenced the pathways of structural recovery, generally delaying conifer establishment and growth. • Higher deciduous composition was associated with a greater pre-fire basal area • Across the landscape, site-specific conditions, such as aspect and elevation, played a minimal role in structural recovery groupings. RQ 4 What drives pathways of early recovery in coniferous dominant forests of the western cordillera? • Shifts in composition, such as conifer to grass, are the most commonly identified recovery pathways. Spectral methods primarily identify these pathways. • Synthesizing recovery pathways and associated drivers is limited by (a) how satellite data is processed, (b) how recovery is defined, (c) the choice and consistency of ecological drivers, and (d) how studies report their findings. • Spectral and field studies highlight different ecological drivers. Spectral studies highlight broader landscape influences, such as post-fire climate. In contrast, field studies highlight specific site conditions, including canopy cover. 190 7.3 Research innovations and significance The findings of this dissertation provide practical innovations to track and characterize post-fire structural forest recovery over time and at large spatial scales (~ half a million hectares) using satellite data. The following section describes these innovations and their significance for forest management and future ecological research. 7.3.1 Cost-efficient RPA lidar can derive estimates of recovering forest structure over space-and-time RQ1 investigated the capacity of cost-efficient RPA lidar to derive silvicultural recovery metrics in a recovering forest. This investigation demonstrated the value of low-cost RPA lidar for estimating forest structure metrics within a highly diverse recovering forest. Specifically, for RQ1, I flew three different locations with RPA lidar and estimated structure from lidar metrics and field validation plots. While approaches to generating forest structure from airborne lidar are relatively established (White et al., 2017a), methods using RPA-lidar are still in their infancy (Goodbody et al., 2018). RQ1 presents a novel approach to model forest silvicultural metrics, including BA, stem counts, and composition. In RQ2 and RQ3, I further validated this approach and supported areas flown at different ages post-fire can characterize the often highly heterogeneous recovering forest structure across both space and time. As the spatial extent of lidar data continues to expand (White et al., 2025), so do possible collaborations to integrate historic opportunistic field samples as ground verification of spectral trends from one time (the original field sample) to the next (the later lidar acquisition). 191 7.3.2 Spectral recovery can identify structurally distinct forest recovery The finding that spectral responses indicated recovering structure and composition is pivotal for monitoring and managing areas recovering from fires that burn hundreds of thousands of hectares. Spectral trends indicated unique trends of spectral recovery. In RQ1, areas that had unique spectral recovery trends 1-16 years post-fire aligned with regions distinct in basal area, composition, and stem density. Thus, spectral reflectance captured by satellite can assess post-fire forest recovering structure (stem counts, basal area, and composition) at spatial scales previously challenging to acquire without extensive field-based measures (White et al., 2023). 7.3.3 Early spectral trends infer longer-term structural recovery Identifying likely structural recovery early on in the post-fire period enables forest managers to plan and prioritize areas for management actions (Larson et al., 2022; North et al., 2019). RQ2 and RQ3 employed a novel methodology that fused satellite and space-for-time RPA lidar data. Data fusion pragmatically identified unique recovery at early time steps and linked it to longer-term structural recovery. This methodological innovation addresses a critical need for continuous, broad-scale post-fire monitoring (Bourgeois et al., 2018). By quantifying the relationships between early post-disturbance spectral responses and structure, the approach also identifies forest structural attributes at a critical recovery juncture. Thus, the methods developed in RQ2 and RQ3 enable strategic management actions (planting or harvesting) early on post-fire (Hessburg et al., 2021; Pettorelli et al., 2018), while contributing to more comprehensive landscape-level forest management (Bettinger et al., 2017). 192 7.3.4 Post-fire climate alters structural recovery – demanding iterative management The importance of post-fire climate is a key takeaway for managers, as it implies that post-fire actions must adapt to the climate events that follow the fire. Frameworks for post-fire management suggest actions should be based on fire severity as well as historical and predicted site conditions (Larson et al., 2022; North et al., 2019). In RQ3, post-fire climate had the most significant impact on structural groupings, specifically, areas that were slow to establish had cold and/or dry summers. Using the approach of RQ2 and the findings of RQ3, forest managers can (a) leverage post-fire spectral response to characterize likely recovery and (b) assess the need for management action from likely recovery and climate conditions. For example, in RQ3, the regrowth delay recovery pathway had the highest bare ground 8-9 years post-fire. Particularly cold or dry summers increased the likelihood of regrowth delay. Theoretically, managers can prioritize management action in areas with dry cold summers. RQ3 methods and findings help us understand what is growing and where, combined with knowledge of why it might be growing there (Seidl et al., 2014). 7.3.5 Understanding forest recovery at scales benefits from satellite and field methods The systematic review of post-fire conifer recovery employed an innovative approach to compare studies that used different data and modeling approaches. Ecological syntheses often struggle to compile and understand the effects across studies, which can hinder the rate of research synthesis (Makowski et al., 2023). RQ4 overcame this difficulty by ranking the overall impact of ecological drivers within models 193 and then linking power ranking to the variability captured by the model. This form of meta-analysis enables the synthesis of findings across diverse spectral and field-based methods. Meta-analyses by agricultural researchers have combined spectral and field studies (Kuyah et al., 2019), but combining these methods in ecological recovery syntheses is novel. My novel approach of RQ4 highlighted the importance of post-fire climate on conifer recovery at a landscape scale. Results also underscored that the approach (field vs. spectral) may alter a specific study's findings. This development in meta-analytical methods is pivotal as we continue to monitor and model forest metrics (both disturbed and undisturbed) with satellite data. My meta-analytical method applies to literature syntheses more broadly, as syntheses are often limited by the diversity of study approaches (Burford et al., 2013). 7.4 Research limitations and future directions It is essential to discuss the limitations of the methods employed in this dissertation. These limitations also frame future research priorities, as research addressing these limitations will improve characterization and understanding of structural recovery across the Western Cordillera. 7.4.1 Defining high-severity study area 7.4.1.1 Limitation High-severity burn areas were identified from a spectral dNBR threshold used by the BC Ministry of Forest (BC Ministry of Forests, 2023a). While dNBR is widely used to stratify burned severity into different severity classes (Picotte et al., 2020), dNBR’s accuracy varies across space and time and generally requires ground validation (Chen et al., 2011). Across the US, burn severity estimated from dNBR, adjusted with field 194 data, only moderately correlated with field-based estimates of burn severity (Picotte et al., 2020). Yet, geographically weighting models with local field samples improved correlations up to 25% (Picotte et al., 2020). British Columbia started adjusting satellite burn severity with stratified aerial samples in 2015 (BC Ministry of Forests, 2021). Burn severity adjustments are not available for fires before 2015. As a result, the dNBR threshold that defined high-severity of my study regions may not always align with high field severity. In addition to the mismatch between field and satellite burn severity, satellite data poorly capture the spatial variability and depth of soil burn severity (Kasischke et al., 2008). Soil burn severity may be an important consideration, as it influences the resprouting of post-fire shrubs and deciduous trees (Pausas and Keeley 2017; Baltzer et al., 2021). In the United States, burned area classification (BARC) products adjust satellite-derived severity using in-field observations. The accuracy of the BARC products decreases as the distance from in-field samples increases (Picotte et al., 2020). Further, British Columbia does not have a BARC protocol, which means there is no validation dataset for soil burn severity. 7.4.1.2 Future research To better understand the accuracy of the burn severity within the study area, future research should investigate the overall accuracy of static dNBR thresholds across years within British Columbia. In this case, investigations could ask if the dNBR threshold for high-severity accurately represents high-severity burns in different years (e.g. 2023 – 2025). In addition to this year-to-year comparison, future research could also investigate how integrating additional variables, such as topography and surrounding vegetation, 195 could facilitate the estimation of soil burn severity from satellite data (Wilson & Prentice, 2024). This investigation of soil burn severity requires establishing a consistent post-fire soil monitoring program – an objective currently underway (BC Wildfire Service, 2024). Overall, more accurate estimates of overall fire severity better constrain areas of high-severity fire and improve knowledge of post-fire conditions. 7.4.2 Sampling spectral responses comprehensively 7.4.2.1 Limitation Comprehensive sampling of unique spectral responses with space-for-time RPAS data was difficult. As a result, RQ2 concentrated research findings on two out of five recovery pathways (emergent conifer and mixed forest regrowth) but limited discussions of other pathways. Adding 500 ha of lidar data to the investigations in RQ3 provided a better understanding of the variability in structural recovery pathways; however, sampling was still unequal across years and spectral clusters. The complications of space-for-time sampling meant I did not test the study's external bias (Lovell et al., 2023). The statistical differences reported in RQ2 and RQ3 are internally valid, meaning they are representative of the observed structure and spectral variability within the study. However, I did not estimate external validity, or the risk of bias when extrapolating structural groupings beyond the study region and period (Boyd et al., 2022). Testing the risk of bias is crucial to ensure that my approach and findings can be effectively extrapolated to other areas and periods (Burford et al., 2013). To briefly illustrate this point, consider the commonly cited outcome that deciduous dominant areas will have a more rapid spectral recovery (White et al., 2023). In RQ2 and RQ3, areas with a higher deciduous component did not necessarily have a higher 196 rate of spectral recovery – areas that grew more coniferous in later years also had rapid TCW recovery. Similarly, Celebrezze et al (2024) found rapid spectral recovery did not distinguish shrubs from coniferous growth, but did isolate regions of grass. Findings from both RQ2 and RQ3, as well as those of Celebrezze et al (2024)., underscore that spectral recovery rates may differ based on the metric measured (grass vs. shrub) and region (Pacific Northwest vs. interior BC). To upscale the findings of this thesis to other areas, there is a need to assess the risk of geographic and time-based bias. 7.4.2.2 Future research Future research should conduct a bias assessment to ensure equal sampling of structure and associated spectral data. Bias assessments are uncommon in ecological literature; however, recent tools, such as the "Risk-Of-Bias in studies of Temporal Trends in ecology" (ROBITT), enable bias estimate reporting (Boyd et al., 2022). There are three main bias types in ecological studies: geographic bias, environmental bias, and bias related to community composition and/or structure (Boyd et al., 2022). A geographic bias assessment tests if the geographic variability of the structural groupings is consistent across time and space. An environmental bias assessment determines whether the variability of the structural groupings remains equal across time and environmental factors. Finally, a community bias assessment tests for consistent representation of structural groupings throughout the study period (Boyd et al., 2022). Bias assessments can identify potential latent variables that should be measured and/or used to weight samples, thereby improving external validity. For example, Boyd et al. (2024) compared populations trends for a UK shrub species based on community reporting and true population estimates (derived from spatially continuous satellite 197 data). They found that community-reported data underestimated the decreasing population trend in the selected shrub species. However, when Boyd et al. (2024) weighted community-reported data based on two latent variables, distance to protected areas and elevation, weighted population trends better aligned with true population trends. Similarly, considering the underlying factors that cause a forested area to burn and be sampled clarifies whether and how to weigh forest structure samples to mitigate geographic, environmental, and community biases. Incorporating underlying factors in structural groupings of RQ2 and ecological modeling of RQ3 would improve model transferability. A highly transferable model can estimate structural recovery in more recent fires (after 2017 – the end of the study period) and in other locations (Lovell et al., 2023). 7.4.3 Accuracy of lidar structural estimates 7.4.3.1 Limitation Building accurate models of recovery forest structure by combining lidar metrics and field plots had a range of accuracies (R²= 0.41 to 0.95). BA models had the highest model accuracies, while estimates of coniferous proportion had the lowest model accuracies. Notably, these model accuracy estimates assume that lidar metrics of field plots represent the variability of the entire flight area. I attempted to consider this assumption by using parameters derived with k-fold validation. However, independent field plots for validation would have improved certainty in the model accuracy estimates. Model accuracy estimates are also not incorporated into the PERMANOVA results, which informed the structural groupings. Considering the accuracy of metrics, such as composition, is important when comparing structural recovery pathways. Pathways 198 identified by RQ2 and RQ3 accentuated differences in composition. Compositional estimates between pathways were larger and/or more consistent than the differences between stem-density and BA metrics. For example, in RQ2, composition was significantly different between emergent conifer and mixed forest regrowth in four out of the five years with structural data, while BA was significantly different in three out of the four years. Yet, differences do not incorporate the underlying uncertainty in estimates of composition. 7.4.3.2 Future research The structure of recovering forests is complex, particularly in the early post-fire period (Bolton et al., 2015). Generally, the accuracy of lidar for estimating forest structure decreases with increasing forest complexity (White et al., 2025). The overall accuracy of RPA models highlights two future research needs: (1) adequate post-fire lidar and field plots for validating structural estimates and (2) propagating uncertainty of structural recovery estimates associated with spectral responses. Airborne lidar data offers additional sources for validating structural estimates. Fortunately, the BC government aims to have processed and published a complete provincial lidar acquisition by 2030 (White et al., 2025). Furthermore, much of this provincial lidar data will be acquired between 2020 and 2030, flying over a large proportion of areas now recovering from the 2017, 2018, 2021, 2024, and 2024 record wildfire seasons. The more recent lidar data is beneficial because it captures the highly dynamic first 1-10 years of structural recovery after the major fire seasons. With more lidar data, the detection of post-burn canopy, as well as recovery, can be better 199 modeled and accurately benchmarked to validation datasets, robustly estimating structure and associated uncertainty (Pletcher et al., 2024). Robust and spatially continuous estimates of forest structure from wide-scale lidar will also help understand uncertainty in structural estimates of recovery pathways. Recent efforts have leveraged the increasing availability of lidar data to create Canada-wide annual estimates of key forest metrics, including species composition (Hermosilla et al., 2024).This is an alternative to grouping structural recovery which instead fuses lidar and satellite data to create spatially and temporally continuous estimates of a single structural metric as well as the underlying accuracy of estimate. RQ2 and RQ3 structural recovery groups did not propagate the underlying uncertainty of lidar structural estimates. Communicating underlying model uncertainty for these metrics, including the accuracy by species or region, is critical component for increasing research transparency and stakeholder confidence (Bhatt et al., 2021). As highlighted in this thesis, the accuracy of lidar structural estimates can vary. Robust field plot networks and spatially complete estimates of forest structure can be used to quantify and communicate uncertainty in structural estimates. 7.5 Closing statement Continued changes to climate and fire regimes mean that global forests will burn and recover from fires that are larger and more severe than historical norms (Jones et al., 2024). Cohesive forest management and climate adaptation rely on knowing the current and likely future forest structure across these vast burned extents (Hoffman et al., 2022). This dissertation showcased the power of combining RPA lidar data with optical time series data to characterize recovering forest structure. Further, work 200 demonstrated that spatially extensive forest estimates from RPA lidar can link unique spectral recovery and distinct structural recovery across a large post-fire landscape. Overall, the work addressed a critical need for dynamic and consistent post-fire forest assessment across the vast remote sub-boreal region of British Columbia, a region with a historical deficit of post-fire monitoring. Of significance for BC forest managers, cold and dry post-fire climate can negatively impact conifer establishment and growth, resulting in delayed establishment and shifts in post-fire composition and structure. Space-for-time substitution with RPAs' lidar and satellite data offers advantages and disadvantages for capturing structural recovery over large areas. Spectral methods provide the distinct advantage of capturing trends and patterns of growth, thereby better classifying pathways. However, spectral methods require consistent processing methods and ground validation. As a whole, my work posits that gaining landscape-level insights into post-fire recovery must leverage the value of combining field, lidar, and satellite data. 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(2021). mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation. https://CRAN.R-project.org/package=mgcv Woodcock, C., Allen, R., Anderson, M., Binschadler, R., Cohen, W., Gao, F., Godward, S., & Hedler, D. (2008). Free Access to Landsat Imagery. Science, 320. Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., … Cook, B. D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 113195. https://doi.org/10.1016/j.rse.2022.113195 222 Wulder, M. A., White, J. C., Gillis, M. D., Walsworth, N., Hansen, M. C., & Potapov, P. (2010). Multiscale satellite and spatial information and analysis framework in support of a large-area forest monitoring and inventory update. Environmental Monitoring and Assessment, 170(1), 417–433. https://doi.org/10.1007/s10661-009-1243-8 Ye, S., Rogan, J., Zhu, Z., Hawbaker, T. J., Hart, S. J., Andrus, R. A., Meddens, A. J. H., Hicke, J. A., Eastman, J. R., & Kulakowski, D. (2021). Detecting subtle change from dense Landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance. Remote Sensing of Environment, 263, 112560. https://doi.org/10.1016/j.rse.2021.112560 Young, D. J. N., Werner, C. M., Welch, K. R., Young, T. P., Safford, H. D., & Latimer, A. M. (2019). Post-fire forest regeneration shows limited climate tracking and potential for drought-induced type conversion. Ecology, 100(2), e02571. https://doi.org/10.1002/ecy.2571 Young, N. E., Anderson, R. S., Chignell, S. M., Vorster, A. G., Lawrence, R., & Evangelista, P. H. (2017). A survival guide to Landsat preprocessing. Ecology, 98(4), 920–932. https://doi.org/10.1002/ecy.1730 Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A. H., Cohen, W. B., Qiu, S., & Zhou, C. (2020). Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment, 238, 111116. https://doi.org/10.1016/j.rse.2019.03.009 Automatic citation updates are disabled. To see the bibliography, click Refresh in the Zotero tab. 223 Appendices Appendix A Systematic Review Procedure Description: The accompanying procedure was developed prior to the systematic review of research question 4. For completeness, I include the entire review protocol which follows the RepOrting standards for Systematic Evidence Syntheses. A.1 Description of Review Protocol: The following document details a systematic review protocol formulated according to the Collaboration for Environmental Evidence (CEE) systematic literature review standards (Pullin et al., 2024). Specifically, the document is structured according the most up-to-date RepOrting standards for Systematic Evidence Syntheses in environmental research (ROSES) structure. Resources of CEE standards: Collaboration for Environmental Evidence. 2022. Guidelines and Standards for Evidence synthesis in Environmental Management. Version 5.1 (AS Pullin, GK Frampton, B Livoreil & G Petrokofsky, Eds) www.environmentalevidence.org/information-for-authors. October 10, 2024 A.2 Objective of Review Analyses consider the impact of ecological factors on the rate of coniferous recovery in interior dry coniferous forests. The review a narrative synthesis that merges a literature disparate in discipline (remote-sensing or plot-based approaches) and/or geographic location (US or Canada based). a. Definitions of the Question 224 Question: What factors determine rates or patterns coniferous establishment after high-severity wildfire in unplanted dry coniferous forests in Western North America? Sample: Post-fire interior dry Coniferous forests of Western North America Phenomenon of Interest: Trends of Recovery (including spectral or structural trends or variable patterns) Design: Spectral and/or field based post-fire monitoring Evaluation: Drivers of Recovery Research Type: Quantitative measures and/or semi-qualitative descriptor A.3 Methods a. Search i. Search Strategy The search query was built and tested in SCOPUS and then expanded the search to Web of Science Core Collection, AESD, and technical reports from both Canadian and US institutions (Table A1). Studies included those identified in past literature reviews and literature articles using snowball searching (Page et al., 2021). Search queries did not impose date cut-offs or geographic limitations. Grey literature searches used simplified search terms because of limited bibliographic information. Search terms to capture articles within the SPIDER framework. To ensure the comprehensiveness of the bibliographic database search, the search query was tested against articles considered critical to include in the review. These benchmarking articles are selected by the reviewers as well as experts in the field. 225 ii. Search String The search query applied in SCOPUS was as follows: ((TITLE-ABS-KEY ("Coniferous Forest" OR "Conifer*" OR "Lodgepole" OR "Douglas-fir" OR "Ponderosa Pine" OR "Mixed Conifer" OR "Subalpine" OR "sub-alpine" OR "British Columbia") AND TITLE-ABS-KEY("postfire" OR "post-fire" OR "post fire" OR "after fire" OR "burn" OR "burned" OR "burnt" OR "post-wildfire" OR "post wildfire" OR "time since fire") AND TITLE-ABS-KEY("Pattern*" OR "Group*" OR "Trend*"OR "Type*" OR "Dynamic*" OR "Class*" OR "State*" OR "pathway*" OR "growth") AND AND TITLE-ABS-KEY("recovery" OR "regeneration" OR "establishment" OR "recruitment" OR "succession" OR "chronosequence" OR "Resilience" OR "Trajector*" OR "restablish*") AND TITLE-ABS-KEY("tree" OR "vegetation" OR "forest" OR "stand" OR "seedling" OR "cover" OR "stem" OR "Basal Area" OR "biomass") AND TITLE-ABS-KEY("Abiotic Conditions" OR "climate" OR "topography" OR "severity" OR "environment" OR "competition" OR "soil"))) AND (LIMIT-TO (PUBSTAGE, "final") OR LIMIT-TO (PUBSTAGE, "aip")) AND (LIMIT-TO (LANGUAGE, "English")) i. Estimating the comprehensiveness To ensure the comprehensiveness of the bibliographic database search we tested the search using “benchmark articles” or articles considered critical to include in the review. Benchmarking articles are selected by the reviewers as well as experts in the field. The test articles are included below: Bright, B.C., Hudak, A.T., Kennedy, R.E., Braaten, J.D., Khalyani, A.H., 2019. Examining post-fire vegetation recovery with Landsat time series analysis in three western North American forest types. Fire Ecol. 15, UNSP 8. https://doi.org/10.1186/s42408-018-0021-9 Clason, A.J., Farnell, I., Lilles, E.B., 2022. Carbon 5–60 Years After Fire: Planting Trees Does Not Compensate for Losses in Dead Wood Stores. Frontiers in Forests and Global Change 5. Davis, K.T., Robles, M.D., Kemp, K.B., Higuera, P.E., Chapman, T., Metlen, K.L., Peeler, J.L., Rodman, K.C., Woolley, T., Addington, R.N., Buma, B.J., Cansler, C.A., Case, M.J., Collins, B.M., Coop, J.D., Dobrowski, S.Z., Gill, N.S., Haffey, C., Harris, L.B., Harvey, B.J., Haugo, R.D., Hurteau, M.D., Kulakowski, D., Littlefield, C.E., McCauley, L.A., Povak, N., Shive, K.L., Smith, E., Stevens, J.T., Stevens-Rumann, C.S., Taylor, A.H., Tepley, A.J., Young, D.J.N., Andrus, R.A., Battaglia, M.A., Berkey, J.K., Busby, S.U., Carlson, A.R., Chambers, M.E., Dodson, E.K., Donato, D.C., Downing, W.M., Fornwalt, P.J., Halofsky, J.S., Hoffman, A., Holz, A., Iniguez, J.M., Krawchuk, M.A., Kreider, M.R., Larson, A.J., Meigs, G.W., Roccaforte, J.P., Rother, M.T., Safford, H., Schaedel, M., Sibold, J.S., Singleton, M.P., Turner, M.G., Urza, A.K., Clark-Wolf, K.D., Yocom, L., Fontaine, J.B., Campbell, J.L., 2023. Reduced fire 226 severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States. Proceedings of the National Academy of Sciences 120, e2208120120. https://doi.org/10.1073/pnas.2208120120 DeLong, S.C., Arocena, J.M., Massicotte, H.B., 2003. Structural characteristics of wet montane forests in east-central British Columbia. The Forestry Chronicle 79, 342–351. https://doi.org/10.5558/tfc79342-2 Guz, J., Gill, N.S., Kulakowski, D., 2021. Long-term empirical evidence shows post-disturbance climate controls post-fire regeneration. Journal of Ecology 109, 4007–4024. https://doi.org/10.1111/1365-2745.13771 Harvey, B.J., Donato, D.C., Romme, W.H., Turner, M.G., 2013. Influence of recent bark beetle outbreak on fire severity and postfire tree regeneration in montane Douglas-fir forests. ECOLOGY 94, 2475–2486. https://doi.org/10.1890/13-0188.1 Harvey, B.J., Donato, D.C., Turner, M.G., 2016. High and dry: post-fire tree seedling establishment in subalpine forests decreases with post-fire drought and large stand-replacing burn patches. Global Ecology and Biogeography 25, 655–669. https://doi.org/10.1111/geb.12443 Hoecker, T.J., Hansen, W.D., Turner, M.G., 2020. Topographic position amplifies consequences of short-interval stand-replacing fires on postfire tree establishment in subalpine conifer forests. Forest Ecology and Management 478, 118523. https://doi.org/10.1016/j.foreco.2020.118523 Kiel, N.G., Turner, M.G., 2022. Where are the trees? Extent, configuration, and drivers of poor forest recovery 30 years after the 1988 Yellowstone fires. Forest Ecology and Management 524, 120536. https://doi.org/10.1016/j.foreco.2022.120536 Littlefield, C.E., 2019. Topography and post-fire climatic conditions shape spatio-temporal patterns of conifer establishment and growth. fire ecol 15, 34. https://doi.org/10.1186/s42408-019-0047-7 Littlefield, C.E., Dobrowski, S.Z., Abatzoglou, J.T., Parks, S.A., Davis, K.T., 2020. A climatic dipole drives short- and long-term patterns of postfire forest recovery in the western United States. Proceedings of the National Academy of Sciences 117, 29730–29737. https://doi.org/10.1073/pnas.2007434117 Rodman, K.C., Veblen, T.T., Battaglia, M.A., Chambers, M.E., Fornwalt, P.J., Holden, Z.A., Kolb, T.E., Ouzts, J.R., Rother, M.T., 2020. A changing climate is snuffing out post-fire recovery in montane forests. Global Ecology and Biogeography 29, 2039–2051. https://doi.org/10.1111/geb.13174 Smith-Tripp, S., Coops, N.C., Mulverhill, C., White, J.C., Gergel, S., 2024. Early spectral dynamics are indicative of distinct growth patterns in post-wildfire forests. Remote Sensing in Ecology and Conservation. https://doi.org/10.1002/rse2.420 Stevens-Rumann, C.S., Susan J. Prichard, Susan Prichard, Ellen Whitman, Ellen Whitman, Marc‐André Parisien, Marc-André Parisien, Arjan J. H. Meddens, Arjan J.H. Meddens, 2022. Considering regeneration failure in the context of changing climate and disturbance regimes in western North America. Canadian Journal of Forest Research. https://doi.org/10.1139/cjfr-2022-0054 227 Talucci, A.C., Lertzman, K.P., Krawchuk, M.A., 2019. Drivers of lodgepole pine recruitment across a gradient of bark beetle outbreak and wildfire in British Columbia. Forest Ecology and Management 451, 117500. https://doi.org/10.1016/j.foreco.2019.117500 Turner, M.G., Braziunas, K.H., Hansen, W.D., Harvey, B.J., 2019. Short-interval severe fire erodes the resilience of subalpine lodgepole pine forests. PNAS 116, 11319–11328. https://doi.org/10.1073/pnas.1902841116 Turner, M.G., Romme, W.H., Gardner, R.H., 1999. Prefire heterogeneity, fire severity, and early postfire plant reestablishment in subalpine forests of Yellowstone National Park, Wyoming. Int. J. Wildland Fire 9, 21–36. https://doi.org/10.1071/wf99003 Turner, M.G., Romme, W.H., Gardner, R.H., Hargrove, W.W., 1997. Effects of Fire Size and Pattern on Early Succession in Yellowstone National Park. Ecological Monographs 67, 411–433. https://doi.org/10.1890/0012-9615(1997)067[0411:EOFSAP]2.0.CO;2 Table A1: List of Database and Collections included in the systematic literature review Type of literature Name of Database / Collection Bibliographic SCOPUS Web of Science AESD Grey Literature Treesearch NRCan Open S&T Repository (OSTR) J.T. Natural Resource Library (BC Specific) Theses Canada Previous Reviews Stevens-Rumman et al., 2019 Bartels et al., 2016 Waldron et al., 2023 Additional Methods Citation searching Expert recommendations iii. Search update Once all searches were completed, reference files were merged and duplicated files were removed automatically as well as by reviews. b. Article screening and study i. Screening strategy Articles were screened by two reviewers in two-phases: (1) title, abstract and (2) full text. All articles from initial reference searching were screened according to inclusion and exclusion criteria (Table A3). All references included title, text, and abstract were imported into the literature review software Covidence (Covidence Systematic Review 228 Software, n.d.). When reviewers authored a publication included in the reference search, they were excluded from the decision to include or exclude their own work. ii. Consistency checking Consistency checks were applied for both the (a) title and abstract phase and (b) full text phases. We first piloted inclusion and exclusion criteria based on a random subset of articles (80 for title and abstract and 20 at the full text phase). Differences in screening decisions were discussed to ensure consistency. After the first round of title and abstract consistency checking we updated the exclusion criteria to explicitly exclude forests that are in Mediterranean forest types with a significant proportion of black oak. After this discussion, reviewers both screened an additional subset of 50 articles at the title and abstract phase. This process was repeated once so that screeners achieved agreement among studies with a kappa statistic greater than 0.6 (Landis and Koch, 197(Landis & Koch, 1977))(Landis & Koch, 1977) or articles at the title and abstract exclusion phase. All articles that individual reviewers consider questionable for inclusion were discussed among the two reviewers (n = 5). Articles excluded at the title and abstract phase or at the full-text phase are included in the final reference database (Supplemental Table 1). For forest managers in British Columbia, we highlighted articles that are British Columbia focused. 229 iii. Inclusion criteria Table A2 Inclusion and Exclusion Criteria for both Title, abstract as well as full-text screening Key Element Inclusion Exclusion Types of Study Studies that include quantitative data on coniferous and/or spectral recovery across a gradient of abiotic effects Commentary or Framework Reviews Language English Population Western Cordillera Forests interior forests with a coniferous component in western North America. Western north America includes: Western North America, Western US, Western Canada, Provinces and/or states in either Canada or US No explicit references to a post-fire coniferous response Boreal forest Concentrated on non-British Columbia Species (e.g. Bristlecone Pine, White fir (abies concolor), Manzanita, Ceanothus) Paper only measures a tree species not included in the dominance species list Forests located in Mediterranean climate Exposure Fire High-severity wildfire Areas planted after fire Prescribed fire Reburned Comparator Drivers of Recovery Forest with differing ecological factors including: Pre-Fire composition, Fire Severity, Site-dynamics, Post-fire weather trends. Must include a consideration at a least two-or more comparators Studies that did not consider specified ecological factors Focused on invasive encroachment Studies which only consider fire severity metrics (i.e. no topography and/or climate characteristics) Outcomes Post-fire coniferous growth Rate of coniferous establishment (years to establishment), growth rate for coniferous treed Conifer presence can include: Volume, basal area, height, density, proportion on landscape Studies where post-fire responses did not consider a coniferous forest response Simulated responses Projected responses Studies where ecological response is a measure biomass or carbon sequestration AND division between live conifer is not given Measures of post-fire conifers that were alive pre fire (i.e. post-fire mortality focus) 230 c. Critical appraisal i. Critical appraisal After article screening each included article was assessed for reliability to answer the review question. Specifically, articles were assessed for internal and external validity, a reference to systematic error present in the study itself and/or in the general applications to the review question (Burford et al., 2013). The critical appraisal strategies address the core components of study validity; studies are focused, extensive, applied, and transparent (FEAT, Frampton et al., 2017) ii. Critical appraisal strategy We adapted the critical appraisal tool developed by CEE (Version 0.3). We assessed each studies internal validity to address the main biases associated with research studies on environmental questions including: bias in control or outcome, research subject or area selection bias, bias in estimation of exposure variable, bias in included data, error in measurement of outcome, outcome reporting biases, and inappropriate statistical comparisons. We assessed each study according to eight criteria that address the main biases, or issues of internal validity, in research studies on environmental questions (see descriptions in Frampton et al. 2020). For measures external validity we assessed both the applicability, or the degree that the study is applicable to the review question, and the transportability of the findings (Wang et al., 2006). An example of the study validity check for the benchmarking articles is included in Table A4. As part of a data extraction, we included a column whether to ‘include’ or ‘exclude’ a study based on the outcomes of the critical appraisal. 231 Table A3. Criteria for critical appraisal Criterion Name Definition 1. Confounding Factors & Control (internal validity) Are the effects possibly confounded in the study? Did the authors control for potential confounding effects? Are there assessments of the accuracy of the measurement of confounders (either within paper or accessible) Selection of Effect Areas (internal validity) Was the selection of areas random or systematic? If there was no selection was the entire (or nearly entire) population followed post-effect? Were areas later excluded from analysis? Were the areas in the study comparable among groups to allow just comparison? Strength of In-Study Comparison (internal validity) Was the range of ecological effects sufficiently large enough to capture variability in observed coniferous response? Was the variability in measurement error of both effects and initial exposures estimated? Risk of Missed Detection (internal validity) Was there measurement bias in the observed response? Was the variability and accuracy of the measurement considered? Comprehensiveness of reporting (internal validitiy) Are the data and results presented non-selectively and completely? Are small and/or insignificant effects considered Accuracy of Statistical Approach (internal validity) Considers statistical issues arising from (1) overall awareness of the exposure scenario (2) errors in descriptive statistics (3) errors statistical inference (4) violation of normality. This criterion is given double weight because it can easily invalidate the study Applicability (external validitiy) Is the article relevant to the setting the review is used? How much could the comparison be applied to the review? Transportability (external validity) Does the study consider characteristics important to the review? Namely, this is the number ecologic factors considered in the approach d. Data extraction i. Meta-data extraction and coding Data extraction will be included for each included study with the following components tabulated: • Study area • Dominant Forest Type • Study design (Field or Spectral) • Study Location • Measure of Coniferous Recovery • Identified or discussed Recovery Pathways 232 • Method to Identify Recovery Pathways • Modeling approach for environmental factors • List of factors considered • Outcome metrics • Linked studies • Author contact ii. Data extraction strategy Our data extraction strategy was two-part focused on (1) identified pathways or trends after fire, and then (2) drivers of recovery including pattern and trends. To normalize across all studies, we ranked the effects for recovery and categorized them by their effect. In cases where error for models was not applicable or results are described qualitatively, we employed vote-counting to add in a narrative description. Where data is missing or the results are or unclear, the studies’ corresponding author was contacted to request clarification. We consider the overall accuracy of the model to understand the effect of the driver on recovery metrics e. Potential effects modifiers/reasons A list of potential effects modifiers was discussed among reviewers and scientific experts- these effects are noted below: 1. Method of quantifying burn severity: known differences in the advantages and disadvantages of different satellite algorithms as well as field measures of burn severity 2. Latitude: Known differences in forest response that gradually change across latitude 3. Species composition: Differences in functional niches based on species composition. Included and adjusted for using random effects within models. 4. Response variable: Differences in responses based on stem density, BA, or NBR recovery 5. Soil composition 6. Prior site history 7. Invasive Species: Studies that note a strong influence of invasive species will be noted f. Data Synthesis and Presentation i. Data synthesis and presentation INCLUDING: 233 ii. Narrative Synthesis strategy iii. Qualitative Synthesis strategy iv. Other synthesis Strategies Assembled data were used to identify the effects that impacting the rate of coniferous establishment in post-fire forest – addressing the overall research objective. Importantly, as this review merged research split by approach (remote-sensing and field-based techniques) the review focused on the nuances observed in these groupings. Additionally, we discuss differences and/or nuances from research specifically in British Columbia interior dry context, as these have been historically missing from review. When possible, data are presented graphically, describing the main impacts on coniferous establishment using a grouped effect table. Additionally, each effect is included in appendices and are discussed in-text and in table format. v. Assessment of risk of publication Possible publication venues were discussed with the editors-and-chief for Ecosystems and the Canadian Journal of Forest Research. vi. Demonstrating procedural As a compendium of this report, a PRISMA flow is included to demonstrate adherence to the systematic literature review process. g. Declarations vii. Competing interests The authors declare no competing interest. 234 Appendix B Supplemental Tables of Systematic Review Description: The accompanying zip file includes four html formatted tables with additional details for studies included in the systematic review. Files are readable by any web browser. For all tables, “ID” refers to the unique study ID for the study. Each supplemental table is highlighted below. For each table, I describe the column names B.1 Supplemental Table 1 Summary of included papers Description of table columns: ID: unique study id for the study Author: main author of study Year: year study was published DOI: Digital Object Identifier Forest Types (s): Forest type of the study Measure of Fire Severity: descriptor for how the study estimated fire severity Paper type: categorical description if study was field-based (structural) or remote-sensing (spectral) Years of recovery assessment: year that study region was sampled for investigation Metric measured: type of recovery metric measured by study (inclusive of all measures) Distinct trends: categorical if recovery was associated with different pathways of recovery Modeling approach: method of relating recovery metric to associated ecological drivers B.2 Supplemental Table 2 Summary of studies using spectral data Description of table columns: ID: unique study id for the study Author: main author of study Year: year study was published 235 Location: main geographic region of study Forest types (s): forest type of the study Satellite imagery source: source for satellite data Additional processing: description of additional processing for satellite data to achieve the metrics used in the investigation. Includes definition of season used in compositing. Composite type: Type of seasonal composite used in investigation (median, mean, maximum). Measure of Fire Severity: The descriptor for how the study estimated fire severity. Method to assess recovery: The method of evaluating either spectral or structural recovery Spectral metrics: indices and metrics used to test spectral recovery. Spectral baseline: method to determine spectral baseline (NULL = no baseline). Years to ID trajectories: number of years used to associate spectral measures with the descriptor of recovery. Field validation: categorical if study included field validations (X or blank = no validation). Field metric: in-field measure related to spectral recovery Years of field measures: The year that field metrics were measured Measurement: description of in-field measurement methods Spectral field findings: description of how spectral recovery associated with recovery. Modeling of drivers: if not null, description of how the spectral recovery was associated with ecological drivers. Application of driver model: If the ecological driver models are purely spectral or associated with a field-based metric (spectral response indicative of conifer growth). Relationship of Y to coniferous growth: Description of what the ecological drive model is predicting. Estimate of model variance: overall accuracy of the ecological driver model. Provide a 3-sentence summary of the paper: short description of the study B.3 Supplemental Table 3 Description of Drivers by Category and Group Description of table columns: Category: ecological driver category described in RQ4 Group: ecological driver groupings describing in RQ4 Drivers: list of unique drivers tested by ecological models for all included studies (n = 58). 236 B.4 Supplemental Table 4 Individual effects and ranked scores for all ecological driver models Description of table columns: EntryID: Unique identified for the study and the associated model (e.g. 1-2 is study 1 model 2). ID: unique identifier for the study Author: main author of the study Year: year of study publication Forest type: forest type for ecological driver model Paper type: categorical for type of study (structural or spectral) Metric measured: type of recovery metric modeled Variance explained: estimate of model accuracy Driver name: name of ecological driver Value: value associated with driver Effect: direction driver effect in model Significance: determination of significance for the driver (based on p-value or variable importance). Categories of significance are: 1. Very high (P < 0.001) 2. High (P < 0.01) 3. Moderately (P < 0.05) 4. Not (P > 0.05) Drivers not included: Ecological drivers tested and ultimately not included in final models based on tests for model parsimony. (n = 58).
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Coops, Nicholas C.
Post-fire conifer recovery and associated ecological drivers across large extents assessed from satellite and field observations
Text
Dataset
Interactive Resource