Forestry, Faculty of
DSpace
UBCV
Travers-Smith, Hana
Doctor of Philosophy - PhD
University of British Columbia
Climate change is impacting the stability and livelihoods of northern communities across Canada. Warming is accelerated at high-latitudes which has cascading effects on permafrost, hydrology, vegetation and carbon storage. The transitional area between the boreal and tundra biomes, known as the “forest-tundra ecotone”, is a large, dynamic region of climate-sensitive vegetation, including forests, tall-shrubs and tundra plant communities. Warming temperatures are expected to release reproductive limitations on trees facilitating northward advance of the boreal biome. However, there is significant variation in measurements of treeline advance across different locations, and the timing of advance relative to recent climatic changes are unclear. The primary objective of my dissertation is to use remote sensing datasets to investigate change in vegetation structure and its implications for carbon storage across the Canadian forest-tundra ecotone. To accomplish this, I used spaceborne light detection and ranging (Lidar) measurements and multi-spectral satellite imagery to model vegetation structure across 180 million hectares of northern Canada. These models were used to evaluate whether change in vegetation structure over the past 40-years is consistent with northward advance of the boreal biome. I found that forested area expanded northward by approximately 1 million hectares across Canada, but large parts of the limit were stable over time. Mean annual temperature and total precipitation also increased along the forest limit, indicating that the position of the forest-tundra boundary is out of equilibrium with recent climate warming. A central theme of this dissertation is assessing change across spatial and temporal scales. To this end, I also used high-resolution drone imagery to link fine-scale vegetation structure with measurements of soil organic carbon across northern tree and shrublines. At the regional scale, I paired historic air photos with contemporary satellite imagery to assess long-term patterns in tree and shrub expansion at multiple treeline sites. These patterns were linked back to national mapping products derived from multi-spectral time series to understand potential biases and evaluate strengths in both approaches. Ultimately, this dissertation provides new insights into climate driven impacts on northern vegetation across Canada, and demonstrates the use of novel remotely sensed data to support future research.
Mapping vegetation structure and carbon dynamics across the Canadian forest-tundra ecotone using multi-scale remote sensing by Hana Travers-Smith BSc, University of Victoria, 2019 MSc, University of Victoria, 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) November 2025 © Hana Travers-Smith, 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: Mapping forest structure and carbon dynamics across the Canadian forest-tundra ecotone using multi-scale remote sensing submitted by Hana Travers-Smith in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Forestry Examining Committee: Nicholas C. Coops, Professor, Forest Resources Management, The University of British Columbia Supervisor Michael Wulder, Research Scientist, Canadian Forest Services Supervisory Committee Member Trevor C. Lantz, Professor, School of Environmental Studies, The University of Victoria Supervisory Committee Member Danielle Ignace, Associate Professor, Department of Forest Resources, University of Minnesota Supervisory Committee Member Greg Henry, Professor Emeritus, Department of Geography, University of British Columbia University Examiner Jason Pither, Associate Professor, Department of Biology, University of British Columbia-Okanagan University Examiner iii Abstract Climate change is impacting the stability and livelihoods of northern communities across Canada. Warming is accelerated at high-latitudes which has cascading effects on permafrost, hydrology, vegetation and carbon storage. The transitional area between the boreal and tundra biomes, known as the “forest-tundra ecotone”, is a large, dynamic region of climate-sensitive vegetation, including forests, tall-shrubs and tundra plant communities. Warming temperatures are expected to release reproductive limitations on trees facilitating northward advance of the boreal biome. However, there is significant variation in measurements of treeline advance across different locations, and the timing of advance relative to recent climatic changes are unclear. The primary objective of my dissertation is to use remote sensing datasets to investigate change in vegetation structure and its implications for carbon storage across the Canadian forest-tundra ecotone. To accomplish this, I used spaceborne light detection and ranging (Lidar) measurements and multi-spectral satellite imagery to model vegetation structure across 180 million hectares of northern Canada. These models were used to evaluate whether change in vegetation structure over the past 40-years is consistent with northward advance of the boreal biome. I found that forested area expanded northward by approximately 1 million hectares across Canada, but large parts of the limit were stable over time. Mean annual temperature and total precipitation also increased along the forest limit, indicating that the position of the forest-tundra boundary is out of equilibrium with recent climate warming. A central theme of this dissertation is assessing change across spatial and temporal scales. To this end, I also used high-resolution drone imagery to link fine-scale vegetation structure with measurements of soil organic carbon across northern iv tree and shrublines. At the regional scale, I paired historic air photos with contemporary satellite imagery to assess long-term patterns in tree and shrub expansion at multiple treeline sites. These patterns were linked back to national mapping products derived from multi-spectral time series to understand potential biases and evaluate strengths in both approaches. Ultimately, this dissertation provides new insights into climate driven impacts on northern vegetation across Canada, and demonstrates the use of novel remotely sensed data to support future research. v Lay Summary Climate change is occurring more rapidly in the Arctic compared to other parts of the globe. The impacts of climate change on wellbeing and livelihoods are felt strongly by northern communities. In particular, warming is changing how and where forests can grow. Warmer temperatures are thought to increase the height of trees and expand their range into places that were previously too cold or dry. These changes in vegetation impact the amount of carbon that can be stored in plants and soils, which has implications for global climate change. In my dissertation, I use field-observations as well as images from drones, airplanes and satellites to map changes in forests over large parts of northern Canada. I found northward forest expansion in some locations, but large parts of the northern forest limit were stable over time despite increasing temperature and precipitation. This research answers questions about how northern forests are responding to climate change and how remote sensing technology can be used in future research. vi Preface The work presented in Chapters 3, 4, 5 and 6 of this dissertation have been published or submitted in four peer-reviewed journals. I implemented the methods discussed with co-authors and supervisory committee, performed formal analysis, prepared original manuscript drafts and implemented revisions. Co-authors provided feedback, comments and revisions to prepare the manuscript for submission and throughout the peer-review process. Generative AI was not used to write any part of this dissertation or the associated publications. Chapter 3 Travers-Smith, H., Coops, N. C., Lantz, T. C., Hamp, M., Ignace, D., Wulder, M. A., & Van der Sluijs, J. (2025). Vegetation structure and soil organic carbon storage across northern forest-tundra ecotones in continuous permafrost. Arctic Science, 11, 1–18. https://doi.org/10.1139/as-2024-0048 Chapter 4 Travers-Smith, H., Coops, N. C., Mulverhill, C., Wulder, M. A., Ignace, D., & Lantz, T. C. (2024). Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data. Remote Sensing of Environment, 305, 114097. https://doi.org/10.1016/j.rse.2024.114097 Chapter 5 Travers-Smith, H., Coops, N. C., Mulverhill, C., Wulder, M. A., Ignace, D., & Lantz, T. C. (2025). Satellite observations reveal stable forest limits and shrub expansion across the Canadian forest-tundra ecotone. Environmental Research Letters, 20, 104006. https://doi.org/10.1088/1748-9326/adfc7f Chapter 6 Travers-Smith, H., Coops, N. C., Wulder, M. A., Ignace, D., & Lantz, T. C. (In Prep). Long-term vegetation change across the Canadian forest-tundra ecotone using historic air photos and satellite time-series. vii Table of Contents Abstract ......................................................................................................................... iii Lay Summary ................................................................................................................. v Preface .......................................................................................................................... vi Table of Contents ........................................................................................................ vii List of Tables ............................................................................................................... xii List of Figures ............................................................................................................ xiv List of Abbreviations ................................................................................................ xviii Acknowledgements .................................................................................................... xix Dedication .................................................................................................................... xx Chapter 1: Introduction ................................................................................................. 1 1.1 High-latitude climate change ............................................................................. 1 1.2 Vegetation structure in the forest-tundra ecotone ............................................. 3 1.3 Soil carbon storage at the treeline .................................................................... 6 1.4 Permafrost thaw and disturbance ..................................................................... 7 1.5 Remote sensing of northern vegetation ............................................................ 8 1.5.1 Multi-spectral satellite observations ......................................................... 10 1.5.2 Spaceborne lidar ...................................................................................... 13 1.6 Challenges and opportunities .......................................................................... 14 1.7 Research questions and dissertation overview ............................................... 16 Chapter 2: Study area and data ................................................................................. 18 2.1 Canadian forest-tundra ecotone ...................................................................... 18 2.2 Caribou Hills ecoregion ................................................................................... 20 2.3 Key remote sensing datasets .......................................................................... 22 viii 2.3.1 Landsat Best Available Pixel (BAP) composites ...................................... 23 2.3.2 ICESat-2: Land and Vegetation Height product ....................................... 24 Chapter 3: Vegetation structure and soil organic carbon storage across the northern tree and shrublines in NWT Canada .......................................................... 26 3.1 Introduction ..................................................................................................... 26 3.2 Methods .......................................................................................................... 28 3.2.1 Overview .................................................................................................. 28 3.2.2 Study area and site selection ................................................................... 30 3.2.3 Field data collection ................................................................................. 31 3.2.4 Laboratory analysis of soil C and N content ............................................. 32 3.2.5 Drone surveys & photogrammetry ........................................................... 33 3.2.6 Characterizing treeline types ................................................................... 34 3.2.7 Drivers of soil carbon storage across treelines ........................................ 35 3.2.8 Landsat vegetation indices ...................................................................... 37 3.3 Results ............................................................................................................ 38 3.3.1 Characterizing treeline types ................................................................... 38 3.3.2 SOC storage across treelines .................................................................. 41 3.3.3 Landsat vegetation indices ...................................................................... 44 3.4 Discussion....................................................................................................... 46 3.4.1 Variation in SOC across local treelines .................................................... 46 3.4.2 Landsat indices reflect vegetation structure at the treeline ...................... 49 3.4.3 Limitations and future directions .............................................................. 50 3.5 Conclusion ...................................................................................................... 51 Chapter 4: Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2 and Landsat time-series ......................................... 52 ix 4.1 Introduction ..................................................................................................... 52 4.2 Methods .......................................................................................................... 53 4.2.1 Overview .................................................................................................. 53 4.2.2 Study area ............................................................................................... 55 4.2.3 Landsat BAP composites ......................................................................... 56 4.2.4 ICESat-2 ATL08 Land and Vegetation Height ......................................... 57 4.2.5 ASTER Global Digital Elevation Model .................................................... 58 4.2.6 LVIS validation data ................................................................................. 58 4.2.7 Modeling canopy presence and height .................................................... 59 4.2.8 Independent validation with LVIS ............................................................. 61 4.2.9 Delineating the northern limit of continuous forests ................................. 61 4.2.10 Comparison with the (1992) forest-tundra limit ........................................ 62 4.3 Results ............................................................................................................ 63 4.3.1 Impact of season on ATL08 retrievals...................................................... 63 4.3.2 Random Forests model assessment ....................................................... 64 4.3.3 Mapping canopy presence and height across the ecotone ...................... 66 4.3.4 Independent validation using LVIS .......................................................... 68 4.4 Discussion....................................................................................................... 70 4.4.1 Improvement over global canopy height models ...................................... 71 4.4.2 Sources of error for northern canopy height models ................................ 73 4.4.3 Limitations and future directions .............................................................. 75 4.4.4 Conclusions ............................................................................................. 76 Chapter 5: Satellite observations reveal stable forest limits and shrub expansion across the Canadian forest-tundra ecotone ............................................................. 78 5.1 Introduction ..................................................................................................... 78 x 5.2 Methods .......................................................................................................... 80 5.2.1 Study area and method overview ............................................................ 80 5.2.2 Landsat time-series data .......................................................................... 80 5.2.3 Northwest Territories Fire History ............................................................ 81 5.2.4 Classifying structural vegetation change .................................................. 81 5.2.5 Mapping advance of the northern forest limit ........................................... 85 5.3 Results ............................................................................................................ 86 5.3.1 Spectral indicators of vegetation structure ............................................... 86 5.3.2 Structural vegetation change ................................................................... 86 5.3.3 Change in the forest limit ......................................................................... 91 5.4 Discussion....................................................................................................... 94 5.4.1 Structural vegetation dynamics ................................................................ 94 5.4.2 Remotely sensed indicators of vegetation structure ................................ 96 5.4.3 Limitations and future directions .............................................................. 96 5.4.4 Conclusions ............................................................................................. 98 Chapter 6: Long-term vegetation change across the forest-tundra ecotone using historic air photos and satellite time-series ............................................................. 99 6.1 Introduction ..................................................................................................... 99 6.2 Methods ........................................................................................................ 101 6.2.1 Overview ................................................................................................ 101 6.2.2 Study Area ............................................................................................. 102 6.2.3 Historic air photos .................................................................................. 105 6.2.4 Landsat structural vegetation change .................................................... 107 6.2.5 Photo interpretation ............................................................................... 108 6.2.6 Modeling probability of forest and shrub expansion ............................... 110 xi 6.2.7 Correlation with Landsat time-series ...................................................... 112 6.3 Results .......................................................................................................... 112 6.3.1 Visually interpreted change .................................................................... 112 6.3.2 Probability of forest and shrub expansion .............................................. 117 6.3.3 Agreement with Landsat time-series ...................................................... 118 6.4 Discussion..................................................................................................... 122 6.4.1 Climate and fire history .......................................................................... 123 6.4.2 Landsat-based change classification ..................................................... 125 6.4.3 Limitations and future work .................................................................... 126 6.4.4 Conclusions ........................................................................................... 127 Chapter 7: Conclusions ............................................................................................ 129 7.1 Dissertation objectives .................................................................................. 129 7.2 Key innovations ............................................................................................. 132 7.2.1 Expanding soil carbon observations in continuous permafrost .............. 132 7.2.2 Mapping northern vegetation structure .................................................. 133 7.2.3 Understanding spectral greening trends at the treeline ......................... 134 7.2.4 Multi-scale validation of Landsat change detection ................................ 135 7.3 Limitations and future directions ................................................................... 136 7.3.1 Use of space-for-time substitution in ecology ........................................ 136 7.3.2 Improving reference height datasets ...................................................... 137 7.4 Closing statements ....................................................................................... 138 References ................................................................................................................. 139 xii List of Tables Table 1-1: List of spectral indices and equations referenced throughout this dissertation. ...................................................................................................................................... 10 Table 3-1: Landsat and topographic variables used to define treeline types within a k-means clustering algorithm. Variables at 30m resolution were aggregated to the transect scale representing an area approximately 250x100m (~27 pixels). ................. 35 Table 3-2: Mean (± standard error) carbon storage parameters summarized by treeline type. Asterisks (*) indicate where there were significant (p < 0.05) differences between treeline types. ................................................................................................................ 42 Table 3-3: Significant variables predicting Soil Organic Carbon (SOC) after step-wise variable selection. The global model included all soil samples with < 30% mineral content (n = 145). Separate models were also fit for each treeline type. The model coefficient is included in brackets and symbology indicates the significance level of the variable (p < 0.001 = ***, p < 0.01 = **, p < 0.05 = *, p < 0.1 = ^). ................................. 44 Table 4-1: Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), Root Mean Square Error (RMSE), and bias for the Random Forests regression model predicting canopy height on a testing dataset of undisturbed ATL08 segments not used in model training (n = 90,000). Error metrics are stratified by height and ecozone. The overall r2 was 0.49. ........................................................................................................ 65 Table 4-2: Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), Root Mean Square Error (RMSE) and bias of canopy height predictions compared with independent airborne laser scanning data from LVIS using 98th percentile canopy height. The overall R2 was 0.53 (n = 30,000). ............................................................... 69 Table 5-1: Descriptions of the nine structural vegetation types classified at 30m spatial resolution. Decision rules were based on change in the majority class between the first and last three time periods and linear trends in canopy height. .................................... 84 Table 5-2: Summary table showing the proportion of structural vegetation change across the entire study area and stratified by ecoregion (%). ........................................ 88 Table 5-3: Summary table showing the proportion of vegetation classes stratified by disturbance history within the Northwest Territories (%). .............................................. 91 Table 6-1: Summary of climate normals for three long-term monitoring stations within My study area. Climate normals are from the 1971-2000 and 1991-2020 periods. Data are publicly available and compiled by Climate Change & Environment Canada xiii (https://climate.weather.gc.ca/climate_normals/index_e.html). Summer temperatures are calculated for June-August, and winter temperatures are January-March. Note data for the 1991-2020 period are not yet available at Fort Reliance. ................................. 105 Table 6-2: Air photo characteristics and metadata across transects crossing the historic forest limit. ................................................................................................................... 107 Table 6-3: Descriptions of the nine Landsat-derived structural vegetation types classified at 30 m spatial resolution. Decision rules describe logic used to classify change in canopy presence and height across six time-periods. The simplified classes used in this study are shown in the last column. ......................................................... 107 Table 6-4: Percent of 300 m grid cells showing stability and change in forest and shrub cover mapped using historic air photos and modern high resolution satellite imagery.114 Table 6-5: Summary of fire history for each transect showing the proportion of 300 m cells with fire and the year of the fires derived from the NWT Fire History and Composite2Change datasets. Transect 1 (Coppermine) did not have any known fire. Percent change indicates the proportion of burned 300 m cells with increasing or decreasing forest and shrub cover. ............................................................................. 115 xiv List of Figures Figure 1-1: Diagram summarizing research goals, key remote sensing datasets and spatial scales of analysis for the four data chapters. ..................................................... 17 Figure 2-1: Extent of the study domain and major ecozones across the Canadian forest-tundra ecotone. The red point indicates the location of field-sites within the Caribou Hills ecoregion in Northwest Territories. .......................................................... 20 Figure 2-2: Location of northern tree and shrubline sites (n = 11) along the Inuvik-Tuktoyaktuk Highway. Oblique photos A) and B) show representative sites in the study area. .............................................................................................................................. 22 Figure 3-1: Flow chart of the primary steps in the data analysis and associated outputs. As noted in the key, the color and shading of the boxes indicate which data source and scale was used to derive each output. .......................................................................... 29 Figure 3-2: Map of the study area in northwestern Canada. Transects were established along the Inuvik-Tuktoyaktuk highway (left). At each transect (n=11) I acquired soil samples (n=15) and concurrent drone imagery at 1cm spatial resolution (right). The image to the right shows the location of soil samples at the transect marked with an arrow. The inset map (top-left) shows the study area in relation to the tree limit and forest-tundra ecotone mapped by Timoney et al. (1992). .............................................. 32 Figure 3-3: Oblique imagery of the three treeline types. The yellow arrows show the approximate direction of transects across local treelines (note that the arrows do not show the exact location of the transects). ..................................................................... 39 Figure 3-4: Boxplots of Landsat indices and topographic variation within three landscape clusters (Moist Tundra, Mesic Woodland and Submesic Woodland), classified using a k-mean clustering algorithm. Note that stem density was not used to cluster treeline types but is shown to contextualize differences in treeline types. ......... 40 Figure 3-5: Cover of plant functional groups within 1m2 and 3m2 plots (n=55) among the three treeline types (Moist Tundra, Mesic Woodland and Submesic Woodland). Note that percent cover can exceed 100 as overlapping layers of plants within the same functional group Ire summed together. .......................................................................... 41 Figure 3-6: Variation in SOC storage (kgC/m2) with tree stem density, calculated as the number of stems > 2.5m within 30m of each soil sample. Panel A) shows SOC across all treeline types (n=145); point color represents sample bulk density and size represents depth of the organic soil layer. The y-axis of A) has been logarithmically xv transformed. Panel B) shows variation in SOC across four classes of increasing stem density within the three treeline types. .......................................................................... 43 Figure 3-7: Relationships between Landsat spectral indices (Enhanced Vegetation Index, Tasseled Cap Brightness, Greenness and Wetness) with drone metrics (Excess Greenness (ExG), per pixel tree stem counts and maximum canopy height). RMSE and r2 values are extracted from Generalized Additive Models. Red plots show the best single Landsat spectral index to predicts each drone variable. ..................................... 45 Figure 4-1: Flowchart showing methods workflow used to predict and validate canopy presence and height across the forest-tundra ecotone. ................................................ 54 Figure 4-2: Map of the northwestern arctic and sub-arctic of Canada. The grey points show the ICESat-2 ATL08 segments (n = 982,396) used to train and test the Random Forests models. The orange lines show the extent of the 2019 LVISF2 product used as validation data (Blair & Hofton, 2020). ........................................................................... 55 Figure 4-3: Boxplots (left) showing height distribution for summer (July-August) and winter (December-January) ICESat-2 ATL08 98th percentile canopy height and LVIS 98th percentile canopy height by increasing latitude. The red line shows the 3m height limit. The map (right) shows the distribution of ICESat segments (light green and dark green points) and LVIS transects (blue) across the forest-tundra ecotone. ................... 64 Figure 4-4: Variable importance for the Random Forests classification and regression models predicting canopy presence and height. Variables are ordered from most (dark colors) to least important (light) based on the permutation method. .............................. 66 Figure 4-5: Map of canopy height and presence across the Canadian forest-tundra transition at 30 m spatial resolution. The red line shows the 1:1 forest limit defined by Timoney et al (1992) and white areas represent water. The inset map (indicated by the arrow) shows spatial detail in 30m canopy height predictions at the forest limit. ........... 67 Figure 4-6: Density plot of the probability density function of canopy height (m) sampled from the Southern Arctic, Taiga Plains and Taiga Shield ecoregions. Note only pixels with a probability of canopy presence > 50% were included. ........................................ 68 Figure 4-7: Canopy height predicted by Random Forests against the validation dataset, LVIS-F2 98th percentile canopy height. The dashed red line is the 1:1 ratio between observed and predicted values. The color indicates the number of cases in each hexagon cell. R2 was 0.53. ............................................................................................ 70 Figure 5-1: Distribution of t-statistic used define non-trended, increasing and decreasing trends in canopy height. Dashed lines indicate the ±1.69 threshold. .......... 85 xvi Figure 5-2: Variation in spectral predictors (Band 4: NIR, Band 8: SWIR 2, Tasseled Cap Brightness Index and Tasseled Cap Greenness Index), that were important in the Random Forests models among canopy height classes. Note that the data were scaled for plotting on the same y-axis and values show the number of standard deviations away from the mean value for each band or index. ....................................................... 87 Figure 5-3: Map of structural vegetation classes across the study area. The inset maps show detail in the 30m change product. Panel A also highlights structural change within the extent of two fires that burned in 1999 and 1968 (black outline). Water and unclassified pixels areas are shown in white. ................................................................ 89 Figure 5-4: Distribution of transitional vegetation classes across the study area relative to the 1995 forest limit. For visualization, 30m pixels were aggregated into 300x300m cells and the number of pixels of each class were counted per cell. ............................. 90 Figure 5-5: Mapping retreat and advance of the northern limit of continuous 3m forest using canopy height and presence in the 1985-1995 and 2010-2021 time periods. The inset map highlights a region where the forest limit has advanced northward. The canopy height data is mapped at 30m spatial resolution. .............................................. 93 Figure 6-1: Flow chart of the primary steps in the data analysis and associated outputs. As noted in the key, the color and shading of the boxes indicate which data source and scale was used to derive each output. ........................................................................ 102 Figure 6-2: Study area maps (top) showing the extent of historic air photos and modeled canopy height across four transects (summarized in Table 1) representing the transition from forest to tundra land cover. The bottom map shows the location of each transect (red circles) relative to the forest limit (white line). The grey squares indicate long-term weather stations close to the study sites. .................................................... 104 Figure 6-3: Examples of air photos and satellite imagery used to map change in forest and shrub cover across four transects crossing the forest-tundra ecotone. The bottom row shows the Landsat structural change classification maps at 30m spatial resolution (Travers-Smith et al., 2025) and the black outline shows the extent of the fine-scale imagery within each transect. White areas are lakes and waterbodies that were removed from analysis. In the WorldView imagery from Transect 4, tamarack (Larix Laricina) is visible on the edges of the black spruce stand. ......................................... 111 Figure 6-4: Change in forest and shrub cover for Transect 4 (Whitefish East) mapped in 300 m cells using the interpretation of historic air photos (1971) and high spatial resolution satellite imagery (2023)............................................................................... 116 xvii Figure 6-5: Probability that tree and shrub cover will increase relative to the 2021 limit of continuous forest using all grid cells (n = 2063). The solid lines show the predicted probability for each combination of transect and vegetation type and the shaded area shows a 95 % confidence interval of the prediction. Points represent individual grid cells and the grey-dashed line shows the position of the 2021 treeline. .............................. 118 Figure 6-6: Boxplots showing correspondence between percent cover of forest, shrub and herbaceous vegetation mapped on air photos and stable vegetation cover derived from Landsat time series. Percent cover represents the proportion of undisturbed 300m2 grid cells where vegetation is present. ........................................................................ 120 Figure 6-7: Confusion matrices showing correspondence of vegetation change interpreted using high-resolution air photos (1970’s and 1930’s) and satellite imagery (2020’s), with a Landsat time-series classification assessed in 30 m pixels (n = 646). Change was binary with 1 indicating increasing vegetation structure and 0 indicating no change. The cell values show the proportion of Landsat pixels in agreement with the direction of change interpreted from the high-resolution imagery. The darker shades of green indicate higher correspondence. ....................................................................... 121 xviii List of Abbreviations ALS Airborne Laser Scanning ASTER Advanced Topographic Laser Altimeter ATL03 ATLAS / ICESat-2 L2A Global Geolocated Photon Data ATL08 ATLAS / ICESat-2 L3A Land and Vegetation Height ATLAS Advanced Topographic Laser Altimeter BAP Best Available Pixel C2C Composite-to-Change CHM Canopy Height Model CV Coefficient of Variation DEM Digital Elevation Model DRAGANN Differential, Regressive and Gaussian Adaptive Nearest Neighbour ETM+ Enhanced Thematic Mapper Plus EVI Enhanced Vegetation Index ExG Excess Greenness FTE Forest-tundra ecotone GEDI Global Ecosystem Dynamics Investigation GLAS Geoscience Laser Altimeter ICESat Ice, Cloud and land Elevation Satellite LVIS Land, Vegetation, and Ice Sensor MAE Mean Absolute Error MAPE Mean Absolute Percent Error NAPL National Air Photo Library NBR Normalized Burn Ratio NDVI Normalized Difference Vegetation Index NIR Near Infrared NWT Northwest Territories OLI Operational Land Imager PCA Principle Component Analysis R2 Coefficient of Determination RF Random Forests RMSE Root Mean Square Error SOC Soil Organic Carbon SWIR Shortwave Infrared TCB Tasseled Cap Brightness TCG Tasseled Cap Greenness TCW Tasseled Cap Wetness TM Thematic Mapper xix Acknowledgements So many people have supported me over my graduate student career! I would like to acknowledge past and present members of the IRSS and Arctic Landscape Ecology lab for help with field work, coding, writing and morale support. In particular, the IRSS-affiliated sports teams Dinosours FC and Disky Buisness for all the good times on the pitch. I would also like to extend special thanks to Dr. Trevor Lantz for introducing me to the world of Arctic ecology and for his steady and kind mentorship over the years. I wouldn’t be here without you! Also, many thanks to my PhD supervisor Dr. Nicholas Coops for the 3-ft long computer monitor (which I will miss dearly), as well as his continuous support of all areas of my research. The IRSS is a truly special environment to work in and I am very grateful for the experiences I have had in the lab. I would also like to thank my committee members Dr. Michael Wulder and Dr. Danielle Ignace, who have been incredibly helpful collaborators and mentors. I am also very appreciative of my cat Henry, for being very cute. There are not enough words to properly thank my parents Edward and Laurel for all of the love and support I have received. My mom was the first person in our family to receive a doctorate degree and I am so proud to follow in her footsteps. Love you guys! Finally, I am fortunate to conduct this research on the traditional, ancestral and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people at the University of British Columbia. I am also grateful to have the opportunity to complete field-work on the traditional territories of Inuvialuit and Gwich’in peoples. A land acknowledgment only goes so far, but during my time as a PhD student and beyond, I commit to supporting anti-colonial initiatives in my community and be open to a process of ongoing learning. xx Dedication This is for my mom!1 Chapter 1: Introduction 1.1 High-latitude climate change Anthropogenic greenhouse gas emissions have changed the global climate, destabilizing ecological and social systems (IPCC, 2014). At high latitudes, warming is intensified due to land-atmosphere feedbacks collectively known as polar amplification (Serreze & Barry, 2011). Across the circumpolar Arctic, air temperatures have warmed more than 2˚ since 1978 (IPCC, 2014), which is 3-4 times the global mean (Rantanen et al., 2022). This pattern is largely driven by positive feedbacks associated with sea ice loss, changes in snow cover, albedo, and atmospheric circulation that enhances the transfer of heat and moisture to the poles (McGuire et al., 2006; Winton, 2006). Warming trends have been the most rapid in the autumn and winter, resulting in earlier snowmelt and longer growing seasons with delayed snow accumulation and formation of sea and lake ice (Box et al., 2019; Chapin III et al., 2000). Projected trends in precipitation are less certain due to sparse monitoring stations and differences in data collection (Box et al., 2019). However, most models predict increasing precipitation over the next century, with a greater proportion falling as rain rather than snow (Box et al., 2019; McCrystall et al., 2021). A warmer and wetter Arctic will have significant impacts on the northern carbon budget (Schuur et al., 2015). Historically, boreal and tundra biomes have acted as strong carbon sinks, sequestering large amounts of carbon in cold soils and permafrost (Lafleur et al., 2012; McGuire et al., 2012; Tarnocai et al., 2009). While warming is associated with greater plant productivity and aboveground biomass (Epstein et al., 2012; Sistla et al., 2013) these gains may be offset by losses from soil respiration (Abbott et al., 2016). Permafrost soils currently store twice as much carbon as the atmosphere and could 2 represent a significant positive feedback to climate warming if respiration rates increase in response to changes in ground temperature and moisture (Schuur et al., 2015; Tarnocai et al., 2009). Rising air temperatures are expected to decrease the extent of permafrost and increase the depth of the seasonally thawed active layer (Farquharson et al., 2019; Kokelj et al., 2017). These changes in permafrost and ground temperature can promote decomposition of organic material, releasing carbon dioxide and methane from the soil (Keuper et al., 2020; Mack et al., 2004; Xu et al., 2024). Measurements of atmospheric CO2 suggest that the strength of high-latitude carbon sinks has diminished over recent decades (Wang et al., 2018). While undisturbed boreal forests remain net carbon sinks, tundra regions are becoming carbon neutral or small carbon sources (Virkkala et al., 2021). Warmer, drier summers are likely driving these trends by increasing respiration and summer CO2 emissions (Angert et al., 2005; Cahoon et al., 2012). Despite a positive correlation between temperature and primary productivity, extreme moisture stress during the summer limits carbon uptake and decreases sink strength in the tundra (Zona et al., 2023). Thus, continued warming results in diminishing returns for northern carbon storage. Expanding tree and shrublines are also expected to impact soil respiration and may accelerate decomposition of soil carbon through soil-root interactions (Clemmensen et al., 2021). Taken together, monitoring ongoing change in the distribution and composition of vegetation will be critical for projecting future carbon dynamics. 3 1.2 Vegetation structure in the forest-tundra ecotone The forest-tundra ecotone (FTE) is the transitional region between the boreal forest and Arctic tundra biomes, covering 1.9 million km2 globally (Ranson et al., 2011). Vegetation structure refers to the three-dimensional arrangement of plants, related to canopy height, volume and biomass (Wulder et al., 2012). Structure across the ecotone is characterized by declining forest height and density from the boreal forest to the northern “treeline”. the northernmost limit where trees with upright growth forms ~3-4 m in height can grow (Epstein et al., 2004; Payette et al., 2001). Beyond the treeline, “krummholz” growth forms of tree species with shrubby, prostrate structure may be present, but are generally not considered in definitions of the northern treeline boundary (McLeod, 2002). Timoney et al. (1993) describes vegetation structure across the ecotone using the ‘sigmoid wave hypothesis, where the ratio of forest to tundra land cover decreases across the ecotone following an S-shaped logistic curve. In places with strong topographic or edaphic restrictions, the transition from forest to tundra may be abrupt (Timoney et al., 1992). However, where climate is the primary limitation this transition may be more gradual, stretching over 200 km in some parts of the Canadian forest-tundra ecotone (Timoney et al., 1992). Over long periods of time, climate warming can release reproductive limits on vegetation and facilitate northward advance of boreal vegetation and wildlife (Fossheim et al., 2015; Speed et al., 2021). MacDonald et al., (1998) described three possible treeline responses to climate change, 1) increases in the height, stature and reproductivity capacity of individual trees, 2) increases in population density of trees below the treeline, 3) northward range expansion of forests. These processes have been shown to occur 4 independently and at different rates across the ecotone (Danby & Hik, 2009; MacDonald et al., 1998). In Canada, reconstruction of the treeline using paleobotanical records show that forests have tracked changes in climate over millennia (Ritchie, 1984). The current position of the treeline was reached approximately ~4000 years ago. However, treelines were significantly higher during a period of rapid warming 7000-11,000 years ago, and likely reached the Arctic coastline in some regions (Ritchie, 1984). The present day position of global alpine and latitudinal treelines closely coincides with mean growing season temperatures of 6-7˚C (Körner & Paulsen, 2004), supporting the Growth Limitation Hypothesis proposed by (Körner, 1998). Meta-analyses of recent treeline migration rates show that the majority of treelines have advanced upwards and northwards (Hansson et al., 2021; Harsch et al., 2009). Hansson et al. (2021) reported that 67% of alpine treelines and 56% of latitudinal treelines showed evidence of advance, while Harsch et al. (2009) reported advance in 52% for both types of treelines. However, the majority of studies included in these meta-analyses are from alpine treelines (92% and 76%, respectively), indicating that latitudinal treelines may be under sampled globally. There is growing evidence that many latitudinal treelines are out of equilibrium with recent climate warming and have not kept pace with increases in air temperature (Maher et al., 2021; Rees et al., 2020; Timoney et al., 2019; Timoney & Mamet, 2020; Travers-Smith & Lantz, 2020). For latitudinal treelines, Rees et al (2020) report an average rate of global treeline advance of 47 m per year, which is three orders of magnitude slower than changes in temperature. Similarly, Maher et al (2021) found that treelines across Alaska 5 occupied isotherms 2-3˚C warmer than the Growth Limitation Hypothesis predicts. Over western and central Canada, Timoney & Mamet (2020) also found limited evidence of systemic treeline advance over a 60-year period. While air temperature provides a proxy for treeline position at broad-spatial scales, local factors including nutrient availability (Timoney, 2023), soil drainage (Lloyd et al., 2002), herbivory (Cairns et al., 2007), shrub cover (Kambo & Danby, 2018), aspect, and wind exposure (Gamache & Payette, 2005) also control tree establishment and growth at the treeline. Spring and autumn conditions, as well as temperature extremes are also important determinants of seedling recruitment, but are less frequently reported (Burger et al., 2025; Harsch et al., 2014). For example, white spruce (Picea glauca) requires several consecutive growing seasons with favorable conditions to produce viable seeds (Brown et al., 2019; Juday & Barber, 2003). In the Mackenzie Delta region of the NWT, Walker et al. (2012) also found that 20 years of warming did not change cone production or seed viability of white spruce in the forest-tundra ecotone. The apparent disequilibrium between treelines and temperature may also reflect the slow growth rates of northern tree species in comparison to rapidly changing climate conditions (Mengtian et al., 2017; Rees et al., 2020). In the short-term, the most significant changes in vegetation across the FTE may be related to disturbance and recovery, or rapid shrubification (Dial et al., 2016; Timoney et al., 2019). Long-term processes including increases in canopy height and stand density below the treeline, may facilitate northward forest advance over much longer time scales (Danby & Hik, 2009; Mamet & Kershaw, 2012). 6 1.3 Soil carbon storage at the treeline Above and belowground carbon storage are linked through interactions between plant roots and the soil (Rustad et al., 2001). Although primary productivity is low, restricted drainage, cold temperatures and the presence of permafrost in some tundra soils limits soil decomposition leading to organic matter accumulation and carbon storage over long periods of time (Wilmking et al. 2006). Sedges and evergreen shrubs characteristic of tundra heath can also increase soil carbon storage by creating stable pools of below-ground necromass that are not easily decomposed (Clemmensen et al., 2021). The expansion of woody vegetation in the tundra can also accelerate decomposition of soil carbon pools via rhizome priming effects, which are currently not accounted for in projections of climate change (Mack et al., 2004; Palmtag et al., 2022). Experimental evidence shows that the introduction of live roots to permafrost soils could release a significant amount carbon within 3-5 growing seasons through rhizome priming effects that stimulate microbial growth and activity (Friggens et al., 2025). Observational studies across alpine treelines have also shown that tundra soils can store more carbon than nearby woodland sites (Clemmensen et al., 2021; Hartley et al., 2012; Kemppinen et al., 2021; Parker et al., 2015, 2021). Based on these findings, Parker et al. (2021) suggest that soil carbon losses following treeline and shrubline advance could be an order of magnitude greater than gains from plant productivity and above-ground biomass. However, some studies have also found no long-term changes in soil carbon pools with warming, despite increases in above-ground biomass and tree stem density (Ziegler et al. 2017; Sistla et al. 2013). For example, Devos et al. (2022) assessed differences in carbon storage between forest and tundra soils across a 500 km latitudinal transect in 7 Norway and found that forested soils had slightly higher mean soil organic carbon (SOC) storage compared to tundra soils, albeit with substantial variation between site types. These findings suggest that factors other than vegetation composition such as topography (Pichler et al. 2022; Zona et al. 2011), landform type (Wojcik et al. 2019) or the presence of permafrost can also mediate soil carbon storage across local treelines. There is a critical need to expand measurements of vegetation structure and soil carbon storage to diverse sites across the circumpolar region (Parker et al., 2021). 1.4 Permafrost thaw and disturbance The distribution of boreal and tundra vegetation also depends on interactions with permafrost and ground temperature (Baltzer et al., 2014; Epstein et al., 2004; Lloyd et al., 2003). In the discontinuous permafrost zone, permafrost thaw can create collapse scars, resulting in declining forest cover and the formation of treeless wetlands (Baltzer et al., 2021; Osterkamp et al., 2000). Conversely in the tundra, tree establishment is limited to warm, and moist sites with deep active layers (Payette et al., 2001; Timoney et al., 1993). Thus, permafrost thaw, which increases ground temperatures and improves soil drainage, can facilitate tree and shrub establishment (Limpens et al., 2021; Lloyd et al., 2003). In turn vegetation cover can mediate permafrost temperature by controlling snow depth and heat loss during the winter (Burn & Kokelj, 2009; Kokelj et al., 2017). In the Mackenzie delta region, deeper snow banks in forested areas insulate the ground and maintain winter temperatures 5˚C warmer than surrounding tundra (Burn & Kokelj, 2009). Higher ground temperatures at sites with forests and tall-shrubs increases the sensitivity of permafrost to disturbance and the likelihood of thaw following climate warming (Kokelj et al., 2017). 8 Climate change can also decrease forest resilience by increasing the likelihood of succession to novel ecosystems after disturbance (Johnstone et al., 2016; Seidl et al., 2017). Warmer summer temperatures and more frequent droughts are expected to increase the number and severity of wildfires across the boreal and tundra (Masrur et al., 2018; Turetsky et al., 2011). Black spruce (Picea mariana) is a fire-adapted treeline species across the boreal that is exhibiting decreased resilience to more intense fire regimes (Baltzer et al., 2021). Across North America from 1989-2014, black spruce re-established at 61% of sites after fire, but completely failed to regenerate in 18% (Baltzer et al., 2021). In Alaska, fires have also driven shifts from coniferous dominant forests to broadleaf deciduous, or mixed forests (Johnstone et al., 2010, 2020). Johnstone et al., (2010) showed that deciduous species preferentially establish on mineral soils following severe fires that remove the organic horizon. Overall, a shift in community composition from coniferous to deciduous forests may have a net cooling effect on the climate, as deciduous trees have higher summer and winter albedo, and are less flammable compared to conifers (Chapin III et al., 2000; Johnstone et al., 2020). A shift to deciduous forests will also impact litter quality and rates of decomposition as conifer needles are slower to decompose compared to deciduous leaf litter (Cornelissen et al., 2007). 1.5 Remote sensing of northern vegetation Satellite remote sensing can provide quantitative and standardized measurements of terrain and vegetation characteristics over large areas (Dubayah et al., 2020; Feng et al., 2023; Wulder et al., 2022). This is critical in remote northern regions, where field-based monitoring networks can be expensive and difficult to maintain (Beamish et al., 2020). Broadly, sensor types can be categorized as either passive (optical) or active, depending 9 on whether they emit a source of energy. Platforms with passive sensors (i.e. multispectral or hyperspectral) measure energy reflected by the Earth’s surface and atmosphere across ‘bands’ of the electromagnetic spectrum, whereas active sensors (i.e. light detection and ranging or “lidar”) transmit energy to the surface and record properties of the returning signal. Optical platforms are sensitive to the reflectance characteristics of the vegetation canopy and soil, while active platforms can assess three-dimensional vegetation structure and surface elevation. Optical platforms, such as the Landsat series of satellites, capture spatially continuous images over the entire world and have archived imagery dating back to the 1970’s (Wulder et al., 2022). The number of spectral bands and their coverage of the electromagnetic spectrum varies, but the majority of Landsat data are composed of six bands from 400-2500 nanometers: blue, green, red, near-infrared (NIR) and two shortwave-infrared (SWIR1, SWIR2) bands. Consistency of surface reflectance measurements over long periods of time make these data extremely useful in monitoring ecosystem change (Hermosilla et al., 2016). The individual spectral bands can also be combined in simple equations to calculate spectral indices (Table 1-1), which reduces the dimensionality of imagery and enhances differences between spectral bands (Crist, 1985; Tucker, 1979). Compared to optical sensors, spaceborne lidar systems are relatively new and are less used frequently at high-latitudes. The Geoscience Laser Altimeter System (GLAS) was the first lidar sensor onboard the Ice, Cloud and Land Elevation Satellite (ICESat) and was operational from 2003 to 2009 (Nelson, 2010). Due to higher energy demands, spaceborne lidar platforms generally have shorter mission durations than optical platforms. However, active sensors are not limited by darkness and can acquire 10 data during the northern winter months, although the effects of winter show cover and leaf-off vegetation conditions must be considered (Feng et al., 2023). Integrating both forms of remote sensing can minimize disadvantages of either type, while providing a comprehensive picture of both surface characteristics and vertical structure of vegetation. Table 1-1: List of spectral indices and equations referenced throughout this dissertation. Index Equation Reference Normalized Difference Vegetation Index (NDVI) 𝑁𝐼𝑅 − 𝑅𝑒𝑑𝑁𝐼𝑅 + 𝑅𝑒𝑑 (Tucker, 1979) Enhanced Vegetation Index (EVI) 2.5 (𝑁𝐼𝑅 − 𝑅𝑒𝑑𝑁𝐼𝑅 + 6𝑅𝑒𝑑 − 7.5𝐵𝑙𝑢𝑒 + 1) (Huete et al., 2002) Normalized Burn Ratio (NBR) 𝑁𝐼𝑅 − 𝑆𝑊𝐼𝑅2𝑁𝐼𝑅 + 𝑆𝑊𝐼𝑅2 (Key & Benson, 2006) Tasseled Cap Greenness (TCG) −0.1603𝐵𝑙𝑢𝑒 − 0.2819𝐺𝑟𝑒𝑒𝑛 − 0.4934𝑅𝑒𝑑 +0.7940𝑁𝐼𝑅 − 0.0002𝑆𝑊𝐼𝑅1 − 0.1446𝑆𝑊𝐼𝑅2 (Crist, 1985) Tasseled Cap Wetness (TCW) −0.0315𝐵𝑙𝑢𝑒 + 0.2021𝐺𝑟𝑒𝑒𝑛 + 0.3102𝑅𝑒𝑑 +0.1594𝑁𝐼𝑅 − 0.6806𝑆𝑊𝐼𝑅1 − 0.6109𝑆𝑊𝐼𝑅2 (Crist, 1985) Tasseled Cap Brightness (TCB) 0.2043𝐵𝑙𝑢𝑒 + 0.4158𝐺𝑟𝑒𝑒𝑛 + 0.5524𝑅𝑒𝑑 +0.5741𝑁𝐼𝑅 + 0.3124𝑆𝑊𝐼𝑅1 + 0.2303𝑆𝑊𝐼𝑅2 (Crist, 1985) 1.5.1 Multi-spectral satellite observations Multi-spectral satellite observations have been used to characterize Arctic vegetation since the 1990’s (Myneni et al., 1997). To date, a significant body of literature has focused on mapping and analyzing trends in the Normalized Difference Vegetation Index (NDVI), which contrasts reflectance in the red and near infrared bands as a proxy for vegetation productivity (Gao et al., 2000). High NDVI measured from satellites correlates with field 11 measurements of Leaf Area Index (LAI) and phytomass (Epstein et al., 2012; Walker et al., 2003). Other vegetation indices such as the Enhanced Vegetation Index (EVI; Gao et al., 2000), or Tasseled Cap Greenness (Crist, 1985), capture similar relationships using additional spectral bands. Collectively these indices capture the phenomenon known as spectral “greening”, which typically refers to increasing NDVI trends across the circumpolar north (Beck & Goetz, 2011; Blok et al., 2011; Bonney et al., 2018; Frost et al., 2014; Ju & Masek, 2016; Kushida et al., 2015; Labrecque-Foy et al., 2024; Pattison et al., 2015; Sulla-Menashe et al., 2018). The drivers of spectral greening trends are complex due to the broad spatial resolution of satellite sensors and their limited ability to resolve features within individual pixels (Frost et al., 2025; Myers-Smith et al., 2020). The most common satellite platforms for deriving greenness include the Advanced Very-High-Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Landsat and Sentinel series of satellites, which have pixel resolutions of 1.1 km, 500 m, 30 m and 10 m, respectively. While there is consensus that large regions of the Arctic are undergoing greening, analysis of data from different platforms and algorithms disagree on the exact location and magnitude of the signal (Guay et al., 2014; Ju & Masek, 2016). Greening trends between sensors may be confounded by the timing of snowmelt (Gamon et al., 2013), clouds (Karlsen et al., 2018), sun angle (Li & Strahler, 1986) and differences in surface water extent and soil moisture between images (Raynolds & Walker, 2016). Across the North American forest-tundra, the greening signal is strongest in recently disturbed areas (Sulla-Menashe et al., 2018), and at the northern margin of the ecotone (Berner & Goetz, 2021). It is hypothesized that greening at the northern limit of the boreal 12 forest could signal northward advance of the biome (Berner & Goetz, 2021; Bonney et al., 2018). However, greening indices alone may not capture variation in sparse northern forests, due to the influence of green understory vegetation in mixed pixels (Brehaut & Danby, 2018; Loranty et al., 2018; Walker et al., 2021b). Indices that use longer wavelengths in the shortwave-infrared spectrum such as Normalized Burn Ratio may be better predictors of forest structure than single greenness indices (Fraser et al., 2017; Pickell et al., 2016). Multi-spectral images can also be used to model a range of biophysical attributes by co-locating surface reflectance with higher spatial-resolution imagery or other reference datasets. For example, the Vegetation Continuous Fields (VCF) product links discrete measurements of forest cover with spectral indices from MODIS or Landsat imagery in a Random Forests model to predict tree cover over large areas (Montesano et al., 2009; Sexton et al., 2013). Other common modelling approaches include linear regression (Margolis et al., 2015), imputation (Matasci, et al., 2018b) and deep learning techniques (Lang et al., 2023). Modeled trends in tree cover across the North American boreal indicate that forested area is increasing overall (Hermosilla et al., 2025; Montesano et al., 2024). However, increases in tree cover are concentrated in the middle of the biome, with more limited forest expansion detected at the northern margin (Rotbarth et al., 2023). These findings reflect other observations of land cover change across the North American forest-tundra, showing decreasing cover of herbaceous tundra and lichens, and increasing cover of shrub tundra and woodlands (Macander et al., 2022; Wang et al., 2020). 13 A key challenge in developing remotely sensed models is assembling sufficient high-quality reference data for training and validation (Wulder et al., 2018). Trade-offs must be made between the level of detail, spatial extent and the cost of acquiring field data or high-resolution imagery (i.e. < 1 m pixel resolution). Common sources of reference data include imagery from aircraft or drones (Macander et al., 2022; Orndahl et al., 2022), forest inventory plots (Hermosilla et al., 2018) or other field-based measurements of canopy cover (Shevtsova et al., 2020). Lidar plots can be used to model structural attributes of vegetation from multi-spectral satellite images and other spatial predictors such as elevation or climate (Coops et al., 2021). 1.5.2 Spaceborne lidar Lidar is a form of active remote sensing that measures vegetation structure by emitting laser pulses and recording the timing and magnitude of the returning pulse. Vegetation height and its variation can be measured directly from the return pulse, and additional attributes such as biomass, or basal area can be modelled by linking lidar measurements to additional field data (Margolis et al., 2015). Full waveform lidar instruments record a vertical profile of return intensity and can sample multiple layers of the vegetation canopy (Degnan, 2016), whereas single photon-counting systems detect individual photons returned from the surface. Single-photon systems typically have lower energy requirements and higher sampling frequencies than full waveform sensors (Degnan, 2016). For spaceborne lidar platforms, the area illuminated by each pulse (footprint size) can vary between 10-70 m (Coops et al., 2021). Unlike imagery from optical sensors, lidar 14 measurements are sampled along transects following the flight-path of the sensor. The spacing between lidar acquisitions varies from ~100 m for waveform returns, to ~1 m for single-photon profiling instruments (Coops et al., 2021). The GLAS and the Global Ecosystem Dynamics Investigation (GEDI), sensors both record full waveform returns, with acquisitions every 170 m and 60 m, respectively. Over its initial deployment between 2018 and 2020, the GEDI instrument collected 10 billion lidar plots, however it only operated in tropical and temperate forests, with spatial coverage between 51˚ north and south (Dubayah et al., 2020). The ATLAS instrument onboard the ICESat-2, was designed to measure sea ice thickness in the polar regions and was operational between 2018 and 2021 (Markus et al., 2017). ATLAS is a single-photon counting instrument with six lasers in the green wavelengths (532 nm). While not the primary mission objective, ICESat-2 also provides robust measurements of terrain and vegetation height (Feng et al., 2023). These spaceborne lidar data have been combined with spectral imagery to generate global and regional models of forest height (Lefsky, 2010; Liu et al., 2021; Malambo et al., 2023; Simard et al., 2011) and biomass (Margolis et al., 2015; Narine et al., 2019; Nelson, 2010). 1.6 Challenges and opportunities A key challenge in high-latitude remote sensing is aligning observations across different spatial and temporal scales (Beamish et al., 2020). Woodcock & Strahler (1987) discuss the impact of spatial resolution on the accuracy of image classification. In general, when the target features in the image are smaller than the size of the pixel, differences between 15 pixels are obscured which increases confusion between mapped classes. This applies to the transitional nature of the forest-tundra, where multiple land cover types are often present in a single pixel (Nill et al., 2022). For example, remote sensing models of northern tree cover can overpredict sparse coniferous canopies due to the presence of green understory shrubs, which have greater horizontal cover and photosynthetic leaf area, thus contributing more to the reflectance of the pixel (Ranson et al., 2011; Timoney & Mamet, 2020). Mixed spectral signals can also be confounded by the more rapid expansion rates of northern shrubs compared to trees (Dial et al., 2016). The duration of the satellite image archive also makes it difficult to detect gradual change in forest cover at the northern margin of the ecotone. Currently, the Landsat series of satellites provides the longest continuous record of 30 m surface reflectance measurements dating to 1985 (Wulder et al., 2022). However, even with multi-decadal datasets, northern spruce trees may take many decades (~70 years) to establish and reach a height and density detectable in a 30 m or 500 m pixel (Timoney et al., 2019). Satellite records from MODIS, with archived images from 2000 may be even less sensitive to small changes in forest-cover at the northern ecotone limit (Huete et al., 2002) To address issues in developing remotely sensed models of long-term vegetation change, several solutions have been proposed: 1) expanding high-quality reference and validation datasets and making them available in open-access repositories, 2) standardizing remotely sensed image processing by sharing software and code, and 3) comparing observations across platforms, sensor types, spatial resolutions over different time-scales (Beamish et al., 2020; Myers-Smith et al., 2020; Orndahl et al., 2025). 16 1.7 Research questions and dissertation overview The primary goal of this dissertation is to improve our understanding of change in vegetation structure and implications for carbon storage across the Canadian forest-tundra ecotone using remotely sensed data (Figure 1-1). In Chapter 2, I will describe the general characteristics and climate of the forest-tundra ecotone across Canada, as well as the location of field data collected in Chapter 3. I also describe the broad-scale remote sensing datasets used throughout this dissertation, including Landsat Best Available Pixel composites and the ICESat-2 Land and Vegetation height product. In Chapter 3, I ask how above and below ground carbon storage varies across tree and shrublines. I use drone imagery to map fine-scale vegetation structure and topography across northern tree and shrublines in the Northwest Territories, Canada and link these data to measurements of soil organic carbon storage. In this chapter I also show how different Landsat spectral indices capture fine-scale vegetation structure at the treeline. In Chapter 4, I expand the spatial extent of my research to the entire northwestern forest-tundra ecotone in Canada. I use Landsat imagery and ICESat-2 lidar data to model vegetation structure continuously across this 183-million-hectare study region. Chapter 5 builds on this modeling framework, and examines change in vegetation structure using 40 years of Landsat time-series data. Specifically, I explore whether change in structure is consistent with northward advance of the forest biome across Canada. In Chapter 6, I use historic air photos dating to the 1930’s and 1970’s to examine how well modeled changes in vegetation structure from the satellite record aligns with long-term change in tree and shrub cover. Key research questions summarizing each of the four data chapters are listed below (Figure 1-1): 17 Chapter 3: How does fine-scale soil carbon storage vary with vegetation structure across northern tree and shrublines? Chapter 4: How can vegetation structure be mapped across the Canadian forest-tundra ecotone using Landsat and ICESat-2 data? Chapter 5: How has vegetation structure changed and are changes consistent with northward movement of the forest limit? Chapter 6: How well does the vegetation change from the satellite record correspond with long-term change mapped using high spatial resolution historic air photos? Specific datasets and context for each research question are described in the relevant chapter. Finally, in Chapter 7 I synthesize the main research findings, innovations and directions for future research. Figure 1-1: Diagram summarizing research goals, key remote sensing datasets and spatial scales of analysis for the four data chapters. 18 Chapter 2: Study area and data 2.1 Canadian forest-tundra ecotone The study area explored in my dissertation covers 183 million hectares of the Canadian forest-tundra ecotone (FTE) within Yukon Territory, Northwest Territories, Nunavut, northern Quebec and the Newfoundland-Labrador Peninsula (Figure 2-1). This region traverses the traditional territories of many Indigenous peoples including the Dene, Inuit, Cree and Métis peoples. In particular, a significant portion of the FTE is within Inuit Nunangat – the Inuit homeland which includes the Inuvialuit, Nunavut, Nunavik and Nunatsiavut regions (RCGS, 2018a). The Denendeh and Eeyou Istchee regions also encompass large areas of northern boreal forest below of the treeline, and are home to many Dene and Cree communities (RCGS, 2018). Climate, vegetation, and soils vary across the study area and are broadly controlled by latitude, proximity to the ocean and glacial history (Ecosystem Classification Group, 2007, 2007, 2012). The overall climate is cold with mean annual temperatures below freezing (Wang et al., 2016). Between 1948 and 2018 air temperatures have warmed by approximately 1-3˚C, with more rapid warming in the northwest (Vincent et al., 2020). Precipitation has also increased at a rate of 1.5-2% per decade, with a greater proportion falling as rain (Box et al., 2019). The width and position of the ecotone varies in response to climate, elevation and soil conditions (Timoney et al., 1992). The ecotone is narrowest in the northwest at approximately 100-115 km, and widens in the southeast to its greatest width of 228-338 km (Timoney et al., 1992). Permafrost is continuous in the northwestern part of the study area, but both continuous and discontinuous permafrost are present in the central and eastern parts of the study area. 19 The Taiga Plains, Taiga Shield and Southern Arctic ecozones make up the majority of the study area. The Taiga Plains and Taiga Shield ecozones are sometimes referred as “the land of little sticks”, as short, cool growing seasons and long, cold winters severely limits vegetation growth and productivity (Ecosystem Classification Group, 2007). The northern boreal forest is composed of hardy, frost-resistant species including white spruce (Picea glauca), black spruce (Picea mariana), Jack pine (Pinus banksiana), and trembling aspen (Populus tremuloides) that established following the retreat of the Wisconsin ice sheet in northern Canada approximately 11,000-15,000 years ago (Brandt, 2009; Ritchie, 1984). The Taiga Plains are located in the northwest part of the study area, white spruce and black spruce are the dominant species at the treeline (Timoney et al., 1992). The boundary between the Taiga Plains in the west and the Taiga Shield in the east is defined by the extent of Precambrian granite that is characteristic of the Shield (Ecosystem Classification Group, 2008). The width of the ecotone increases across the Taiga Shield and drops southward in latitude due to shallow and nutrient poor soils present across the Shield (Timoney et al., 1992). Microsites supporting tree establishment are limited by areas of exposed bedrock and acidic soils that primarily support black spruce and Jack pine at the treeline (Timoney et al., 2019). The Southern Arctic makes up the northernmost part of the study area and is largely treeless, hosting extensive lake and stream networks (Ecosystem Classification Group, 2012). 20 Figure 2-1: Extent of the study domain and major ecozones across the Canadian forest-tundra ecotone. The red point indicates the location of field-sites within the Caribou Hills ecoregion in Northwest Territories. 2.2 Caribou Hills ecoregion In Chapter 3, I use field-data from the Caribou Hills ecoregion in the Northwest Territories (Ecosystem Classification Group, 2012; Figure 2-2). This area is located within the Inuvialuit Settlement Region and is of significance to the nearby communities of Inuvik and Tuktoyaktuk. The Caribou Hills Ecoregion is underlain by continuous permafrost and is a transitional area between the High Subarctic ecozone in the south and the Low Arctic 21 in the north (Ecosystem Classification Group, 2012). This region is lake-rich and is characterized by rolling morainal deposits consisting of till and associated gravel and sand deposited by glacier ice (Rampton, 1988). Vegetation across the ecoregion shifts from open white spruce woodlands and low-shrub tundra, to the tussock-sedge and dwarf-shrub tundra across the Low Arctic (Ecosystem Classification Group, 2012). The dominant tree species at my sites was white spruce. Upright shrub species included willow (Salix spp.), birch (Betula spp.) and green alder (Alnus viridis). The most common understory herbs and dwarf shrubs Ire bog bilberry (Vaccinnium uliginosum), kinnikinnick (Arctostaphylos rubra) and crowberry (Empetrum nigrum). 22 Figure 2-2: Location of northern tree and shrubline sites (n = 11) along the Inuvik-Tuktoyaktuk Highway. Oblique photos A) and B) show representative sites in the study area. 2.3 Key remote sensing datasets Throughout Chapters 3-6 I used Landsat Best Available Pixel (BAP) composites as the primary source of multispectral surface reflectance data. Annual BAP composites at 30 m spatial resolution were used for the years 1985 to 2021. In Chapters 4 and 5 I combine Landsat spectral time-series with spaceborne lidar data from ICESat-2. Additional field-based and remotely sensed datasets are described in the relevant chapters. 23 2.3.1 Landsat Best Available Pixel (BAP) composites Analyses of optical satellite imagery are challenged at high latitudes by cloud-cover, long periods of darkness and short growing seasons (Beamish et al., 2020). Image composites can improve data continuity by combining high-quality pixels from multiple images across a given time period (White et al., 2014). Landsat BAP composites are cloud and gap-free mosaics of surface reflectance generated at an annual scale across Canada using images acquired by Landsat-5 Thematic Mapper (TM), -7 Enhanced Thematic Mapper Plus (ETM+) and -8 Operational Land Imager (OLI) (Hermosilla et al., 2022b). Composites are created from multiple images acquired over a single growing season, and pixel scoring functions are used to select the “best” pixel based on the sensor, acquisition day, distance to clouds, cloud shadow and atmospheric opacity (White et al., 2014). The scoring function preferentially selects images acquired close to August 1, which is the assumed peak of the growing season (Hermosilla et al., 2016). The scoring function also penalizes pixels with clouds, haze, cloud shadows as well as pixels impacted by the Landsat-7 scanline corrector failure (Hermosilla et al., 2022b). If there is no suitable pixel for a given year, “proxy values” are interpolated using a breakpoint detection process called Composite2Change (C2C) (Hermosilla et al., 2016). The C2C algorithm removes “noisy” points from the pixel time-series and finds breakpoints representative of abrupt disturbance. A series of linear trends are then fit between each breakpoint and data gaps can be filled based on modeled values for every year in the time-series. The C2C algorithm has been used to generate spatially and temporally exhaustive surface reflectance measurements over the entirety of Canada’s 24 land area (Hermosilla et al., 2016). These data have been used as the foundation for modeling changes in forest structure (Matasci, et al., 2018a), land cover (Hermosilla, et al., 2022b) and tree species (Hermosilla, et al., 2022a) at the national scale. 2.3.2 ICESat-2: Land and Vegetation Height product ICESat-2 was launched by NASA in 2018 to map the Earth’s ice caps, vegetation and terrain (Markus et al., 2017). The platform carries the Advanced Topographic Laser Altimeter System (ATLAS) instrument, which is a single-photon counting system with a green (532 nm) laser (Markus et al., 2017). The laser is split into paired strong and weak beams spaced 3 km apart (Markus et al., 2017). Each pulse illuminates the ground every 70 cm along the satellite’s path and corresponds to a circular ground footprint 11 m in diameter (Magruder et al., 2021). Up to 16 photon returns can be detected from the strong beam, and four from the weak beam (Neumann et al., 2019). Using the strong beam, reflective surfaces like ice sheets are expected to return approximately 9 photons per pulse, but vegetated surfaces may only return one or two photons (Neuenschwander et al., 2022b). Advanced noise detection algorithms are needed to separate background photons scattered by the atmosphere from signal photons returned from the Earth’s surface (Neuenschwander & Pitts, 2019). The ATL08: Land and Vegetation Height product uses the Differential, Regressive and Gaussian Adaptive Nearest Neighbour (DRAGANN) classification method (Neuenschwander & Pitts, 2019). The algorithm assumes that signal photons will be clustered in space compared to randomly distributed noise returns (Neuenschwander et al., 2022a). The number of neighbours within a search distance of 25 each return are used to classify signal and noise photons based on an adaptative threshold in nearest-neighbours (Neuenschwander et al., 2022a). Signal photons are further classified as either top of canopy, canopy or ground using the relative heights of surrounding signal returns. To improve the signal to noise ratio for vegetation, Neuenschwander et al., (2022b) recommend using data acquired at night and by the strong beams only. Snow cover can also increase the number of photon returns by increasing the reflectivity of vegetated surfaces, but may introduce errors if the elevation of the ground is not correctly identified (Neuenschwander et al., 2022b). Acquisitions from peak growing season (mid-summer) are generally preferred in the literature due to greater canopy cover and snow-free conditions (Mulverhill et al., 2022; Neuenschwander et al., 2022b). Once the photons are classified, they are grouped into 100 m and 20 m segments along the satellite’s ground track. Summary height statistics are calculated for ground and canopy photons within each segment (mean, median, standard deviation and height percentiles). Within 20 m segments, a minimum of 10 signal photons and three canopy photons are required to estimate summary height statistics (Neuenschwander, 2022). 26 Chapter 3: Vegetation structure and soil organic carbon storage across the northern tree and shrublines in NWT Canada1 3.1 Introduction Half of the world’s soil organic carbon (SOC) is stored at high latitudes and is vulnerable to release via permafrost thaw and microbial decomposition (Keuper et al., 2020; Tarnocai et al., 2009). While climate warming is associated with greater primary productivity, plant growth can also increase the rate of SOC decomposition through positive rhizosphere priming effects (Friggens et al., 2025; Xu et al., 2024). These effects are driven by the introduction of plant roots to soil, which provide fresh carbon inputs that can increase microbial activity and emission of CO2 and CH4 (Huo et al., 2017). Under experimental conditions, root activity can increase soil respiration in permafrost soils by 12-31%, potentially representing a large positive feedback to climate warming (Friggens et al., 2025; Keuper et al., 2020). Models of carbon flux suggest that high latitudes are currently net carbon sinks (McGuire et al. 2012; Virkkala et al. 2021). However, there is a high level of uncertainty in these projections, as they typically do not include interactions between plants and soils (Keuper et al., 2020). The effects of expanding tree and shrublines on soil carbon storage have been explored using space-for-time substitution by comparing SOC above and below the current treeline (Wilmking et al., 2006). Recent work has shown that soil carbon storage can be several times greater in tundra soils compared to nearby forested sites, prompting 1 This chapter has been adapted from the following publication: Travers-Smith, H. et al. Vegetation structure and soil organic carbon storage across northern forest-tundra ecotones in continuous permafrost. Arctic Science 11, 1–18 (2025). 27 calls for additional field studies over a greater variety of treeline types (Clemmensen et al., 2021a; Parker et al., 2015). To date, many studies comparing soil carbon storage across treelines have been conducted in alpine regions without permafrost (Clemmensen et al. 2015, 2021; Hartley et al. 2012; Parker et al. 2015, 2021; Sørensen et al. 2018), or in discontinuous and sporadic permafrost (Devos et al. 2022; Wilmking et al. 2006; Ziegler et al. 2017). The relative lack of treeline studies conducted in continuous permafrost limits our ability to predict the consequences of vegetation change on soil carbon storage across large parts of the circumpolar region (see Rodionov et al. 2007; SieIrt et al. 2015; Sistla et al. 2013; Wojcik et al. 2019). In addition to the effects of vegetation on soil carbon, topography also influences soil moisture, temperature and nutrient accumulation over the landscape (Kokelj et al., 2007; Zona et al., 2011). These factors have indirect effects on soil carbon decomposition and storage across treelines. Drones can be used to generate three-dimensional models of both terrain and vegetation structure at high spatial resolution (Fraser et al. 2016). Thus, integrating field observations with remotely sensed data can provide insight into links between vegetation structure and topographic controls on carbon storage (Siewert et al., 2015). In addition, drone imagery can be used to scale between field measurements and medium spatial resolution (10-100 m) satellite imagery to infer vegetation dynamics over larger areas (Wang et al., 2020). In this analysis, I investigated relationships between SOC storage, microtopography and vegetation structure across treelines underlain by continuous permafrost in northwestern Canada. I conducted drone surveys and collected vegetation and soil data across 11 28 transects representing local transitions from spruce woodlands to shrub and tussock tundra. For this study, “local” treelines represent localized gradients of forested to un-forested vegetation, which fall below the continental-scale treeline defined previously. Transects were grouped into three treeline types using Landsat spectral indices and site-level topographic variation. At each transect I collected samples of the organic soil horizon (n=155) and field-surveys of vegetation functional groups (n=55). High-resolution drone surveys of each transect were used to map vegetation height and microtopography, which were spatially associated with the plot-level data. I used these data to explore whether increasing vegetation height and forest density are correlated with decreasing SOC storage within, and across treeline types. To understand how Landsat data captures fine-scale treeline dynamics, I also linked 30 m spectral indices with canopy height, tree density and modeled productivity derived from the drone surveys. 3.2 Methods 3.2.1 Overview This section provides an overview of the workflow with additional detail provided in the sections that follow (Figure 3-1). Data collection was divided into three parts, representing nested spatial scales of analysis, 1) fine-scale soil and vegetation sampling within 11 treeline sites, 2) intermediate scale drone imagery (1 cm spatial resolution) of each treeline site, and 3), landscape scale spectral and topographic indices (30 m spatial resolution). First, I used medium resolution Landsat spectral indices and topographic metrics to group transects into three local treeline site types (Moist Tundra, Mesic Woodland and 29 Submesic Woodland) based on statistical clusters. At each treeline site (n=11), I collected 15 soil samples along 200 m transects that crossed a local treeline. I used carbon content (%), soil bulk density (kg m-3) and the depth of the organic horizon to estimate SOC (kgC m-2) for each sample (n=155). Data from vegetation plots (n=55) collected along each transect were used to characterize differences in plant communities between treeline types. In the second part of data analysis, I linked terrain and vegetation structure derived from drone imagery to SOC measured in the field. I used these data to 1) compare mean SOC among treeline types and 2) model SOC as a function of microtopography, tree stem density and canopy height along transects. Finally, I associated the drone models of vegetation structure with Landsat vegetation indices to understand how well they capture fine-scale vegetation dynamics at the treeline. Figure 3-1: Flow chart of the primary steps in the data analysis and associated outputs. As noted in the key, the color and shading of the boxes indicate which data source and scale was used to derive each output. 30 3.2.2 Study area and site selection Field sampling was conducted at undisturbed treeline sites adjacent to the Inuvik-Tuktoyaktuk Highway, north of Inuvik (Figure 3-2). The study site is within the Caribou Hills ecoregion (Ecosystem Classification Group, 2007). Average annual air temperature recorded at Inuvik between 1970-2016 was -8.6˚C (Environment Canada, 2019). Between 1926 and 2016, mean annual temperatures and summer temperatures have warmed by 3.5 ˚C and 1.9 ˚C, respectively (Lantz et al., 2019). In addition to climatic changes, the construction (2014-2017) and ongoing use of the gravel highway impacts the surrounding tundra environment by driving earlier snowmelt (Hammar et al. 2023), and increasing soil nutrient availability and moisture (Gill et al. 2014). Widespread shrub expansion has also been documented throughout this area (Fraser, Lantz, et al., 2014; Lantz et al., 2010; Moffat et al., 2016). While the northern position of the treeline has remained stable over time (Timoney & Mamet, 2020), stand density has increased at sites below the treeline (Lantz et al., 2019; Travers-Smith & Lantz, 2020). I used WorldView-2 imagery from the satellite base maps in ArcGIS Pro to identify local treelines accessible from the highway. Treelines were defined as open woodlands transitioning to areas without any trees, or only a few stems visible. I restricted sampling to undisturbed sites north of the 1:1 forest-tundra boundary defined by Timoney et al (1992) and did not sample areas where the trees were obviously limited by water features or steep topography. Sites were selected to represent a range of topographic and environmental conditions and were placed at least 200 m apart from each other (Figure 3-2). 31 3.2.3 Field data collection All soil and vegetation surveys were conducted between July 15-26, 2023. At each site (n=11) I delineated a 200 m transect crossing the local treeline and sampled five 1 m2 and 3 m2 vegetation plots at 50 m intervals along the transect (Figure 3-2). At each of the five plots, a single observer recorded the percent cover of plant functional groups within nested 1 m2 and 3 m2 and quadrats. In the 3 m2 quadrat I estimated the percent cover of tree and upright shrubs (including Betula spp., Alnus spp., Salix spp.). In the 1 m2 quadrat I estimated percent cover of dwarf shrubs (including Empetrum spp., Vaccinium spp., Arctostaphylos spp.) herbs, graminoids, moss, lichen and litter. At each plot I collected triplicate soil samples (A, B, C). Sample B was always placed in the center of the 1 m2 vegetation plot, while samples A and C were randomly spaced 2-3 m apart on either side of each vegetation plot (Figure 3-2). Soil samples (n=155) were extracted as a monolith of the unfrozen organic horizon to the depth of the mineral horizon, or to permafrost using a small shovel and knife. I sampled a vertical profile from the extracted material representing a “slice” of the active layer, which was later homogenized in laboratory analysis. The pit dug to extract the monolith was then used to measure the thickness of the organic layer (cm) measured either to the mineral layer or to permafrost. Samples of the organic horizon were subsequently frozen and shipped to the Stable Isotope Facility at the University of British Columbia for bulk density and carbon content analysis. At the location of each soil sample, I also recorded the depth of the active layer and litter layer. To locate soil samples within the drone imagery, soil pits were filled and covered by brightly colored discs that were easily discernable in the 1 cm resolution imager 32 Figure 3-2: Map of the study area in northwestern Canada. Transects were established along the Inuvik-Tuktoyaktuk highway (left). At each transect (n=11) I acquired soil samples (n=15) and concurrent drone imagery at 1 cm spatial resolution (right). The image to the right shows the location of soil samples at the transect marked with an arrow. The inset map (top-left) shows the study area in relation to the continental 1:1000 tree limit and forest-tundra ecotone mapped by Timoney et al. (1992). 3.2.4 Laboratory analysis of soil C and N content After collection in the field, soil samples were kept frozen until analysis of carbon and nitrogen content. Samples were completely dried in an oven at 60˚C for three days. After drying, large twigs and roots were removed and discarded. The soils were sieved to <2 mm and homogenized using a mortar and pestle. The bulk density was calculated as the weight of the dry soil divided by the volume of the sample. A small amount of soil (~5 33 mg) was then taken from the homogenized sample and sent to the Stable Isotope Facility for analysis. Samples were measured in an elemental analyzer (Elementar Vario EL Cube; in C, N mode, Hanau, Germany) for carbon and nitrogen content (C% and N%). SOC stocks were calculated using laboratory and field measurements in the following equation (Parker et al. 2021; Devos et al. 2022): 𝑆𝑂𝐶 (𝑘𝑔𝐶 𝑚−2) = 𝐶 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 (%) ∗ 𝐵𝑢𝑙𝑘 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 (𝑘𝑔 𝑚−3) ∗ 𝑂𝑟𝑔𝑎𝑛𝑖𝑐 ℎ𝑜𝑟𝑖𝑧𝑜𝑛 𝑑𝑒𝑝𝑡ℎ (𝑚) The final SOC measurement is an estimate of the amount of carbon stored in the unfrozen organic horizon at the time of sampling, per unit area. SOC stocks may be underestimated for samples where I was not able reach the mineral horizon due to the presence of permafrost (n = 36/155). 3.2.5 Drone surveys & photogrammetry For each transect, I used RGB aerial images from a DJI Mavic 2 Pro to derive models of topography and vegetation height. The drone was flown at 40 m altitude, producing an image resolution of 1 cm. Surveys were centered on the treeline transects and were approximately 250 m in length and 100 m in width (Figure 3-2). The Mavic 2 Pro was equipped with an RGB camera and flown in a grid pattern. The camera was triggered at equal distance intervals with front and side overlap of each image 80% and 70%, respectively (Fraser et al., 2017). I used Agisoft Metashape Professional (v.1.8.3) to generate dense point clouds for each area surveyed. Images were aligned using a key point limit of 40,000 and a tie point limit of 4,000 and were optimized using adaptive camera model fitting (Fraser et al., 2016). In the image alignment step I used the “Low” accuracy alignment as higher “accuracy” produced unrealistic curvature at the edges of 34 the survey. This resulted in fewer tie-points but removed “doming” effects in the Digital Elevation Model (DEM). Point clouds were processed further in R Statistical Software (v.4.4.1; R Core Team, 2022) using the lidR package (Roussel et al., 2020). The full point clouds had point densities between 2200-2700 points per m2. As no ground control points were used to improve the absolute georeferencing of the data, the expected horizontal offset of the point clouds was 5-10 m based on comparison of the orthomosaics with georeferenced basemaps. The point clouds were classified into vegetation and terrain classes using the cloth simulation function, with a class threshold of 0.25 m, the default cloth resolution and a cloth rigidness parameter of three (indicating more rigid). A DEM was generated at 1m spatial-resolution by interpolating the ground classified points which had an approximate density of 55-60 points per m2. From the DEM, I used the raster package in R (Hijmans, 2022) to calculate slope, roughness and Topographic Position Index at 1 m resolution. Points classified as vegetation were normalized to the DEM. Canopy Height Models (CHM’s) were generated at 10 cm resolution using the maximum height of vegetation within each pixel. From the CHM, I mapped tree stems >2.5 m at each site using a local maximum focal function with a window size of 2 m. The 2.5 m threshold was selected to conservatively estimate the number of mature white spruce stems at the study site. 3.2.6 Characterizing treeline types The 11 treeline transects were grouped in three site types using a DEM and Landsat spectral indices at 30 m spatial resolution. Clustering was performed using a k-means algorithm using four Landsat indices, EVI, Tasseled Cap Greenness (TCG), Tasseled 35 Cap Brightness (TCB) and Tasseled Cap Wetness (TCW), and variation in elevation across each transect (Table 3-1). Variation in elevation and slope was calculated from a 30 m resolution DEM to match the spectral indices. Clustering was performed in R using the “cluster” package (Maechler et al. 2022), and all data were standardized before analysis. To visualize the cluster centroids, I used a Principal Component Analysis (PCA) to reduce the input variables to two dimensions and calculated the variance explained by each dimension using the “factoextra” package (Kassambara and Mundt, 2020). Table 3-1: Landsat and topographic variables used to define treeline types within a k-means clustering algorithm. Variables at 30 m resolution were aggregated to the transect scale representing an area approximately 250x100 m (~27 pixels). Variable name Site scale aggregation Tasseled Cap Wetness Mean of 30 m pixels Tasseled Cap Greenness Mean of 30 m pixels Tasseled Cap Brightness Mean of 30 m pixels Enhanced Vegetation Index Mean of 30 m pixels Slope mean Mean of slope in 30 m pixels DEM range Range in elevation in 30 m pixels DEM standard deviation Standard deviation of elevation in 30 m pixels 3.2.7 Drivers of soil carbon storage across treelines I assessed differences in SOC storage, C:N ratio, active layer depth, litter layer depth, organic layer depth and bulk density among the three treeline site types using a one-way ANOVA and Tukey HSD pairwise comparisons. Comparisons were performed using soil samples with less than 30% mineral fraction (n=145), as samples with high mineral 36 content came from cryoturbated areas with frost boils and did not represent the organic horizon (Kokelj et al. 2007). In some cases, the one-way ANOVA indicated a statistically significant (p < 0.05) difference in mean values, but the Tukey pairwise comparisons did not. I first report p-values for the one-way ANOVA and then include mean values for each site type with the associated p-values for specific pairwise comparisons. To evaluate differences in SOC across tree stem density, I used maps of tree stems (>2.5 m) to calculate stem density within circular 30 m plots of each soil sample. A Generalized Additive Model was used to visualize the relationship between SOC and stem density across all samples. Next, I used Ordinary Least Squares regression models to compare fine-scale vegetation and topographic drivers of SOC storage (kgC m-2) within treeline types. I fit a global model using all soil samples as well as models within each treeline type. Histograms of each input variable were examined prior to modeling, and variables exhibiting right-skewed distributions were log-transformed to approximate a normal distribution. Input variables representing vegetation structure included the mean, maximum and standard deviation in height using the drone-derived CHM. For each soil sample, I generated a 3 m circular buffer and summarized height measurements within the buffer. Topographic metrics, including slope, aspect, Topographic Position Index (TPI), and roughness were derived from the 1 m DTM and were extracted from the pixel intersecting with the location of the soil sample. Stepwise variable selection was used to minimize AIC and fit parsimonious models. I used the statistical significance of model coefficients to assess the relative importance of vegetation structure, litter, and topographic controls on soil carbon storage. 37 3.2.8 Landsat vegetation indices I evaluated the sensitivity of Landsat spectral indices (TCB, TCW, TCG, EVI; Table 1-1) to fine-scale variation in vegetation structure from drone imagery at my study sites. First, I derived Excess Greenness (ExG) index from the RGB drone imagery as a proxy for multispectral greenness indices. The index is calculated as: 2*Green - (Red + Blue) (Richardson et al. 2007). In northern boreal forests, ExG was found to be the strongest discriminator between green vegetation and background signals (Fraser et al. 2017). I calculated ExG from the drone orthomosaics and aggregated to 30 m spatial resolution using the mean value of each pixel. Stem density was calculated as the number of stems >2.5 m within each Landsat pixel, divided by pixel area (900 m2). Maximum height was calculated by aggregating the CHM to 30 m using the maximum value within each pixel. I used Generalized Additive Models (GAMs) and extracted model R2 and root mean square error (RMSE) for each combination of structural metric and spectral index. GAMs were fit with a smooth term for the predictor variable using cubic regression splines and three knots (which determine the type of smoothing algorithm and the degree of smoothing). As this positional error of drone orthomosaics is less than or similar to registration accuracy of Landsat (one half pixel; < 15 m) it is not expected that the spectral comparison and scaling used in this work was impacted (Fraser et al. 2017; Storey et al. 2014). 38 3.3 Results 3.3.1 Characterizing treeline types Treeline transects (n=11) were clustered into ecological types based on variation in topography and Landsat spectral indices across each transect (Figure 3-3). The first two dimensions of the Principal Component Analysis explained 89% of the variation in the input variables, with the first dimension reflecting variation in slope and elevation and the second dimension reflecting differences in TCG, TCW, and EVI. Based on these characteristics, in the following text I define three treeline types: Moist Tundra (MT), Mesic Woodland (MW) and Submesic Woodland (SW). The first treeline type (MT) contained four transects which with high spectral greenness and wetness and low topographic variation (Figure 3-4). Vegetation communities were characterized by willow (Salix spp.) and green alder (Alnus viridis subsp. fructicosa), with 29.5% median percent cover (Figure 3-5). Median stem density was also low compared to other sites with 14 stems per hectare. The second treeline type (MW) contained four transects which had low spectral greenness and wetness (Figure 3-4). This site type was characterized by moderate tree stem density (65 stems per hectare) with lower shrub cover (12.5%) and a moss and graminoid understory of 30% and 17.5% cover, respectively (Figure 3-5). Topographic relief was similar to the MT sites with 1.88m of elevation change. The final treeline type (SW) contained three transects with high topographic variation with moderate spectral greenness and wetness (Figure 3-4). The median change in elevation was 3.44 m with slopes 47% greater than the other two treeline types. Similar to the MW sites, SW sites also had high median tree stem density (71 stems per hectare), but compared to the other woodland site, the 39 understory was composed primarily of dwarf shrubs (53% median cover), instead of mosses and graminoids, 5% and 12%, respectively (Figure 3-5). Figure 3-3: Oblique imagery of the three treeline types. The yellow arrows show the approximate direction of transects across local treelines (note that the arrows do not show the exact location of the transects). 40 Figure 3-4: Boxplots of Landsat indices and topographic variation within three landscape clusters (Moist Tundra, Mesic Woodland and Submesic Woodland), classified using a k-mean clustering algorithm. Note that stem density was not used to cluster treeline types but is shown to contextualize differences in treeline types. 41 Figure 3-5: Cover of plant functional groups within 1 m2 and 3 m2 plots (n=55) among the three treeline types (Moist Tundra, Mesic Woodland and Submesic Woodland). Note that percent cover can exceed 100 as overlapping layers of plants within the same functional group were summed together. 3.3.2 SOC storage across treelines Across all treeline types mean SOC pools were 15.6 ± 1.4 kgC m-2 (Table 3-2). Mean SOC (± standard error) was significantly different (p = 0.04) across treeline types. Tukey HSD pairwise comparisons indicated that mean SOC was not significantly different between MW sites (16.2 ± 0.8 kgCm-2) and MT (15.8 ± 1.2 kgCm-2; p = 0.12), but was slightly lower in SW compared to MW (14.5 ± 1.3 kgCm-2; p = 0.06). Mean litter layer depths were significantly different (p = 0.05) among sites, and were higher in SW (0.8 cm ± 0.1) compared to MW sites (0.4 cm ± 0.1; p = 0.06). The C:N ratio was also significantly lower (p = 0.001) within MW, (20.3 ± 0.6) compared to MT (25.3 ± 1.1; p = 0.005). Mean 42 soil C content (%), organic matter depth and bulk density did not differ significantly among treeline types. Table 3-2: Mean (± standard error) carbon storage parameters summarized by treeline type. Bold font indicates where there were significant (p < 0.05) differences between treeline types from the ANOVA. Treeline types that share the same letter were not different from each other within Tukey pairwise comparisons. Moist Tundra (n=46) Mesic Woodland (n=59) Submesic Woodland (n=40) Global (n=145) Active layer depth (cm) 41.0±1.98 49.2±2.77 48.2±1.89 46.3±1.41 C:N Ratio 25.3A±1.16 20.3B±0.62 22.6AB±0.61 22.5±0.50 Organic horizon depth (cm) 17.6±1.61 14.9±0.63 13.4±1.08 15.3±0.65 Litter layer depth (cm) 0.6AB±0.13 0.4B±0.08 0.8A±0.16 0.6*±0.07 C content (%) 38.1±0.57 36.1±0.53 38.0±0.77 37.3±0.46 Bulk density (kgm-3) 256.9±9.91 306.0±8.71 288.1±11.5 285.5±5.92 SOC (kgCm-2) 15.8AB±1.18 16.3B±0.77 14.5A±1.34 15.6±0.61 Across all soil samples, SOC storage increased slightly with increasing tree stem density (Figure 3-6; RMSE = 7.28 kgC m-2). However, the range in SOC storage was much greater at lower stem densities due to greater variation in organic soil depth (Figure 3-6). These patterns were consistent between all three treeline types. Median SOC storage 43 increased slightly and variation in SOC decreased with increasing stem density (Figure 3-6). Figure 3-6: Variation in SOC storage (kgC/m2) with tree stem density, calculated as the number of stems > 2.5m within 30m of each soil sample. Panel A) shows SOC across all treeline types (n=145); point color represents sample bulk density and size represents depth of the organic soil layer. The y-axis of A) has been logarithmically transformed. Panel B) shows variation in SOC across four classes of increasing stem density within the three treeline types. Ordinary least squares regression models indicate the relationship between SOC storage and fine-scale vegetation structure and microtopography differed among treeline types (Table 3-3). The intercept-only model (no covariates) was the best fitting global model using all samples, indicating that there were no consistent predictors of SOC storage across all site types. Mean vegetation height exhibited a significant positive relationship 44 with SOC for MT sites (p = 0.04) and non-significant relationships in the woodland sites (MW and SW; Table 3-3). Topographic Position Index showed a negative relationship (p = 0.02) with SOC in MT, indicating that topographic depressions had higher SOC values. Terrain roughness also exhibited negative relationship with SOC in MW (p = 0.01), indicating that samples in areas with less topographic variation had higher SOC. Table 3-3: Variables included in models predicting Soil Organic Carbon (SOC) after step-wise variable selection. The global model included all soil samples with < 30% mineral content (n = 145). Separate linear models were also fit for each treeline type. The model coefficient is included in brackets and symbology indicates the significance level of the variable (p < 0.001 = ***, p < 0.01 = **, p < 0.05 = *, p < 0.1 = ^). Model Intercept Vegetation variables Topographic variables Global 15.63 None None MT (n = 46) 29.72 Mean height (+2.40)* Roughness (+3.84), TPI (-75.15)* MW (n = 59) 9.04 Mean height (+1.51)^ Roughness (-4.57)* SW (n = 40) 16.08 Litter layer depth (-1.97) None 3.3.3 Landsat vegetation indices Overall, Landsat indices at 30 m resolution exhibited linear and non-linear relationships with forest structure derived from high-resolution drone surveys (Figure 3-7). ExG showed a moderate positive relationship with Landsat EVI (r2 = 0.33, RMSE: 18.19) and TCG (r2 = 0.31, RMSE: 18.39), two measures linked to vegetation productivity. However, TCW showed the strongest positive associations with ExG (r2 = 0.54; RMSE: 15.0). Canopy height and stem density showed weaker relationships with spectral indices with r2 values ranging from 0.02-0.35. With the exception of TCW, all Landsat indices showed negative 45 relationships with tree stem counts. TCB was the best predictor of tree stem count (r2 = 0.32, RMSE: 7.03), with lower TCB associated with higher tree stems. TCG was the best predictor of maximum canopy height (r2 = 0.35, RMSE: 2.23) and all Landsat spectral indices exhibited negative relationships with maximum canopy height. Figure 3-7: Relationships between Landsat spectral indices (Enhanced Vegetation Index, Tasseled Cap Brightness, Greenness and Wetness) with drone metrics (Excess Greenness (ExG), per pixel tree stem counts and maximum canopy height). RMSE and r2 values are extracted from Generalized Additive Models. Red plots show the best single Landsat spectral index to predicts each drone variable. 46 3.4 Discussion In this research, I linked vegetation structure and below-ground carbon storage across distinct treeline types underlain by continuous permafrost in the Northwest Territories, Canada. Overall, SOC stocks increased slightly with increasing tree stem density located within 30 m of soil samples. However, across treeline site types the global linear model did not show any consistent predictors of SOC storage related to vegetation height and microtopography for fine-scale (3 m) plots. This contrasts with observations across a number of alpine treelines at similar spatial scales (Clemmensen et al. 2021; Kemppinen et al. 2021; Parker et al. 2015), and in discontinuous permafrost (Wilmking et al. 2006), where SOC stocks were significantly greater in tundra versus forest sites. My findings suggest that potential losses of soil carbon in the upper organic horizon due to increasing tree density may be mediated by the effects of topography, which influence soil moisture and soil temperature at fine-spatial scales (Pichler et al. 2022; Zona et al. 2011). Instead of the direct effects of vegetation expansion, permafrost thaw and active layer deepening may be more significant drivers of soil carbon losses in this region (Kokelj et al. 2023). Additional studies throughout northwestern Canada and elsewhere should be conducted, as my findings may not apply to other treeline environments in continuous permafrost. The growing availability of high-resolution drone imagery will also support these efforts (Orndahl et al., 2022; Van der Sluijs et al., 2018b). 3.4.1 Variation in SOC across local treelines Overall, there were small differences in SOC storage between local treeline types. Mesic Woodland (MW) sites stored the most SOC (16.3 kgC m-2), Submesic Woodland (SW) stored the least (14.5 kgC m-2) and Moist Tundra (MT) was intermediate (15.8 kgC m-2). 47 This could reflect differences in understory community composition as well as site-level topography. The SW sites showed the greatest topographic variation and the smallest SOC stocks, reflecting well-drained slopes and ridges with drier and warmer soils (Wojcik et al. 2019). In Moist Tundra and Mesic Woodland sites, surface roughness and TPI were significant predictors of SOC storage. These relationships likely reflect associations between microtopography, soil moisture and depth of the organic horizon. Given these findings, future work predicting SOC across treelines should emphasize abiotic factors such as landform classifications (Wojcik et al. 2019), surficial geology (Rampton et al. 1988) and permafrost features (Kokelj et al., 2023b; Robinson & Moore, 2000; Siewert et al., 2015) to expand on the SOC associations observed in this study. Across all samples, and within treeline types, SOC stocks increased slightly with increasing tree stem density. At the MT and MW mean canopy height was also positively related to SOC. While dissimilar from some studies in alpine treelines (Clemmensen et al., 2015; Hartley et al., 2012; Parker et al., 2021), my findings are similar to Devos et al. (2022) who reported larger SOC stocks in forested soils compared to tundra soils across treelines in Norway. Similarly, in a long-term warming experiment Sistla et al (2013) show that increasing tundra plant biomass did not significantly impact SOC stocks in organic soils over two decades. One important factor that could account for these differences is the timing of tree recruitment across treelines. Experimental evidence shows that initial introduction of roots to soils induces decomposition over several growing seasons (Friggens et al., 2025). However, it is unclear if these effects persist over longer time-scales or if new carbon inputs from litter eventually “rebalance” soil carbon pools across the forest-tundra transition (Ziegler et al., 2017). It is likely that future recruitment of trees 48 in the FTE will occur in sites that already have tall-shrubs and other woody plants, potentially reducing the impact of rhizome priming effects. The white spruce treelines sampled here have likely remained stable for long periods of time, due to low recruitment levels and seed viability in this region (Lantz et al., 2019; McLeod, 2002). However, I did not directly measure stand age or vegetation expansion rates in my sampling. Future work could pair SOC measurements with stand age demographics to better understand long-term outcomes for soil carbon pools at the treeline. Additionally, I also observed large variation in SOC stocks at the tundra sites, which adds uncertainty to the direction of change in SOC with increasing tree stem density. This variation was largely due to differences in the depth of the organic horizon (ranging from 2-30 cm), which highlights the significance of site selection and sample size in assessing these relationships. Compared to previous studies, I observed relatively small differences in mean SOC storage between the three treeline types. This could reflect more gradual vegetation transitions in my study area, similarities in the cover of plant functional groups across transects (Figure 3-5), or the presence of permafrost which could decrease the influence of plant communities on soil carbon storage (Hartley et al., 2012; Wagner et al., 2023). However, small changes in mean SOC storage could still result in significant net change in carbon storage if these patterns are consistent over large areas (Parker et al. 2021). My treeline transects were selected to reflect local changes in stem density occurring over decadal scales at existing local treelines. Larger differences in SOC storage may be observed by comparing completely treeless tundra sites to more densely treed boreal sites below the treeline (Devos et al., 2022; Ziegler et al., 2017). 49 Litter layer depth and C:N ratios reflect carbon inputs to the soil as well as decomposition rates. Overall, the woodland sites showed evidence of more rapid carbon turnover, with deeper litter layers and lower C:N ratios ( Table 3-2). Potentially greater decomposition at the woodland sites may result from greater tree cover which is associated with higher ground temperatures and soil biological activity (Cleve et al. 1981). In the Tuktoyaktuk Coastlands, ground temperatures in upland forests are also generally higher than tundra sites throughout the year (Palmer et al., 2012). Taken together, the woodland sites may have maintained consistent SOC pools by balancing more rapid decomposition with higher litter inputs compared to the tundra sites (Zeigler et al., 2017). 3.4.2 Landsat indices reflect vegetation structure at the treeline My analysis also showed that Landsat 30 m spectral indices can capture fine-scale changes in site productivity and forest structure in the FTE, particularly for indices that emphasize the short-wave infrared bands, such as TCB and TCW. While drone-derived ExG values were weakly correlated with Landsat EVI (r2 = 0.33) and Tasseled Cap Greenness (TCG, r2 = 0.31), Tasseled Cap Wetness showed a much stronger positive correlation (r2 = 0.54). The stronger correlation between ExG and TCW likely reflects the ability of ExG to capture differences in moisture content of vegetation and soil (Kauth & Thomas, 1976). Tree stem density and maximum canopy height also showed negative relationships with Landsat spectral indices (Figure 3-7). Notably, TCB and TCG exhibited negative relationships with tree density (r2 = 0.32 and 0.35, Figure 8). These results are likely driven 50 by the influence of shadows and woody stems in higher density stands, which impact spectral mixtures and decrease overall reflectance (Fraser et al. 2014b; Pickell et al. 2016; van der Sluijs, et al. 2023a). Lower spectral greenness at more densely forested sites may also reflect greater moss and lichen cover in the understory, which can increase brightness and decrease greenness (Fraser et al. 2014b). These findings support previous work in the forest-tundra ecotone that highlight poor correspondence between forest structure and spectral greenness indices (Timoney & Mamet, 2020). This indicates that widespread greening trends in the forest-tundra may not coincide with increases in overall vegetation height, or tree stem density (Berner & Goetz, 2021; Loranty et al. 2018; Walker et al. 2021). 3.4.3 Limitations and future directions To improve monitoring of SOC stocks at the northern treeline, future studies should consider changes in both organic and mineral soils. In this analysis I followed similar studies across northern treelines (Devos et al., 2022; Parker et al., 2015) and shrublines (Kemppinen et al., 2021) and use homogenized samples of the organic horizon. However, in the boreal (Bill et al., 2023; Walker et al., 2020) and tundra (Wagner et al., 2023), bulk density and carbon content can vary significantly within 5-100 cm of the soil profile (Walker et al., 2020). I assumed that interactions between vegetation and soil carbon stocks were present within the organic horizon – but it is possible that vegetation structure has greater impacts on SOC stocks deeper in the mineral horizon, or at varying depths of the organic horizon (Mack et al., 2004; Street et al., 2020). Future work should also consider the placement of treeline transects to capture potential effects of treeline expansion on SOC storage. My experimental design was intended to capture changes in 51 SOC from unforested to sparsely forested woodlands, synonymous to increasing stem density at local scales. The transects do not necessarily reflect long-term changes related to biome scale shifts, especially given that spruce reproduction and range expansion in this area is limited at the northern margins (Lantz et al., 2019; Travers-Smith & Lantz, 2020; Walker et al., 2012; Brown et al., 2019). My study area also represents a relatively small part of the forest-tundra transition and future work should expand measurements over larger gradients in vegetation composition and structure. 3.5 Conclusion In this study, I linked measurements of SOC with vegetation structure and microtopography derived from drone imagery across local gradients in tree density in the Northwest Territories, Canada. Treelines were clustered into three distinct types representing gradients in productivity, topographic variation and the composition of plant functional groups. Across all site types, SOC increased slightly with vegetation height and tree stem density. However, there was substantial variation on SOC storage in tundra sites with low stem density. My results suggest that microtopography, and broader-scale landscape position and drainage patterns that influence soil temperature and moisture, are also strong drivers of soil carbon accumulation in continuous permafrost. This work also underscores the value of using remote sensing data at different spatial scales. To this end, I linked medium-resolution spectral indices from Landsat to fine-scale treeline vegetation structure. Greenness and brightness indices tended to decrease with greater canopy height and stem density. Taken together, this work demonstrates the use of high-resolution drone imagery for enhancing fine-scale mapping of vegetation structure and terrain characteristics that influence below-ground soil carbon storage. 52 Chapter 4: Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2 and Landsat time-series2 4.1 Introduction Long-term satellite observations show that vegetation productivity is responding to rapid changes in climate (Arndt et al., 2019; Beck & Goetz, 2011; Berner et al., 2020a; Ogden et al., 2023). Spectral “greening” in the tundra is driven by increasing shrub cover and phytomass (Epstein et al., 2012; Walker et al., 2003). Boreal forests have shown both increasing and decreasing greenness trends associated with wildfire recovery (Bonney et al., 2018; Sulla-Menashe et al., 2018). The transitional region between these two biomes, known as the forest-tundra ecotone (FTE) could experience large shifts in vegetation structure and composition under a warming climate (Berner & Goetz, 2021; Montesano et al., 2024). Modeling suggests that climate warming could increase above-ground biomass between 15-42% by 2050 (Pearson et al. 2013). Developing models of vegetation structure using remotely sensed data across the circumpolar is challenging due to limited sources of broad-scale training data. Spaceborne lidar data from ICESat-2 offers new opportunities to monitor vegetation structure across the forest-tundra ecotone (Neumann et al., 2019). ICESat-2 is a single photon-counting system and was launched in 2018 to measure terrain height in polar regions. A large number of lidar measurements can be used to model vegetation structure in combination with multispectral satellite imagery (Coops et al., 2021). To date, methods 2 This chapter has been adapted from the following publication: Travers-Smith, H. et al. Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data. Remote Sensing of Environment 305, 114097 (2024). 53 using spaceborne lidar data have been focused at lower-latitudes, where tree canopies are generally taller and denser (Mulverhill et al., 2022; Zhang & Liu, 2023). Biases in global models tend to overpredict height in the forest-tundra and have greater uncertainty due to a lack of training data in northern environments, or optimization for taller vegetation (Lang et al., 2023; Lefsky, 2010). Thus, our ability to make inferences about vegetation change in the forest-tundra ecotone with existing satellite-based canopy height products is currently limited. To capture the variable and fine-scale nature of changes in vegetation structure across the FTE, there is a need for remote sensing products that capture sufficient detail, are spatially continuous, and validated against fine-scale observations (Timoney, 2022; Timoney & Mamet, 2020). In this analysis I used ICESat-2 ATL08 Land and Vegetation Height data (Neuenschwander & Pitts, 2019) to model canopy presence and canopy height across 180 million hectares of the Canadian forest-tundra ecotone from suite of covariates derived from Landsat BAP composites and a Digital Elevation Model at 30 m spatial resolution. These models were validated against an independent source of airborne lidar data from the NASA Land, Vegetation and Ice Sensor (LVIS). Finally, I used these data to map the northernmost limit of continuous forest across Canada and compared it to the boundary of the forest-tundra derived by Timoney et al. (1992). 4.2 Methods 4.2.1 Overview The workflow for this research followed three primary steps: 1) deriving spectral and topographic predictor variables using a time-series of Landsat surface reflectance data 54 and a DEM, 2) spatially joining predictor variables to 98th percentile canopy height from the ICESat-2 ATL08 Land and Vegetation Height Product, and 3) training and validating two Random Forest models to predict canopy presence or absence and canopy height across the study area (Figure 4-1). To validate these models I used the LVIS Geolocated Surface Elevation and Canopy Height data product (LVISF2) which provides a range of vegetation structural metrics from full-waveform airborne lidar (Blair & Hofton, 2020). Data processing and analysis were conducted in R (version 4.2.2; R Core Team, 2022). Figure 4-1: Flowchart showing methods workflow used to predict and validate canopy presence and height across the forest-tundra ecotone. 55 4.2.2 Study area In this analysis, I focus on approximately 180 million hectares of the Canadian forest-tundra ecotone within Northwest Territories, Nunavut, Quebec and the northern Labrador Peninsula (Figure 4-2). The study area was chosen to encompass the transition from the boreal forest in the Taiga Plains and Taiga Shield to treeless tundra in the Southern Arctic ecozones. Figure 4-2: Map of the northwestern Arctic and Sub-Arctic of Canada. The grey points show the ICESat-2 ATL08 segments (n = 982,396) used to train and test the Random Forests models. The orange lines show the extent of the 2019 LVISF2 product used as validation data (Blair & Hofton, 2020). 56 4.2.3 Landsat BAP composites Landsat Best Available Pixel (BAP) composites use multiple images across a single growing season to generate annual cloud and gap free mosaics of surface reflectance (White et al., 2014). When selecting pixels for inclusion in a given BAP composite, priority is given to pixels closest to the center of the annual growing season (August 1) that are free of atmospheric interference. Missing reflectance values are imputed using linear trends fit across the entire time series (Hermosilla et al., 2015). This surface reflectance product provides consistency over time and space and reflectance values are expected to represent similar physical features over time (Hermosilla et al., 2018). In this analysis, I use BAP surface reflectance data over a 10-year time period from 2012 to 2021 (inclusive). I calculated median reflectance across six Landsat spectral bands (red, green, blue, NIR, SWIR1, SWIR2), and four indices, Tasseled Cap Brightness (TCB), Tasseled Cap Wetness (TCW), Tasseled Cap Greenness (TCG) and the Enhanced Vegetation Index (EVI) using equations from Table 1-1. The ten-year period was used to smooth year-to-year noise across the time series. The 2012-2021 period also overlaps with the ICESat-2 data acquired from 2019-2021. I used the Landsat Composite-2-Change (C2C) data product (Hermosilla et al., 2016) to flag pixels that experienced stand replacing disturbance between 2012-2021 and removed them from subsequent modeling. To remove pixels impacted by drainage and expansion of waterbodies, I generated a mask of maximum water extent using minimum thresholds in annual TCB and EVI and removed pixels with water in three or more years over time-series. 57 4.2.4 ICESat-2 ATL08 Land and Vegetation Height I first assessed correspondence between ATL08 98th percentile canopy height retrievals from the summer (July-August) and winter (December-January) against airborne lidar from LVIS collected over the forest-tundra transition in NWT, Canada. While summer ICESat-2 data is generally preferred to align with leaf-on conditions, background solar radiation can interfere with measurements of low canopy heights (Feng et al., 2023). Winter acquisitions can be collected in darkness with less background noise, but vegetation canopies are expected to be snow covered at high-latitudes which may impact estimation of terrain height. Following comparison of seasonal data against LVIS, winter ICESat-2 data showed better agreement with LVIS across the forest-tundra transition. Based on these findings, further modeling was conducted with ATL08 segments collected in December and January only. ICEat-2 ATL08 data (Version 5) were downloaded from NASA’s National Snow and Ice Data Center (Neuenschwander et al., 2021). I used 98th percentile height summarized within 20 m segments (n= 982,396) to correspond with the spatial resolution of Landsat pixels (30 m). Segments were filtered based on the quality control flags to remove samples impacted by water, blowing snow, aerosols, multiple scattering effects and clouds. The terrain quality flag was used to remove segments where the estimated ground height was outside of an acceptable range from a reference Digital Terrain Model (Neuenschwander, 2022). Due to the low number of photon returns expected from sparse vegetation, I only used data acquired by the strong beams (Neuenschwander et al., 2022b). Overall, 52%, 17.5% and 30.5% of segments were located in the Southern Arctic, 58 Taiga Plain and Taiga Shield ecozones, which correspond to 34%, 14% and 47% of the total study area, respectively. 4.2.5 ASTER Global Digital Elevation Model Five topographic variables (elevation, slope, aspect, roughness, and Topographic Position Index) were derived from the ASTER Global Digital Elevation Map at 30 m spatial resolution (Tachikawa et al., 2011; Version 2). Slope, aspect, roughness and Topographic Position Index (TPI) were calculated from the DEM using the adjacent eight pixels in R using the “terrain” function in the raster package. Roughness is calculated as the difference between the maximum and minimum elevation of a pixel and its neighbours, and TPI is the difference in elevation between a pixel and the mean of its neighbours (Hijmans, 2022). 4.2.6 LVIS validation data The LVIS instrument is a full-waveform lidar system flown at an altitude of 10 km (Blair & Hofton, 2020). The laser footprint on the ground varies between 10 m and 25 m and data is collected along 2 km wide transects. The LVIS Level-2 Geolocated Surface Elevation and Canopy Height Product (LVISF2; Version 1) contains geolocated footprints and canopy metrics derived from the full-waveform measurements. In this study, I used approximately 33,000 km2 of LVIS data acquired as part of NASA’s Arctic-Boreal Vulnerability Experiment (Figure 4-2). Data covering the Canadian forest-tundra ecotone were acquired on four dates in 2019: July 15, July 16, July 25 and August 8. The LVISF2 data product is delivered as points on a 10x10 m grid corresponding to the lidar footprints. I extracted the maximum, 95th and 98th percentile height for each footprint and calculated 59 the mean value within 30 m pixels to match the spatial resolution of Landsat. Landsat pixels with fewer than three LVIS footprints were excluded from analysis. Error statistics for the modeled canopy heights were calculated from a sample of 30,000 pixels stratified by LVIS canopy height to ensure balanced representation of observed heights across the study area. 4.2.7 Modeling canopy presence and height Random Forests models were trained using the ten spectral variables from 2012-2021 corresponding with the 2019-2021 ICESat-2 ATL08 acquisitions (n = 982,396 segments), and the four topographic variables from the ASTER-GDEM data. For model training, the ATL08 segments were filtered to a dataset representing undisturbed and “spectrally homogenous” areas. Spectral homogeneity in the training data ensures that models are fit with a minimum number of pixels with sudden spatial changes in landcover or mixed land cover types that could increase class confusion (Mulverhill et al., 2022). Homogeneity was assessed for pixels within a 20 m circular buffer around the center of each ATL08 segment. Within the buffer I extracted disturbance history from the C2C product and the coefficient of variation in spectral values. Segments were included in the training dataset if they had no disturbance history between 2012-2021 and each band had a coefficient of variation < 10% (n = 565,541). The canopy presence/absence classification was trained on a balanced sample of 300,000 segments labelled as “canopy present” or “canopy absent”. The labelling was based on the land cover flag in the ATL08 metadata and a minimum of four number valid photons classified as “canopy” or “top of canopy” in each 20 m segment. Segments were 60 classified as “canopy absent” when there were fewer than four returns labeled with a canopy within the 20 m segments, and where the segment land cover type was unvegetated. The land cover classification is based on the 2019 Copernicus Global Land Cover dataset at 100 m spatial resolution, which has been spatially joined to ATL08 segments and included in the segment metadata. The probability of canopy presence and absence (ranging from 0-100%), represents the proportion of decision trees in the forest that predicted each class. The number of trees was 1001 (to break ties) and the mtry parameter set to five. The canopy presence model was tested on a dataset of 200,000 segments split evenly between the “canopy present” and “absent” classes. The models and predictions were generated using the “ranger” R package (Wright & Ziegler, 2017). I used the unscaled permutation method to calculate relative variable importance, which calculates increase in Mean Square Error when the values of each predictor variable are randomly permuted. The canopy height regression model was trained on 193,805 segments representing undisturbed and spectrally homogenous areas with a canopy present. The Random Forests regression model was generated using 1000 trees and an mtry value of seven. I also assigned a weight to each sample to increase the probability of sampling high and low heights. This helps mitigate the effect of sample averaging within decision trees that reduces the range of the predicted values relative to the training data. Weights were defined based on the number of standard deviations away from the mean, with greater weight assigned to samples further from the mean. After training, the model was tested on 90,000 segments not used in the training data, which were stratified by observed height with an equal number of samples drawn from each stratum. 61 All processing was done locally on Dell Precision T7910 workstations with 64GB of RAM. Multiple (2-4) workstations with read/write access to the same network location were used to reduce overall compute time. Pixel values were stored as parquet files to reduce file size and computational overhead. Using the ranger package in R, Random Forest predictions of canopy presence and height took ~1.5 minutes per 1 million pixels totalling 219 hours (~9 days) to predict over the entire study area. Model outputs were transferred back to raster format as the last step of processing. 4.2.8 Independent validation with LVIS I used the LVISF2 dataset to further evaluate the performance of the Random Forest canopy presence and height models. In total, there were 126 LVISF2 tiles within the study area. As the input Landsat surface reflectance layers represent a 10-year median, I removed pixels that underwent a stand-replacing disturbance within the 2012-2021 time period. For undisturbed pixels where a canopy was present, I evaluated canopy height predictions using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) and bias against LVIS 98th percentile canopy height, and examined error statistics across different height classes (0-2m, 2-5m, 5-10m and >10m). 4.2.9 Delineating the northern limit of continuous forests From the resulting canopy height and presence maps, I automatically delineated the northernmost boundary of continuous forest at least 3 m in height. The 3 m height threshold was chosen as a general boundary between tall shrubs and low-stature forests in the forest-tundra ecotone. Based on field data in the NWT, the height of shrublands close to the forest limit may vary between 0.3-3.2 m (van der Sluijs, et al., 2023a). Across 62 NWT and northern Manitoba maximum forest heights at the treeline ranged from 5.1-11.9 m (Mamet & Kershaw, 2012). Thus, the 3 m limit is expected to capture the transition between forest and shrub-dominant landscapes while still being reliably detected with spaceborne lidar data. To map the forest limit, I calculated average canopy height within 1km grid cells then classified each cell using the 3 m threshold. Next, simplified polygons were created from the classified raster using the Raster to Polygon tool in ArcGIS Pro (version 2.9.5). From these polygons, the Buffer and Generalize Line tools were used to create smoothed continuous boundaries representing 3 m canopy height. This boundary was then compared to mapping of the forest-limit, forest-tundra and tree limit by Timoney et al., (1992). 4.2.10 Comparison with the (1992) forest-tundra limit I visually compared my map of canopy height and presence to fine-scale photo interpretation of the Canadian forest-tundra transition by Timoney et al. (1992). Timoney et al. (1992) used 1,314 air photos to define the boundaries of the forest-tundra ecotone across western and central Canada. Boundaries for the forest limit, forest-tundra and tree limit were defined by Timoney et al. (1992) using the 1:1000, 1:1 and 1000:1 ratio of forest to tundra land cover, respectively. This work represents the most comprehensive and high-resolution mapping effort of the Canadian FTE using high resolution air photo interpretation. 63 4.3 Results 4.3.1 Impact of season on ATL08 retrievals Overall, median canopy heights measured using ICESat-2 were higher (3.92 m) than LVIS (2.15 m), likely due to misclassification of background solar radiation as signal photons for low canopies (Feng et al., 2023). Winter rather than summer ICESat-2 data showed less bias compared to LVIS, with median heights of 3.81 m and 4.02 m, respectively (Figure 4-3). Across the study area, LVIS canopy height ranged from 0.57-21.87 m, while winter and summer ICESat-2 measurements ranged from 0.54-31.34 m and 0.52-16.32 m. Summer and winter ATL08 retrievals were similar at lower latitudes, but diverged north of 68˚N (Figure 4-3). At higher latitudes (> 68.3˚N) median height from LVIS was less than 3 m. However, only the winter ICESat-2 data reflected this transition to lower-stature vegetation as the median height of summer ICESat-2 data never fell below 3 m across the study area (Figure 4-3). 64 Figure 4-3: Boxplots (left) showing height distribution for summer (July-August) and winter (December-January) ICESat-2 ATL08 98th percentile canopy height and LVIS 98th percentile canopy height by increasing latitude. The red line shows the 3 m height limit. The map (right) shows the distribution of ICESat segments (light green and dark green points) and LVIS transects (blue) across the forest-tundra ecotone. 4.3.2 Random Forests model assessment The Random Forests canopy presence model had an overall 89.9% accuracy on the test dataset (n = 200,000). Canopy presence was detected with 82% accuracy while canopy absence was detected with 93% accuracy. The canopy height regression model showed overall RMSE of 2.07 m and r2 was 0.49 on the test dataset (n = 90,000). Model performance was best for canopy heights between 5-10m with RMSE of 1.98 m and MAPE of 21%. The majority (81%) of observed canopy heights were between 2-10 m. The model tended to overpredict height below 2 m (bias = 1.17 m) and underpredict height greater than 10 m (bias = -3.78 m). In both classification and regression models, topographic predictors had low variable importance compared to the spectral bands and 65 indices. The most important variables in the classification model were the SWIR1 bands, red bands and the TCB index. Similarly for the height model the most important variables were TCB and the NIR and SWIR2 bands (Figure 4-4). Table 4-1: Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), Root Mean Square Error (RMSE), and bias for the Random Forests regression model predicting canopy height on a testing dataset of undisturbed ATL08 segments not used in model training (n = 90,000). Error metrics are stratified by height and ecozone. The overall r2 was 0.49. Height Class MAE (m) MAPE (%) RMSE (m) Bias (m) Proportion of all ATL08 segments <2m 1.22 103.17 1.75 1.17 16% 2-5m 1.38 42.06 1.75 0.80 36% 5-10m 1.57 21.64 1.98 -1.05 45% >10m 3.80 32.28 4.48 -3.78 3% Overall 1.55 42.46 2.07 -0.24 -- 66 Figure 4-4: Variable importance for the Random Forests classification and regression models predicting canopy presence and height. Variables are ordered from most (dark colors) to least important (light) based on the permutation method. 4.3.3 Mapping canopy presence and height across the ecotone Random Forests models were applied to map canopy height and presence at 30 m resolution across the Canadian forest-tundra ecotone (Figure 4-5). Overall, this map shows the expected transition from high and continuous tree canopies in the south, to lower and discontinuous canopies in the north (Figure 4-5). Within the Taiga Plain, Taiga Shield and Southern Arctic ecoregions, a vegetation canopy was detected in 86%, 58% and 21% of pixels, respectively. The average and standard deviation of modeled heights were 4.80 m (sd = 1.95). Canopy height also varied across ecoregions (Figure 4-6). Overall, the Southern Arctic had the lowest canopy heights with a mean and standard deviation of 3.78 m (sd = 1.89). The Taiga Plain and Taiga Shield had mean canopy heights of 5.71 m (sd = 1.86) and 4.94 m (sd = 1.86), respectively. The automatic 67 delineation of continuous 3 m forest extent (Figure 4-5) corresponded best with the 1:1 forest-tundra boundary mapped by Timoney et al., (1992). Discontinuous patches of taller vegetation were also visible above this limit, but are generally bounded by the 1:1000 tree limit defined by Timoney et al. (1992; Figure 4-5). Figure 4-5: Map of canopy height and presence across the Canadian forest-tundra transition at 30 m spatial resolution. The red line shows the 1:1 forest limit defined by Timoney et al (1992) and white areas represent water. The inset map (indicated by the arrow) shows spatial detail in 30 m canopy height predictions at the forest limit. 68 Figure 4-6: Density plot of the probability density function of canopy height (m) sampled from the Southern Arctic, Taiga Plains and Taiga Shield ecoregions. Note only pixels with a probability of canopy presence > 50% were included. 4.3.4 Independent validation using LVIS Overall, LVISF2 98th percentile height showed moderate agreement with the Random Forests canopy height predictions with an R2 value of 0.53 and an overall RMSE of 2.23 m (Table 4-2; Figure 4-7). Modeled canopy heights were most accurate in the 5-10 m range, with RMSE of 1.67 m and MAPE of 18.48 % (Table 4-2). Model performance decreased when predicting the highest and lowest canopies, where canopies <2 m were 69 overestimated (Bias: +2.17 m), and canopies >10m were underestimated (Bias: -3.24 m; Table 4-2). Table 4-2: Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), Root Mean Square Error (RMSE) and bias of canopy height predictions compared with independent airborne laser scanning data from LVIS using 98th percentile canopy height. The overall R2 was 0.53 (n = 30,000). MAE (m) MAPE (%) RMSE (m) Bias (m) Proportion of all LVIS 30 m pixels <2m 2.17 182.28 2.57 2.17 19% 2-5m 1.77 54.91 2.11 1.68 41% 5-10m 1.32 18.48 1.67 -0.20 34% >10m 3.34 27.15 3.94 -3.24 4% Overall 1.76 53.31 2.23 +0.47 -- 70 Figure 4-7: Canopy height predicted by Random Forests against the validation dataset, LVIS-F2 98th percentile canopy height. The dashed red line is the 1:1 ratio between observed and predicted values. The color indicates the number of cases in each hexagon cell. R2 was 0.53. 4.4 Discussion Lidar observations play an important role in understanding global changes in vegetation structure (Schimel et al., 2015). In particular, canopy height derived from lidar can be used to estimate above-ground biomass and carbon storage (Lefsky et al., 2002; Simard et al., 2011). However, lidar data acquired by airborne sensors are limited in spatial extent and are expensive to acquire at high-latitudes. Several approaches have been used to model lidar observations from spaceborne platforms including ICESat and GEDI over large areas using optical remote sensing imagery (Coops et al., 2021b; Matasci, et al., 2018b; Wulder et al., 2012). However, ICESat-2 is the only spaceborne lidar system 71 collecting data in polar regions and few studies have used it to model vegetation structure in this region (Montesano et al., 2024). In this research, I integrated ICESat-2 canopy height measurements with Landsat BAP composites to produce maps of canopy presence and height across 180 million hectares of the Canadian arctic and sub-arctic at 30 m spatial resolution. I demonstrated the use of these maps to derive the limit of continuous 3m forest and map variation in vertical structure across the forest-tundra ecotone. As this approach exclusively uses spaceborne remote sensing products, it can be applied to other areas of the circumpolar and can be updated as new data from the Landsat and ICESat missions become available. 4.4.1 Improvement over global canopy height models Error metrics show that my workflow was effective in classifying canopy presence and predicting height. Overall, the canopy height model showed a MAE of 1.76 m and R2 of 0.53 when compared to independent airborne lidar data. These values are consistent with similar work by Malambo et al. (2023) and Sothe et al. (2022) who also modeled ICESat-2 canopy heights using Landsat data across Texas and forested ecoregions of Canada. These studies reported MAE of 2.09 m and 3.4 m, and R2 of 0.36 and 0.46, respectively. Similarly, Lang et al. (2023) used GEDI, Landsat and Sentinel-2 data to generate spatially continuous estimates of canopy height across the entire globe. However, global models generally show higher uncertainty in the forest-tundra ecotone due to the lack of training data at high latitudes and model optimization for vegetation in the tropics (Lang et al., 2023). Using the same LVIS data for validation, my model shows better RMSE for lower-stature vegetation in the study area. Lang et al. (2023) reported RMSE of 2.8 m, 4.6 m and 5.3 m for three LVIS tiles. My models show similar RMSE for short canopies less 72 than 2 m in height (2.57 m), but improvement in the 5-10 m range with RMSE of 1.67 m and bias of -0.20 m. Improved performance is likely due to training samples exclusive to high-latitudes. Additionally, I used a single model over the entire study area which eliminates artefacts introduced by tiling over large areas (Lang et al., 2023), making this approach is more appropriate for continuous mapping of the forest limit. I used a two-step approach to first predict canopy presence and avoid making height predictions below the detection limit of ICESat-2. However, misclassification in the canopy presence layer can propagate into the canopy height model leading to overestimation of height at some tundra sites above the tree limit. Land cover with different vegetation structure can appear spectrally similar, contributing to false positives in the canopy presence layer. For example, treeless wetlands may appear spectrally similar to forested wetlands (Hermosilla, et al., 2022b). These areas both represent wet, vegetated land cover types with moderate green and NIR reflectance, exhibiting high class confusion. In addition, wet areas may absorb the green laser emitted from ICESat-2, which would decrease the signal to noise ratio and potentially lead to incorrect classification of vegetation returns. These effects likely explain some of the overestimation in canopy height for treeless wetland areas. To mitigate some of these impacts, water was removed using thresholds in TCB and EVI however, ephemeral water bodies and wetlands could still be missed by this process. Thus, existing knowledge of forest distribution and high-resolution imagery to confirm anomalous results is needed to constrain predictions from remote sensing models. Future work could incorporate additional environmental data, such as vegetation phenology (Bolton et al., 2020), or higher spatial resolution land cover 73 maps (Hermosilla, et al., 2022a) to identify evergreen and broadleaf shrubs and differentiate canopy height in these areas. 4.4.2 Sources of error for northern canopy height models Model performance was also impacted by the modeling algorithm, as well as inaccuracies at the sensor and data level. I observed the most accurate height predictions towards the centre of the height distribution (5-10 m), and declining performance for very high (>10 m) and low (<2 m) canopies. At the modeling level, the Random Forests regression algorithm generates predictions using the average output of many decision trees. This tends to pull extreme predictions towards the mean, and truncates the range of predicted values compared to the input dataset (Wright & Ziegler, 2017). In comparison, k-nearest neighbours imputation does not alter the range of predicted values relative to training data, but may be more sensitive to noise and outliers in training data. As ICESat-2 is expected to have a moderate degree of noise in the canopy finding algorithm, imputation methods may be less suitable. At the sensor and data level, previous studies have also observed underestimation of high canopies and overestimation low canopies for ICESat-2 canopy height products (Feng et al., 2023; Liu et al., 2021; Neuenschwander & Pitts, 2019). Underestimation of tall canopies likely reflects the point density of the ATLAS sensor (0-2 photon returns per ~11 m footprint), which makes it less likely that photon returns will come from the top of the canopy where foliage is sparse (Neuenschwander et al., 2022; Neuenschwander & Pitts, 2019). The apparent overestimation of shorter canopies is less clear, but may be related to errors associated with estimating terrain height (Feng et al., 2023; 74 Neuenschwander & Pitts, 2019). While ICESat-2 shows improved ability to resolve complex topography compared to the previous ICESat mission, both terrain and canopy heights are likely impacted by steep slopes >30 degrees (Feng et al., 2023; Liu et al., 2021). Due to high relative error for canopies less than 2 m, my results show limited use of ATL08 data to reliably detect the northern transition of shrub to graminoid tundra present in the northernmost range of my study area. However, despite these limitations the resulting canopy height map still captured meaningful variation in structure across the ecotone, allowing for identification of the forest-tundra boundary. Due to persistent snow cover, large seasonal variation in daylight, and lower overall canopy height, the forest-tundra vegetation transition poses unique challenges for ICESat-2 canopy height retrievals. In these conditions, I found that season (summer vs winter) substantially impacted ATL08 canopy height estimates, especially for canopies close to the forest limit (Figure 4-3). Compared to summer acquisitions, the distribution of canopy height values acquired in the winter showed better agreement with LVIS in the same region. These results likely reflect improved detection of photon returns from the ground and canopy at night as well as greater reflectivity of snow-covered surfaces (Feng et al., 2023; Neuenschwander et al., 2022b). Future work using ATL08 data in circumpolar regions should carefully consider how snow and solar background noise impact the consistency of canopy height estimates. While a higher number of photon returns do not guarantee more accurate canopy height estimates, more signal photons from the ground and canopy should improve the ability of the DRAGANN algorithm to accurately classify photons as ground, canopy and noise (Feng et al., 2023; Neuenschwander et al., 2022b). As summer daytime hours increase at high-latitudes, there is also a strong correlation 75 between season, snow cover and solar background radiation that should be considered in data acquisition. 4.4.3 Limitations and future directions At a spatial resolution of 30 m, my method will not be sensitive to all nuances in changing vegetation structure across the forest-tundra ecotone. For example, modeled canopy heights may not reflect small differences in canopy cover and stand density within a 30 m pixel. It is also likely that sparse stands of smaller trees (close to 3 m) are present in pixels where a canopy was not detected. Thus, fine-scale analyses are still required to understand mechanisms of treeline change and spatial patterns identified in medium resolution satellite images. Increasingly, very high-resolution imagery at the sub-meter scale is becoming available which would be able to support analyses at the level of individual trees and stands (Lantz et al., 2019; Travers-Smith & Lantz, 2020). This kind of fine-scale validation is also important because both increases in tree height and stand density are likely occurring simultaneously across the ecotone, which may or may not result in changes in the northern forest limit (Danby & Hik, 2009; Lantz et al., 2019; Travers-Smith & Lantz, 2020). The position of the northern forest limit will also be sensitive to definitions of “forest” and “non-forest”. The Food and Agriculture Organization defines forests globally as “land more than 0.5 hectares with canopy cover over 10% and trees that can reach at least 5 meters in situ”. Previous mapping of the forest-tundra ecotone in Canada by Timoney et al. (1992) used the ratio of tundra to forest land cover to establish upper, lower and central boundaries of the ecotone. My use of a 3 m height threshold spatially aligns with the 1:1 76 ratio of forest to tundra landcover and is located north of the 5 m height threshold set by the FAO. Future work could test the sensitivity of the forest limit boundary using different height thresholds. Additionally, Random Forests models can be sensitive to parameter and feature selection which can be optimized with feature selection algorithms (Ding & Peng, 2005) and hyperparameter tuning (McKay et al., 1979). Recently, Orndahl et al. (2025) used Random Forests models to predict tundra plant biomass over the entire circumpolar region (850 million hectares) using Landsat data. To quantify uncertainty in predictions they used 100 Monte Carlo simulations of randomly sampled training data and calculated the 5th and 95th percentiles in biomass predictions across model runs. For this thesis, given that a single model run took 9 days to compute over the study area, I did not take this approach to quantify uncertainty in height predictions. Future researchers with similar compute capacity could consider running simulations on smaller samples of pixels rather than the entire dataset to avoid massively increasing compute time. 4.4.4 Conclusions In this study, I derived detailed and spatially continuous maps of vegetation structure across 180 million hectares of the Canadian forest-tundra ecotone, and use these maps to delineate the northern forest limit. I validated predictions of canopy presence and height using fine-scale ALS data and found that it was most effective in identifying vegetation canopies in the 5-10 m range, representative of short, growth-limited spruce forests in this region. These findings also highlight limitations and considerations when using ICESat-2 ATL08 Land and Vegetation Height product at high latitudes. As this method 77 relies on freely available satellite remote sensing data it is cost effective, quantitative, repeatable and scalable over large areas. These results can inform large-scale monitoring of climate driven changes in vegetation structure and carbon storage across the circumpolar forest-tundra ecotone. 78 Chapter 5: Satellite observations reveal stable forest limits and shrub expansion across the Canadian forest-tundra ecotone3 5.1 Introduction Vegetation is responding to changes in growing season length, precipitation and fire regimes (Berner et al., 2020; Matasci, et al., 2018b; Myers-Smith et al., 2020; Orndahl et al., 2022). The forest-tundra ecotone (FTE), located between the boreal and tundra biomes is expected to undergo significant changes in vegetation structure and composition with climate warming (Berner & Goetz, 2021; Payette et al., 2001). Projected increases in temperature and precipitation could facilitate northward advance of the boreal biome over the next century (Montesano et al., 2024; Pearson et al., 2013). These changes are concerning due to broad-scale implications for habitat quality, availability (Rickbeil et al., 2018; Speed et al., 2021), carbon storage (Epstein et al., 2012), surface albedo (Chapin et al., 2005), and permafrost stability (Chapin III et al., 2000). Over recent decades, boreal forests have shown mixed responses to climate change. Satellite observations of tree cover suggest that disturbance at the southern margin of the boreal biome is outpacing forest expansion at the treeline (Rotbarth et al., 2024). This is consistent with fine-scale observations showing limited change (Brodie et al., 2019; Evans & Brown, 2017; Rotbarth et al., 2023), or no northward advance of latitudinal treelines despite recent favorable climate conditions (Lantz et al., 2019; Timoney et al., 3 This chapter has been adapted from the following publication: Travers-Smith, H., Coops, N. C., Mulverhill, C., Wulder, M. A., Ignace, D., & Lantz, T. C. (2025). Satellite observations reveal stable forest limits and shrub expansion across the Canadian forest-tundra ecotone. Environmental Research Letters, 20, 104006. https://doi.org/10.1088/1748-9326/adfc7f . 79 2019; Timoney & Mamet, 2020, Maher et al., 2021). Vegetation change in the FTE is complex and changes in forest cover are also influenced by expansion of tall shrubs (Brodie et al., 2019; Dial et al., 2016; Terskaia et al., 2020). Shrubs have shown more rapid responses to climate compared to conifers (Travers-Smith & Lantz, 2020), and establishment of tall shrubs may restrict future tree establishment in the tundra (Terskaia et al., 2020). Taken together, these findings indicate complex relationships between climate and potential shifts in the boreal biome, in part due to interactions with disturbance and shrub expansion. To date, many remote sensing studies of northern vegetation have focused on analyzing patterns in spectral greening indices, including the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI; Berner et al., 2020; Berner & Goetz, 2021; Chen et al., 2021; Guay et al., 2014; Myers-Smith et al., 2020; Sulla-Menashe et al., 2018). Spectral greening trends have been associated with increasing Leaf Area Index (McManus et al., 2012; Myers-Smith et al., 2011), vegetation phytomass (Epstein et al., 2012; Walker et al., 2003), and growing season length (Arndt et al., 2019). Stronger greening trends at the northern margin of the boreal could also indicate northward advance of the biome (Berner & Goetz, 2021; Sulla-Menashe et al., 2018). However, to fully interpret spectral greening trends it is necessary to link them to physical characteristics of vegetation (Rotbarth et al., 2023). This chapter builds on modeling developed in Chapter 4 to examine change in vegetation structure across the entire Landsat time-series from 1985 to 2021. I examined long-term trends in vegetation presence and height over 36-years and classify multiple types of 80 structural change at 30 m spatial resolution. These time-series data were used to address the following research questions: 1) Are observed changes in forested area consistent with northward advance of the forest limit and climate warming? 2), How has vegetation cover and height changed across the Canadian FTE? And 3) How much of these changes are attributable to disturbance-recovery dynamics? 5.2 Methods 5.2.1 Study area and method overview Analysis was conducted over 180 million hectares of the Canadian FTE previously described in sections 2.1 and 4.2.2. I applied the Random Forests classification and regression models from Chapter 4 to a Landsat time-series from 1985-2021. 5.2.2 Landsat time-series data To model change in vegetation structure, I used BAP annual composites over the following six decadal time periods: 1985-1995, 1990-2000, 1995-2005, 2000-2010, 2005-2015, 2010-2021. Overlapping 10-year time periods were chosen due to the stronger predictive power of long-term average reflectance values in Random Forests models (Bolton et al., 2018). Additionally, annual BAP composites earlier in the time-series have more missing values that have been temporally filled (Hermosilla et al., 2015). Thus, longer time periods (> 5 years) ensure there are adequate number of real surface reflectance measurements in each period, rather than proxy values. In each time period, I used spectral values across six bands (red, green, blue, NIR, SWIR1, SWIR2) and four multi-band indices: Tasseled Cap Brightness (TCB), Tasseled Cap Wetness (TCW), Tasseled Cap Greenness (TCG) and the Enhanced Vegetation 81 Index (EVI) using equations from Table 1-1. Pixels experiencing a stand replacing disturbance during the time series were identified using the Composite2Change data product and removed from model training (Hermosilla et al., 2016). To remove pixels impacted by drainage and expansion of waterbodies, I generated a mask of maximum water extent using minimum thresholds in annual TCB and EVI and removed pixels with water in three or more years of the time series. 5.2.3 Northwest Territories Fire History Within a subset of my study area, I used a dataset of fire boundaries that burned between 1950 and 2020 across the Northwest Territories to examine associations between vegetation change and disturbance history (NWT Centre for Geomatics, 2024). This dataset represents one of the most comprehensive records of fire history in northern Canada and uses historic air photographs to map fires that burned before the satellite record. Fires were grouped into the following classes: undisturbed, pre-1985, 1985-1999 and 2000-2011. We did not assess change for recent fires between 2011 and 2020 because the Landsat time-series represents median spectral conditions over the entire decade and will not capture change occurring within the time-period. 5.2.4 Classifying structural vegetation change Methods to model canopy height and presence follow the same procedure described in Chapter 4. Here, I apply the same Random Forests classification and regression models to each time period (n = 6). To complement variable importance from Chapter 4, I first assessed how spectral indices capture variation in vegetation structure. To do this I 82 sampled 200,000 pixels classified as no canopy, 1-3 m 3-5 m, 5-10 m and > 10 m and plotted the distribution of spectral values within each structural class. I defined change over time using a heuristic model to create nine stable and transitional vegetation types (Table 5-1). First, within each time period I classified pixels into three structural types: no canopy (probability of presence < 50%), shrub (1-3 m) or forest (>3 m). Next, I compared the majority class between the first and last three time periods. If the majority was the same, the pixel was classified as “stable no canopy”, “stable shrub” or “stable forest”, according to the majority class over time. The following transitional vegetation types were defined based on change in the majority vegetation class: “canopy loss” (forest or shrub to no canopy), “new shrub” (no canopy to shrub), “new forest” (no canopy or shrub to forest), “shrub infilling” (forest to shrub). For pixels with majority forest class, I calculated linear trends in canopy height to further classify “forest growth” (increasing height) and “shrub infilling” (decreasing height). To classify pixels with increasing and decreasing trends in height, I used a threshold in the t-statistic, representing the significance of the linear trend between height and the six time-periods for each pixel. This approach was used due to its low computational cost compared to other methods in the literature including the Thiel-Sen slope estimator or Mann-Kendall trend tests, which are typically applied to smaller subsets of pixels (Berner & Goetz, 2021; Bonney et al., 2018). I calculated the t-statistic for every pixel in the study area (~8.7 billion) and defined thresholds using the distribution of t-scores from a random sample of 200,000 pixels (Figure 5-1). Two “peaks” are evident in the distribution corresponding with t-scores of ±1.69, which separates 27% pixels showing the most 83 extreme decreasing and increasing trends in forest height. Due to the greater uncertainty in modeled heights below 3 m I did not calculate linear trends in height within the shrub class and only used the t-statistic to define change within forested pixels. Finally, if a pixel transitioned from the forest to shrub class or vice versa, but there was no significant trend in height I classified it as “stable low forest”. This accounts for potential spurious transitions between the forest and shrub class in pixels that are close to the 3 m threshold. 84 Table 5-1: Descriptions of the nine structural vegetation types classified at 30 m spatial resolution. Decision rules were based on change in the majority class between the first and last three time periods and linear trends in canopy height. Vegetation class Decision rules Stable no canopy Majority no canopy class Stable shrub (1-3 m) Majority shrub class Stable forest (> 3 m) Majority forest class & no trend in height Stable low forest Change between forest and shrub classes but no trend in height Forest growth Majority forest class & increasing height Shrub infilling Forest to shrub change OR majority forest with decreasing height Canopy loss Change from forest or shrub to no canopy New forest Change from no canopy or shrub to forest New shrub Change from no canopy to shrub 85 Figure 5-1: Distribution of t-statistic used define non-trended, increasing and decreasing trends in canopy height. Dashed lines indicate the ±1.69 threshold. 5.2.5 Mapping advance of the northern forest limit I used the canopy presence and canopy height layers to map the northernmost extent of vegetation >3 m in height for the first and last decadal periods in the analysis (1985-1995, 2010-2021) and evaluated northward advance of the boreal forest. In both time periods, canopy height was set to 0 m when the probability of canopy presence was < 50%. Following methods described in Chapter 4, I summarized mean canopy height within a 1x1 km grid and classified cells using a 3 m threshold in mean canopy height. From the classified grid, I created polygons representing forested areas and generated a smoothed continuous boundary generalizing the geometry of the resulting features. To quantify areas representing advance and retreat of the forest limit, I calculated the difference 86 between the extent of continuous 3m forest area in 1995 and 2021 and removed regions covered by water bodies. An advance in the forest limit indicates the region within the 2021 forest boundary that was not in the 1995 forest boundary, and retreat indicates regions within the 1995 forest boundary that was not in the 2021 forest boundary. To quantify climatic controls on northward forest advance, I used interpolated climate data from Climate NA (Wang et al., 2016) to extract mean annual temperature and total annual precipitation for pixels intersecting the forest limit in both time periods. 5.3 Results 5.3.1 Spectral indicators of vegetation structure Overall, higher reflectance values were associated with lower canopy heights (Figure 5-2). The NIR band and TCG showed similar non-linear trends, with high NIR reflectance and greenness for pixels without a canopy, and with vegetation less than 3 m tall. Intermediate canopy heights showed the lowest NIR reflectance and greenness, but the tallest canopies (>10 m) had slightly higher median values. Both the SWIR2 band and TCB showed linear decreasing values with increasing canopy height (Figure 5-2). 5.3.2 Structural vegetation change The vast majority of the study area had stable vegetation structure over time (92%), with 45% classified as “no canopy”, 34% as “forest” and 9% as “shrub” (Figure 5-3; Table 5-2). The Southern Arctic showed the least amount of change with 2.3% classified as transitional, while 11.7% and 10.7% of the Taiga Plains and Taiga Shield, showed structural change (Table 5-2). The most frequently occurring structural change was “shrub infilling” covering 3.2% of the study area (Table 5-2). The “forest growth” class covered 87 1.8% of the study area and “canopy loss” covered 1.2% of the study area. Both the “new forest” and “new shrub” classes showed similar coverage with 0.7% and 0.9% respectively (Table 5-2). Figure 5-2: Variation in spectral predictors (Band 4: NIR, Band 8: SWIR 2, Tasseled Cap Brightness Index and Tasseled Cap Greenness Index), that were important in the Random Forests models among canopy height classes. Note that the data were scaled for plotting on the same y-axis and values show the number of standard deviations away from the mean value for each band or index. 88 Table 5-2: Summary table showing the proportion of structural vegetation change across the entire study area and stratified by ecoregion (%). Structural class Taiga Plains Taiga Shield Southern Arctic Arctic Cordillera Hudson Plains Overall Stable no canopy 10.0 33.2 80.3 19.8 56.5 45.4 Stable shrub (1-3m) 0.8 9.5 7.3 59.2 0.6 9.0 Stable forest (> 3m) 74.3 43.5 8.6 12.9 31.6 34.9 Stable low forest 3.2 3.1 1.5 4.6 3.5 2.7 Total Stable 88.3 89.3 97.7 96.6 92.2 92.1 Forest growth 4.5 2.3 0.3 0.6 0.5 1.8 Shrub infilling 4.7 4.8 0.2 0.4 1.5 3.2 Canopy loss 1.5 1.5 0.6 0.5 4.8 1.2 New forest 0.8 1.0 0.2 0.6 0.4 0.7 New shrub 0.2 1.1 1.0 1.2 0.6 0.9 Total Transitional 11.7 10.7 2.3 3.3 7.8 7.8 Ecozone % of total area 10.0% 55.1% 30.4% 2.5% 1.6% - Maps showing the distribution of the transitional classes indicate divergent patterns in forest and shrub expansion across the ecotone (Figure 5-4). Pixels classified as “new forest” were close to, or south of the 3 m forest limit, while “new shrub” pixels showed widespread and diffuse presence above the forest limit (Figure 5-4). The northwestern part of the study area shows concentrated “forest growth” compared to Nunavik on the 89 eastern side of Hudson’s Bay, which shows a greater amount of “shrub infilling” (Figure 5-4). The central part of the study area also has a higher concentration of canopy loss, associated with fires. Figure 5-3: Map of structural vegetation classes across the study area. The inset maps show detail in the 30 m change product. Panel A also highlights structural change within the extent of two fires that burned in 1999 and 1968 (black outline). Water and unclassified pixels areas are shown in white. 90 Figure 5-4: Distribution of transitional vegetation classes across the study area relative to the 1995 forest limit. For visualization, 30 m pixels were aggregated into 300x300 m cells and the number of pixels of each class were counted per cell. Fire history accounts for a high proportion of structural change, with 80% of transitional pixels located in fire boundaries. Older fires that burned before 1985 were more likely to show changes in forest structure with 17.3% and 6.1% of these areas classified as “forest growth” or “new forest”, respectively ( 91 Table 5-3). In undisturbed areas, “forest growth” and “new forest” covered 1.9% and 0.4%, respectively. As expected, recent fires had a higher proportion of pixels classified as “shrub infilling” and “canopy loss” compared to undisturbed areas ( Table 5-3). Table 5-3: Summary table showing the proportion of vegetation classes stratified by disturbance history within the Northwest Territories (%). Structural class Undisturbed Burned 1950-1985 Burned 1985-1999 Burned 2000-2011 Stable no canopy 52.3 7.2 8.0 4.4 Stable shrub (1-3m) 4.9 4.4 1.4 1.1 Stable forest (> 3m) 34.4 58.9 59.6 63.1 Stable low forest 1.9 4.5 6.8 1.9 Total Stable 93.5 75.0 75.8 70.5 Forest growth 1.9 17.3 0.7 0.2 Shrub infilling 2.6 0.5 14.2 23.6 Canopy loss 1.0 0.2 8.0 5.5 New forest 0.4 6.1 0.6 0.1 New shrub 0.5 0.9 0.7 0.1 Total Transitional 6.4 25.0 24.2 29.4 % of total 88.2 4.6 1.4 2.5 5.3.3 Change in the forest limit Overall, the extent of continuous 3m forest was similar between the two time periods (1985-1995 and 2010-2021; Figure 5-5). The area of continuous 3 m forest increased by 92 15,921 km2 (0.9% of the 1995 continuous forest extent). The area representing forest advance was 24,146 km2 and the area representing retreat was 8,225 km2. Advance was not uniform across the study area, and large parts of the 1995 forest limit were unchanged in 2021 (Figure 5-5). Areas of advance were spatially constrained and tended to occur between neighbouring areas of the forest limit. The Taiga Shield east of Hudson’s Bay showed more advance than western Canada, with 12,264 km2 of new forest area. The Taiga Shield and the Hudson Plains ecozone west of Hudson’s Bay showed the most extensive forest retreat (Figure 5-5). Analysis of climate variables along the forest limit showed that mean annual temperature has warmed by 1.08˚C and total annual precipitation has increased by 25 mm from 1985 to 2021. 93 Figure 5-5: Mapping retreat and advance of the northern limit of continuous 3 m forest using canopy height and presence in the 1985-1995 and 2010-2021 time periods. The inset map highlights a region where the forest limit has advanced northward. The canopy height data was mapped at 30 m spatial resolution. 94 5.4 Discussion In this research, I used lidar data and spectral-time series to map change in vegetation structure across the Canadian FTE over a 36-year period. My results support mapping conducted with high-resolution air photos that show limited treeline advance, despite increases in temperature and precipitation (Lantz et al., 2019; Maher et al., 2021; Timoney & Mamet, 2020; Travers-Smith & Lantz, 2020). I also show overall stability in the cover of shrub (1-3 m) and forest (>3 m) canopies across the ecotone. The distribution of “new shrub” and “new forest” classes exhibited divergent patterns across the ecotone. Forest expansion was concentrated south of the historic forest limit, while shrub expansion was more frequent north of the forest limit. These findings are also consistent with analysis of Canada’s forested ecozones by Hermosilla et al. (2025), who reported infilling of trees in proximity to existing treed areas as well as long-term recovery from wildfires predating the Landsat record in the mid-1980’s. The strong association between the type of vegetation change and the timing of fires indicates that current and future regimes will play significant role in decadal-scale vegetation dynamics. 5.4.1 Structural vegetation dynamics Meta-analyses of global treelines have suggested that the majority of treelines have advanced in elevation or latitude (Hansson et al., 2021; Harsch et al., 2009). However, sites representing latitudinal treelines are under sampled globally (Hansson et al., 2021). I observed limited evidence of systemic forest advance, despite increases in mean annual temperature and total precipitation at the forest limit. My mapping reveals local areas of both advance and retreat in the boundary of continuous forest, but highlights overall stability of this boundary. This is consistent with several studies using field-data and high-95 resolution mapping conducted across the Northwest Territories (Lantz et al., 2019; Travers-Smith & Lantz, 2020), Nunavut (MacDonald et al., 1998; Timoney, 2023; Timoney & Mamet, 2020) and northern Quebec (Gamache & Payette, 2004, 2005) documenting limitations in tree recruitment and range expansion beyond the northern treeline. In black spruce forest across the Taiga Shield, edaphic factors including nutrient-poor and shallow soils underlain by bedrock may restrict expansion of the boreal forest even with reduced climate limitations (Timoney, 2023). Disturbance history shows that older fires that burned prior to 1985 were primary divers of increasing vegetation structure. Compared to undisturbed regions, these fires had higher proportions of “forest growth”, “new forest” and “new shrub” transitions. This is similar to analysis of greening trends in the Canadian boreal, which were concentrated and magnified in disturbed rather than undisturbed areas (Sulla-Menashe et al., 2018). These findings indicate that climate driven increases in forest height and tree density are subtle and less frequent in comparison to disturbance-recovery processes. As expected, recent fires were more likely to show decreases in vegetation structure, represented by declining height over time. This reflects the time required for tall-stature vegetation to establish and reach densities sufficient to be detected in medium resolution satellite data (Timoney et al., 2019). However, this could also indicate more persistent shifts in vegetation communities as upright shrubs can also exhibit long-term increases in cover following fire (Gaglioti et al., 2021; Lantz et al., 2010; Miller et al., 2024). 96 5.4.2 Remotely sensed indicators of vegetation structure My results highlight the importance of multiple spectral indices for understanding vegetation change in the FTE. Longer wavelengths including the SWIR2 band, and the TCB index which integrates reflectance across all spectral bands were significant predictors of canopy presence and height. Overall, tall-stature vegetation was characterized by low brightness and lower NIR and SWIR reflectance, likely driven by the density of coniferous forests (Olthof & Fraser, 2007). Conifers have a higher proportion of darker woody material and foliage that casts shadows that decreases overall reflectance within medium spatial resolution pixels (Cohen & Spies, 1992; Pickell et al., 2016). In contrast, low-stature vegetation (< 3 m) was characterized by higher NIR reflectance and TCG values, likely driven by the greater horizontal leaf area of tall deciduous shrubs compared to conifer needles, which are arranged vertically and may be obscured in remote sensing imagery (McManus et al., 2012; Walker et al., 2003). These findings show that spectral greening at the northern margin of the boreal may not be indicative of widespread forest advance. Thus, care should be taken to interpret greening trends across the ecotone, especially in relation to sparsely forested areas. 5.4.3 Limitations and future directions ICESat-2 provides measurements of vegetation structure across the entire circumpolar Arctic that can support mapping at broad spatial scales (Lang et al., 2023; Sothe et al., 2022). However, several studies show that ICESat-2 can overestimate the height of low-stature vegetation which can propagate in modeled products (Feng et al., 2023; Neuenschwander & Pitts, 2019). As well, model validation in Chapter 4 showed greater error in predicting low canopy heights < 2 m. To mitigate these uncertainties in this 97 analysis, I focused on mapping change between broad-structural classes. I assumed that overestimation or underestimation of canopy height is consistent over the time-series, and that pixels without large structural change will be classified as stable. As such, the frequency of transitional vegetation classes is likely to be conservative. The ±1.69 threshold in the t-statistic used to classify “growing forest” and “shrub infilling” is also somewhat arbitrary. The “forest” class encompasses all pixels with height > 3 m throughout the time-series however, and the threshold was intended to capture meaningful change in height within these pixels. While the total number of pixels in the transitional classes would increase if the threshold was closer to 0, the overall distribution of pixels within fires versus undisturbed areas, and the frequency of these classes across the ecotone are expected to show similar patterns. To reflect the uncertainty in low-stature vegetation I also do not derive linear trends in height within the shrub class (1-3 m). Future work focusing on growth of tundra shrubs should incorporate higher resolution lidar data available at regional scales, including airborne lidar data from the LVIS instrument in Alaska and northwestern Canada (Blair & Hofton, 2020), and the growing availability of photogrammetric data from drones (Cunliffe et al., 2022; van der Sluijs, Saiet, et al., 2023b). The spatial resolution of my mapping (30 m) also limits the ability to detect more recent establishment of northern spruce seedlings which may take up to 75 years to be visible in satellite imagery (Timoney, 2023). Furthermore, using spaceborne lidar I was only able to detect upright tree growth forms rather than matted or “whorled” structures which are common for black spruce at the northern treeline (Lavoie & Payette, 1992). Higher-resolution mapping and long-term monitoring plots are still needed to 98 understand these subtle vegetation changes and determine if shifts in the position of the treeline will occur over the coming decades or centuries. 5.4.4 Conclusions I used satellite remote-sensing to map multiple types of structural vegetation change in 183 million ha of the Canadian FTE between 1985 and 2021. Forest and shrub range expansion showed divergent patterns across the ecotone, with a greater proportion of shrub expansion occurring above the historic forest limit. The northern forest limit showed localized expansion representing ~15,000 km2 (0.9%) of new forested area, but the majority remained stable over the past 36 years. I show that the forest limit has also experienced increases in mean annual temperature and precipitation. This suggests that northward forest advance is falling behind changes in climate at the national scale. Instead of climate-driven increases in vegetation height, newly forested areas and forests with increasing trends in height were strongly associated with fire history, highlighting the importance of disturbance-recovery dynamics in future vegetation change. 99 Chapter 6: Long-term vegetation change across the forest-tundra ecotone using historic air photos and satellite time-series 6.1 Introduction The forest-tundra ecotone is the transition zone between the boreal forest and the tundra and covers 1.4 million km2 globally (Brandt, 2009; Payette et al., 2001). At the regional scale, analysis of air photos and high spatial-resolution imagery show a variety of local responses to climate (Frost & Epstein, 2014a; Terskaia et al., 2020). In northwestern Canada, the range of upright shrubs are expanding northward, replacing graminoid and herbaceous tundra (Fraser et al., 2014a; Moffat et al., 2016). In comparison, northern treelines have shown both advance (Danby & Hik, 2009), retreat and stability (Timoney & Mamet, 2020). Taken together, these analyses indicate that the magnitude and drivers of vegetation change can be variable and regionally specific. Given the variety of methods, data sources, image types, spatial resolution and temporal representation across studies it is challenging to predict continental-scale dynamics from remote sensing imagery. Satellite imagery has been used to model vegetation attributes, by linking training data to pixel-level spectral values and other predictor variables, such as elevation or gridded climate variables (Beck & Goetz, 2011; Berner & Goetz, 2021; Montesano et al., 2009). However, validating these models over large areas remains difficult. Sub-pixel changes unrelated to vegetation, including snow and water, can confound interpretation of spectral trends (Cunliffe et al., 2020; Hoad et al., 2025; Loranty et al., 2018). Differences in the time-period analyzed also introduces discrepancies in the magnitude and direction of change across studies, with many satellite-based archives only spanning one to four decades (Wulder et al., 2022). Subtle changes in vegetation 100 over these time scales can be difficult to capture with contemporary satellite imagery due to the relatively slow reproduction cycles and growth at high latitudes (Beamish et al., 2020). Historic air photos can be used to map changes in vegetation structure predating the satellite record. In Canada, the National Air Photo Library (NAPL) holds over 6 million aerial images acquired between 1920 and the present day (Natural Resources Canada, 2011). Photos at different spatial scales and time periods have been used to map vegetation at several sites across the Canadian forest-tundra ecotone (Danby & Hik, 2009; Moffat et al., 2016; Timoney, 2023; Timoney et al., 1992; Timoney & Mamet, 2020; Travers-Smith & Lantz, 2020). Interpreters use variation in color, shape and texture of images to differentiate land cover types, and in some cases map the density and size of individual stems (Brodie et al., 2019; Danby & Hik, 2009; Lantz et al., 2019). Systemic sampling across an image can be used to estimate total land cover proportions and the frequency of specific land cover transitions (Olofsson et al., 2014). These data have been used to map the position of the 1:1 forest-tundra limit across western Canada (Timoney et al., 1992), local changes in tree height and density at the treeline (Danby & Hik, 2009) and to compare advance of northern tree and shrub lines (Lantz et al., 2019; Travers-Smith & Lantz, 2020). Although the cost, time and logistic requirements of air photos challenges repeated monitoring efforts, the spatial detail and ability to map change before the satellite record makes them a powerful tool to extend observations. In this research I used highly detailed, yet spatially limited information from aerial photographs to improve interpretation of satellite spectral time-series. I compared change 101 in forest and shrub cover interpreted for historic air photos and high-spatial resolution optical imagery with the same Landsat-based classification of structural change in Chapter 5. These comparisons were used to test the agreement between change detection methods (photo interpretation vs Landsat change classification) across treeline sites and time periods. To do so, I paired historic air photos from the 1930’s and 1970’s with very high-resolution satellite imagery (0.4 m) acquired in the 2020’s. Air photos were acquired at 1:12,000 – 1:20,000 scale, for four latitudinal transects across northwestern and central Northwest Territories, Canada. Following established practices of the NAPL, the air photos were digitized to an effective pixel size of 0.2-0.3 m. I grouped photos by transect and time-period to asked the following research questions: 1) Are there consistent patterns in forest and shrub change across the ecotone between sites? and 2) Are these patterns consistent with estimates of change in vegetation structure derived from satellite time-series? 6.2 Methods 6.2.1 Overview Key data processing steps are outlined in Figure 6-1. I acquired panchromatic air photographs (n = 27) at scales from 1:12,000 to 1:20,000 from the National Air Photo Library (NAPL) across four study sites (Figure 6-1). Photos at two sites were captured in the 1970’s, and photos at the remaining sites were captured in the 1930’s. First, I used the air photos to assess the historic percent cover of forest, shrub and herbaceous vegetation within 300x300 m grid cells (n = 2,063) encompassing the entire extent of the air photos. To map change in forest and shrub cover I compared historic air photographs to pansharpened World View-2 and 3 images from 2023 and 2024 at a level of spatial 102 detail similar to the digitized air photos (0.4-0.5 m). I used these data to model the probability of increasing forest and shrub cover as a function of distance to the northern forest limit. In the second stage of analysis, I used the photo interpretation to validate Landsat-based estimates of change described in Chapter 5. Figure 6-1: Flow chart of the primary steps in the data analysis and associated outputs. As noted in the key, the color and shading of the boxes indicate which data source and scale was used to derive each output. 6.2.2 Study Area My study includes four air photo transects of variable lengths separated by 200-400 km, crossing the northern limit of continuous forest in the NWT, Canada (Figure 6-2). Climate across the region is characterized by short summers (June-August) with mean temperatures between 9˚C and 14˚C (Table 6-1). Average winter (January-March) temperatures vary between -26˚C and -20 ˚C. Data from three long-term monitoring 103 stations within the study area (Figure 6-2) indicate 0.8-1.2 ˚C of warming between 1970 and 2020, with stronger increases in winter temperatures compared to summer (Table 6-1). Trends in precipitation are variable, showing wetting and drying trends in the south and northwest, respectively (Table 6-1). Overall, the two southernmost stations (Yellowknife and Fort Reliance) received more annual precipitation than the northernmost station in Kugluktuk. Transect 1 (Coppermine) is the coldest, driest and northernmost site located within the Coppermine Upland (High Subarctic) ecoregion. The Coppermine site hosts open stands of white spruce and an abrupt transition to treeless tundra above the forest limit. Upland bedrock areas are composed of lichens, heath and shrub communities. The Coppermine River flows adjacent to the transect and more dense spruce and shrub woodlands are present within the river valley. Transect 2 is located in the Great Slave Upland (Low Subarctic) ecoregion at intermediate latitude (Ecosystem Classification Group, 2008). Woodlands in the Great Slave Upland (GSU) are mainly composed of black spruce (Picea mariana) and paper birch (Betula papyrifera). Low-intensity fires are common and the westernmost portion of this transect experienced fires in 1976, 1977 and 1994 (Figure 6-2). The southernmost transects, Transect 3 (Whitefish West) and Transect 4 (Whitefish East), are both located within the Whitefish Plain (High Subarctic) ecoregion. The sites have low relief and host dense stands of black spruce woodlands in the south. The transition to tundra vegetation is more diffuse with pockets of spruce woodlands present above the continuous forest limit. Treeless tundra sites in this region are composed of shrub and lichen communities. The southernmost portion of Transect 4 (Whitefish East) 104 experienced wildfires in 1994, 1997 and 1998 and most of Transect 3 (Whitefish West) is within a recent 2014 fire scar (Figure 6-2). Figure 6-2: Study area maps (top) showing the extent of historic air photos and modeled canopy height from Chapter 4 across four transects representing the transition from forest to tundra land cover. The bottom map shows the location of each transect (red circles) relative to the forest limit (white line). The grey squares indicate long-term weather stations close to the study sites. 105 Table 6-1: Summary of climate normals for three long-term monitoring stations within the study area. Climate normals are from the 1971-2000 and 1991-2020 periods. Data are publicly available and compiled by Environment and Climate Change Canada (https://climate.weather.gc.ca/climate_normals/index_e.html). Summer temperatures are calculated for June-August, and winter temperatures are January-March. Note data for the 1991-2020 period are not yet available at Fort Reliance. Mean summer temp (˚C) Mean winter temp (˚C) Total annual precipitation (mm) Degree days >5˚ Kugluktuk 1971-2000 8.2 -26.8 249 387 1991-2020 8.9 -26.0 233 440 Yellowknife 1971-2000 14.8 -22.5 280 1077 1991-2020 15.1 -21.6 293 1108 Fort Reliance 1971-2000 12.4 -25.0 271 815 1991-2020 13.5 -23.2 --- --- 6.2.3 Historic air photos Photo selection from the National Air Photo Library was opportunistic based on availability, image quality and cost, and are not necessarily representative of the entire study area. Photos were considered for selection if they met the following criteria: 1) within 50 km of the forest limit mapped in Chapter 5, 2) acquired at 1:20,000 scale or finer, 3) snow and ice free, 4) and acquired between June 15-September 15. To be considered for analysis tree stems must be visible in the historic imagery allowing us to distinguish forest and shrub cover from herbaceous and unvegetated areas. I used the photo extents 106 from the metadata to ensure that photos also overlapped with the available contemporary high spatial-resolution satellite imagery for change comparison. Having identified four areas with historic imagery matching these criteria, I selected 27 photos along transects crossing the historic (1985-1995) limit of continuous forest defined in Chapter 5. Photos from Transect 2 (GSU) and Transect 3 (Whitefish West) were acquired in the 1930’s (1930 and 1937, respectively) and photos for Transect 1 (Coppermine) and Transect 4 (Whitefish East) were acquired in the ~1970’s (1969 and 1971, respectively). Air photos were georeferenced in ArcGIS Pro version 3.4.3 against high-resolution satellite base map derived from pansharpened WorldView-2 and WorldView-3 imagery at 0.4 or 0.5 m spatial resolution, respectively. The “projective” image transformation type was used with a minimum of 15 control points. The size, scale and relative georeferencing error of the air photos varied by transect (Table 6-2). The average georeferencing error between the high-resolution datasets is expected to be less than half the width of a Landsat pixel (15 m). In comparison, following digitization the pixel size of the georeferenced air photos was between 0.2 and 0.3 m (Table 6-2). 107 Table 6-2: Air photo characteristics and metadata across transects crossing the historic forest limit. Transect Air photo scale Air photo dates (yyyy-mm-dd) Total photo area Mean georeference RMSE Satellite image dates (yyyy-mm-dd) Air photo pixel size Transect 1 (Coppermine) (n=5) 1:15,000 1969-09-04 99.4 km2 5.0 m 2024-06-01 0.25 m Transect 2 (GSU) (n=8) 1:20,000 1937-07-09 71.94 km2 3.29 m 2019-06-27 0.30 m Transect 3 (Whitefish West (n=6) 1:12,000 1930-07-25 23.06 km2 7.22 m 2022-07-11/ 2024-07-23 0.20 m Transect 4: Whitefish East (n=8) 1:20,000 1971-07-29 45.7 km2 8.3 m 2023-10-09 0.30 m 6.2.4 Landsat structural vegetation change I compared photo interpreted change from the air photos and high-resolution satellite imagery with the Landsat classification of structural change (hereafter referred as LSC) developed in Chapter 5. Here the original nine classes were simplified into six: stable no canopy, stable shrub, stable forest, increasing forest height and cover, increasing shrub cover and decreasing vegetation height and cover (Table 6-3). 108 Table 6-3: Descriptions of the nine Landsat-derived structural vegetation types classified at 30 m spatial resolution. Decision rules describe logic used to classify change in canopy presence and height across six time-periods. The simplified classes used in this study are shown in the last column. Vegetation classes Decision rules Simplified classes Stable no canopy Majority no canopy class Stable no canopy Stable shrub (1-3 m) Majority shrub class Stable shrub Stable forest (>3 m) Majority forest class & no trend in height Stable forest Stable low forest Multiple transitions between forest and shrub but no trend in height Stable forest Forest growth Majority forest class & significant increasing height Increasing forest structure New forest Change from no canopy or shrub to forest Increasing forest structure New shrub Change from no canopy to shrub Increasing shrub structure Forest to shrub transition Forest to shrub change OR majority forest with decreasing height Decreasing structure Canopy loss Change from forest or shrub to no canopy Decreasing structure 6.2.5 Photo interpretation I estimated historic proportional land cover from the historic aerial photos for the following five land cover types: forest, shrubs, herbaceous vegetation (including graminoids, forbs, mosses, lichen), barren and water (Figure 6-3). I overlaid each air photo with a grid of 300x300 m cells (n = 2063) encompassing the entire extent of the air photos (240 km2) and estimated the percent cover of vegetation types within each cell. Percent cover was 109 estimated using following ranges: very low (1-5 %), low (5-25 %), moderate (25-40%), high (40-60 %) and very high (> 60 % cover). Due to the large cell size, I expect that the impact of georeferencing misalignment (~3-9m) between the photos and satellite imagery would be negligible. Images were viewed at multiple scales to identify land cover types and ensure mapping consistency across cells. Forests were identified based on the presence of trees, distinguished by their conical shape and long shadows (Figure 6-3). The shrub class included both low and tall shrubs. Tall shrubs were identified based on their circular crowns and lack of shadows, while low shrubs were distinguished by their darker and textured appearance relative to lighter surrounding herbaceous land cover (Figure 6-3). Interpretation of land cover types was aided by oblique aerial photography from nearby transects available from the NWT Ecosystem Classification Group (2013; photos available from the GWNT Centre for Geomatics: Geocortex Viewer for HTML5). A single interpreter was used to ensure mapping consistency across photos and transects. Next, I compared the percent cover for each cover type with pansharpened 2023-2024 satellite imagery and assessed change within the same 300x300 m cells (Figure 6-3). For the contemporary imagery, shrubs were identified by their dark green color and contrast with surround herbaceous vegetation, rather than texture which was not clear in the imagery. All remaining herbaceous vegetation was grouped and identified based on its lighter color and homogenous texture. Barren landcover was defined as unvegetated rock outcrops. Increasing and decreasing percent cover was assessed within the following ranges: no change, trace (1-5 %), moderate (5-10 %), high (10-20%) and very high (>20 %). The NWT Fire History dataset (NWT Centre for Geomatics, 2024) and the product 110 Composite2Change (Hermosilla et al., 2016) were used to identify stand replacing wildfires the burned between 1950 and 2022. The final photo interpretation dataset shows the historic proportion of land cover classes, the magnitude and direction of change between historic and modern images, and the fire history encompassing the extent of all air photos within 300x300 m cells. 6.2.6 Modeling probability of forest and shrub expansion I used percent change from all 300x300 m cells (n = 2063) to model the probability of increasing tree and shrub cover within each transect using a Generalized Linear Mixed Effect Model. Distance to the 2021 forest limit from Chapter 5 was a global fixed effect and random slopes and intercepts were used for each combination of vegetation type (forest or shrub) and transect. Change in tree and shrub cover was used as a binary response variable with a value of 0 for cells with no change or decreasing cover, or 1 for cells with increasing cover. A negative treeline distance indicates that the cell centrepoint was below the forest limit and a positive distance indicates that the centrepoint was above the forest limit. The model was fit using the R package lme4 with a binomial link function (Bates et al., 2015). To compare the effect of distance to treeline between sites and vegetation types, I reported the slope and intercept coefficients across levels and plotted the predicted probability of increasing vegetation cover against treeline distance within each transect. 111 Figure 6-3: Examples of air photos and satellite imagery used to map change in forest and shrub cover across four transects crossing the forest-tundra ecotone. The bottom row shows the Landsat structural change classification maps at 30 m spatial resolution (Travers-Smith et al., 2025) and the black outline shows the extent of the fine-scale imagery within each transect. White areas are lakes and waterbodies that were removed from analysis. In the WorldView imagery from Transect 4, tamarack (Larix laricina) is visible on the edges of the black spruce stand. 112 6.2.7 Correlation with Landsat time-series I compared the visually interpreted change with the Landsat-derived classification within 300x300 m grid cells and individual 30 m pixels (n = 646). In the 300 m grid cells, I compared the percent cover of herbaceous, shrub and forest estimated from the air photos to the proportion of the cell covered by corresponding stable LSC classes (no canopy, shrub, forest, respectively). To directly assess how fine-scale change in tree and shrub cover were captured by the LSC data, I also sampled 646 individual 30 m pixels representing stable and increasing forest and shrub classes. For each transect, I randomly sampled up to 90 stable pixels, 60 increasing forest pixels and 30 increasing shrub pixels. These strata were proportional to the frequency of each class in the LSC data. The minimum distance between pixels was 60 m, and in cases where there were fewer than 30 pixels of specific class within a transect all available pixels were chosen. In each sampled pixel, I examined the air photos and high-spatial resolution satellite imagery and recorded whether there was increasing forest or shrub cover in the pixel. I used these data to test whether the Landsat time series corresponds to fine-scale increases in tree and shrub cover identified using the air photos and high-resolution WorldView imagery. 6.3 Results 6.3.1 Visually interpreted change Change in vegetation cover was variable across transects and air photo acquisition dates (1970’s and 1930’s). Table 6-4 shows the proportion of 300x300 m grid cells exhibiting change in forest and shrub cover using the air photographs and the 2020’s high-resolution satellite imagery. Forest cover was generally stable for Transect 1 (Coppermine) and Transect 2 (GSU), with 22 % and 36 % of cells showing some level of increasing cover 113 (Table 6-4). Transect 3 (Whitefish West) and Transect 4: Whitefish East showed higher frequency of change with 43 % and 65 % of cells with increasing forest cover, respectively. Across Transect 3 (Whitefish West), the majority (62 %) of these increases were small (covering < 10 % of the cell). In contrast, the magnitude of increasing forest cover at Transect 4 (Whitefish East) was more evenly distributed with a similar proportion of cells exhibiting trace (< 5%) and high (> 20%) increasing cover (Table 6-4; Figure 6-4). Notably, the increase in forest cover across Transect 4 (Whitefish East) appears driven by tamarack (Larix laricina) rather than black spruce (Picea mariana), which was distinguished by its bright yellow crown in the early autumn satellite imagery (Figure 6-3). Transect 3 (Whitefish West) exhibited the highest rates of change in shrubs with 93 % of cells showing some level of increasing shrub cover (Table 6-4). Shrub cover at Transect 1 (Coppermine) was the most stable with 78% of cells showing stability. Transect 2 (GSU) and Transect 4 (Whitefish East) had intermediate increases in shrub cover, with 44% and 58% showing increases, respectively (Figure 6-4). Fires impacted 21 % of all cells (n = 446). Transect 1 (Coppermine) had no disturbed cells and Transect 3 (Whitefish West) had the most, with 71 % of all cells burned (Table 6-4). More recent fires in Transect 3 (Whitefish East) and Transect 4 (Whitefish West) corresponded with increasing shrub cover in 90% and 82% of burned cells, respectively (Table 6-5). The older fires in Transect 2 (GSU) showed increasing forest and shrub cover in 13% and 28% of cells, respectively (Table 6-5). 114 Table 6-4: Percent of 300 m grid cells showing stability and change in forest and shrub cover mapped using visual interpretation of historic air photos and modern high resolution satellite imagery. Forest Cover Transect 1 Coppermine (1969) Transect 2 GSU (1937) Transect 3 Whitefish West (1930) Transect 4 Whitefish East (1971) No change % 88.8 74.4 39.2 16.9 Decreased % 0.0 2.5 17.7 18.3 Trace increase (+1-5%) 8.0 16.4 27.2 18.7 Increase (5-10%) 2.9 5.4 12.0 18.8 Increase (10-20%) 0.2 1.2 3.8 17.0 Increase (>20%) 0.0 0.2 0.0 10.2 Shrub Cover No change % 78.8 66.6 7.0 42.5 Decreasing % 0.2 0.0 0.0 0.6 Trace Increase (+1-5%) 8.5 21.2 22.2 17.8 Increase (5-10%) 7.8 8.4 39.2 19.5 Increase (10-20%) 3.9 3.2 27.2 12.2 Increase (>20%) 0.7 0.5 4.4 7.5 Total grid cells N = 411 N= 590 N = 158 N = 904 115 Table 6-5: Summary of fire history for each transect showing the proportion of 300 m cells with fire and the year of the fires derived from the NWT Fire History and Composite2Change datasets. Transect 1 (Coppermine) did not have any known fire. Percent change indicates the proportion of burned 300 m cells with increasing or decreasing forest and shrub cover. Transect 1 Coppermine (1969) Transect 2 GSU (1937) Transect 3 Whitefish West (1930) Transect 4 Whitefish East (1971) Percent of cells burned % 0 25.1 71.1 20.0 Decreased forest % NA 6.1 23.7 83.9 Increased forest % NA 13.3 38.1 13.3 Increased shrub % NA 28.4 90.7 82.2 Fire dates NA 1976, 1977, 1994 2014 1994, 1997, 1998 116 Figure 6-4: Change in forest and shrub cover for Transect 4 (Whitefish East) mapped in 300 m cells using the interpretation of historic air photos (1971) and high spatial resolution satellite imagery (2023). 117 6.3.2 Probability of forest and shrub expansion The likelihood of increasing forest and shrub cover also varied with distance to the forest limit (Figure 6-5). Forest cover was more likely to increase closer to the forest limit for every transect except Transect 1 (Coppermine), which showed a low probability of increasing forest cover across the entire ecotone (Figure 6-5). Similarly, shrub cover was also more likely to increase close to, or above the present forest limit, with significant increasing trends in Transect 1 (Coppermine) and Transect 2 (GSU; Figure 6-5). The Transect 3 (Whitefish West) and Transect 4 (Whitefish East) transects showed less variation in the probability of shrub increase across the ecotone. The probability of increasing shrub cover was generally greater than forests, with the exception of Transect 4 (Whitefish East; Figure 6-5). 118 Figure 6-5: Probability that tree and shrub cover will increase relative to the 2021 limit of continuous forest using all grid cells (n = 2063). The solid lines show the predicted probability for each combination of transect and vegetation type and the shaded area shows a 95 % confidence interval of the prediction. Points represent individual grid cells and the grey-dashed line shows the position of the 2021 treeline. 6.3.3 Agreement with Landsat time-series The level of agreement between vegetation cover types based on image interpretation and Landsat classification in 300x300 m cells was also variable (Figure 6-6). Very low (1-5 %) and low (5-25 %) cover of stable herbaceous vegetation cells was underestimated in the LSC data. Conversely, stable forest cover was overestimated in the LSC classification, especially when forest cover was low (Figure 6-6). Stable shrub cover 119 showed the least agreement between datasets. The LSC data consistently underestimated the cover of shrub mapped using the high-resolution imagery (Figure 6-6). At the pixel-scale the level of agreement between LSC classes and interpreted change varied between phot acquisition year and the vegetation type (Figure 6-7). Agreement between the LSC data and visually interpreted change was generally high for the stable classes, with 80% and 91% correlation for pixels classified as stable forest and shrub respectively. Overall, the LSC data were more likely to underestimate the magnitude of increasing forest and shrub cover (Figure 6-7). While false positives (where the LSC data detected a change that was not identified in image interpretation analysis) occurred in 12% of sampled pixels, false negatives occurred in 50% of pixels with forest and shrub change. However, the level of agreement varied by vegetation type and photo year. The highest agreement was found for increasing forest cover using the 1970’s photos as the baseline, which was detected in 65% of pixels showing change. Using the 1930’s air photos as the baseline only 44% of pixels with increasing forest cover were classified as such in the LSC data (Figure 6-7). Change in shrub cover showed similar patterns, with LSC data detecting 57% and 33% of shrub cover for the 1970’s and 1930’s photos respectively. (Figure 6-7). 120 Figure 6-6: Boxplots showing correspondence between percent cover of forest, shrub and herbaceous vegetation mapped on air photos and stable vegetation cover derived from Landsat time series. Percent cover represents the proportion of undisturbed 300 m2 grid cells where vegetation is present. 121 Figure 6-7: Confusion matrices showing correspondence of vegetation change interpreted using high-resolution air photos (1970’s and 1930’s) and satellite imagery (2020’s), with a Landsat time-series classification assessed in 30 m pixels (n = 646). Change was binary with 1 indicating increasing vegetation structure and 0 indicating no change or decreasing cover. The cell values show the proportion of Landsat pixels in agreement with the direction of change interpreted from the high-resolution imagery. The darker shades of green indicate higher correspondence. 122 6.4 Discussion In this research, I used high spatial-resolution air photos and satellite imagery to map tree and shrub expansion across four treeline transects in NWT, Canada and compared these changes with Landsat time-series data. I showed that differences in fire history influenced the type and magnitude of change across transects. These dynamics were also broadly reflected in the satellite time-series data, however varying performance highlights the need for careful interpretation and high-quality validation data. Overall, I found that shrub cover was more likely to increase compared to forests at three of the four sites. Forest cover was also more likely to increase closer to, or above the historic forest limit at three of the sites. This reflects a pattern where fires below the treeline decrease forest cover while forest expansion occurs above the treeline. Differences in forest and shrub expansion across sites were likely driven by local factors such as fire history (Lantz et al., 2010; Walker et al., 2021a), climate (Frost & Epstein, 2014a; Lantz et al., 2019), and topography (Terskaia et al., 2020). The southernmost sites hosting black spruce woodlands had greater increases in forest cover than the northernmost white spruce site, which could reflect warmer and wetter conditions at the southern sites. Correlation of forest and shrub expansion with a Landsat-based change detection approach showed that forest advance was better captured than shrub advance. At the scale of individual pixels, Landsat change detection tended to underestimate the actual frequency of change. The level of agreement was also reduced when using the 1930’s photographs as a baseline compared to the 1970’s photos, likely due to vegetation dynamics between the 1930’s and the beginning of the satellite record (1985). My analysis of multiple sites across the Canadian forest-tundra ecotone highlights 123 challenges in generalizing local patterns and the importance of validating change from medium resolution satellite data. 6.4.1 Climate and fire history Fire history was strongly related to vegetation dynamics at the southernmost black spruce sites, driving large decreases in forest cover below the treeline. Smaller isolated woodlands above the treeline may be less likely to burn, and tended to increase in area over time compared to densely forested regions below the treeline. This is consistent with national scale trends in treed area, where forest expansion was more likely to occur in northern compared to southern ecozones, and in proximity to existing treed areas (Hermosilla et al., 2025). Transect 4 (Whitefish East), also exhibited this trend showing strong increasing probability of forest cover expansion across the ecotone. However, the magnitude of change in forest cover is likely unbalanced with expanding tree area above the treeline outweighed by recent losses from forest fires below the treeline (Rotbarth et al., 2023; Timoney et al., 2019). The recent 2014 fire across most of Transect 3 (Whitefish West) may also have contributed to the high (93%) rates of shrub expansion at this site (Lantz et al., 2010). Conversely, Transect 1 (Coppermine) was the only transect with no known fires and showed the most stability. Differences in climate may have also influenced patterns in forest and shrub expansion across sites. Transect 1 (Coppermine), was the northernmost as well as coldest and driest site, with very little change in the area covered by white spruce woodlands. In comparison, the three warmer sites closer to Yellowknife and Fort Reliance showed more frequent fires and a higher proportion of cells with forest expansion. Previous work 124 suggests that precipitation and growing season length rather than air temperature are stronger controls of forest and shrub expansion (Frost & Epstein, 2014b; Maher et al., 2021). Although few long-term monitoring stations are publicly available in the study region, these records suggest that precipitation in the southernmost sites may have increased more than in the northwest. The width of the forest-tundra transition could also explain differences in forest advance observed across transects. Abrupt treelines occur when there is a strong environmental gradient that limits vegetation expansion, while diffuse treelines may experience less reproductive restrictions (Epstein et al., 2004; Montesano et al., 2020). Transect 1 (Coppermine) represents an abrupt transition between forest and non-forested tundra across steep topographic gradients. Here I observed very little increase in forest cover above the historic forest limit compared to more diffuse transitions present across other sites. In contrast, Transect 4 (Whitefish East) and the surrounding area hosts more isolated spruce stands above the forest limit (Timoney, 2023), which provide a source for new seed and could facilitate range expansion over the observation period (Dial et al., 2022). Notably, this expansion appeared to be driven by tamarack (Larix Laricina), rather than black spruce. While it is unclear whether this is common to other treeline sites, labelled oblique air photos in the Whitefish Plain confirm mixed tamarack and black spruce stands are present at the treeline. Additionally, Baltzer et al., (2021) also note that post-fire tamarack regeneration occurs infrequently across boreal black spruce forests. Additional field measurements, or other photographs from this area are needed to confirm species-specific tree expansion above the current forest limit. 125 6.4.2 Landsat-based change classification The Landsat-based classification of vegetation change underestimated the frequency of actual forest and shrub expansion at the pixel level. Increasing forest and shrub cover were only detected in the Landsat classification 50% of the time across both time periods. However, agreement varied by vegetation type and the year of the air photo acquisition. Landsat change showed higher agreement using the 1970’s photos as a baseline compared to the 1930’s which could reflect better image quality of the 1970’s photos, or closer temporal alignment with the Landsat record starting in 1985. If the majority of change in vegetation cover occurred prior to the Landsat record, these changes would be captured using the historic air photographs but not Landsat time-series and reduce the correspondence between datasets. Differences in vegetation change could also be because the lidar-informed remote sensing model is based on vertical structure while the photo interpretation methods primarily detect horizontal cover. Additionally, at the scale of a single 30 m pixel, georeferencing error is expected to have a larger impact than the mapping conducted on a grid with 300x300 m cells. It is also likely that spectral variation within a 30 m pixel is driven by factors other than tree and shrub change, including changes in understory composition (i.e. mosses and lichens), or soil and canopy moisture (Fraser et al., 2017; Myers-Smith et al., 2020). Previous validation of modeled tree cover at 30 m resolution in the forest-tundra show greater uncertainty in open stands due to mixing with non-forested components in the pixel (Loranty et al., 2018; Walker et al., 2021). The cover of shrubs was also consistently underestimated in the Landsat-based models compared to the photo mapping. This is likely due to the height of shrubs detected in both 126 datasets. The Landsat classification uses ICESat-2 spaceborne lidar to model canopy height with the shrub class defined as vegetation 1-3 m in height, thus low shrubs are not consistently detectable with ICESat-2. In the photo interpretation data low and tall shrubs were not separated, resulting in much higher shrub cover in the interpretation dataset. Other Landsat-based models that are trained on datasets that better represents low stature vegetation may be better suited to separate areas of low and tall shrub cover (Orndahl et al., 2022). Recent remotely sensed models using harvested biomass (Orndahl et al., 2025) and percent cover (Macander et al., 2022) in the tundra may also be more sensitive to variation in low-stature vegetation. Due to differences in modeling approach and training data, my specific Landsat change detection approach may not be representative of all vegetation models for the forest-tundra in terms of accuracy or potential biases. However, the environmental covariates, modeling approach and validation of the Landsat-based approach are typical for this type of vegetation modeling (Coops et al., 2021; Hermosilla, et al., 2022b). 6.4.3 Limitations and future work The selection of historic air photos was opportunistic and restricted to transects that crossed the historic forest limit. Due to these restrictions, I did not acquire repeat aerial photography from the same sites at multiple times over the study period. To better understand rates of vegetation change over time, future work could compare spectral time series to repeat aerial photography with multiple image dates and within the Landsat record (1985-present). For example, Terskaia (2020) examined vegetation change across three image dates in Alaska. They found consistent shifts in vegetation type between earlier and later periods, suggesting that analysis of two image dates will still 127 capture the majority of vegetation changes. This study was also limited by absence of ground-truth data to confirm photo interpretation results. However, labeled oblique aerial photographs taken as part of the NWT Ecosystem Classification (2008, 2009) greatly aided my interpretation. The initial search of the National Air Photo Library identified over 15,000 candidate photos within the Yukon, Northwest Territories and Nunavut from 1930 to 1999. This represents an information rich dataset which could be used to validate and augment other remote sensing analyses across northern Canada using similar protocols (Hermosilla, al., 2022a; Timoney & Mamet, 2020). By using photos across multiple sites and time periods, this study demonstrates the localized nature of vegetation change at the tree line and highlights opportunities to improve interpretation of broad-scale satellite remote sensing models. This research also demonstrates the need to contextualize satellite change detection approaches with higher spatial resolution data. 6.4.4 Conclusions I used historic air photos from the 1970’s and 1930’s and high spatial-resolution satellite imagery to map change in forest and shrub cover across four sites in the Canadian forest-tundra ecotone. I observed variable patterns in forest and shrub expansion across the ecotone reflecting differences in climate, local fire history, site conditions and forest density. The likelihood of forest and shrub expansion increased with proximity to the historic forest limit, and increases in shrub density are closely linked to recent fire history. Increases in air temperature were similar over the study area but southern sites had greater increases in total annual precipitation and are likely closer to change thresholds. 128 I also compared visually interpreted change with models of structural vegetation change derived from Landsat time-series and spaceborne lidar observations of canopy height. The Landsat product was better at representing tall-stature forests and herbaceous vegetation than shrub cover from photo interpretation. Agreement in the direction and type of vegetation change varied by vegetation type and the year of the air photo acquisition. Higher agreement was observed for forest rather than shrub expansion, and for transects using the 1970’s photos compared to the 1930’s photos. This is likely due to closer temporal correspondence between the Landsat record (1985-2021) with the air photo acquisitions in the 1970’s compared to the 1930’s. This research also highlights the importance of high spatial-resolution validation in remotely sensed models of vegetation change, and the utility of Canadian National Air Photo Library in supporting these efforts. 129 Chapter 7: Conclusions 7.1 Dissertation objectives The overall objective of my dissertation was to use multi-scale remotely sensed data to improve our understanding of shifting vegetation structure and implications for carbon storage across the Canadian forest-tundra ecotone. I addressed this objective through the following four research questions summarized below. How does fine-scale soil carbon storage vary with vegetation structure and across northern tree and shrublines? In Chapter 3, I used homogenized samples of the organic soil horizon (n=155) and high spatial resolution drone imagery to examine variation in soil organic carbon (SOC) storage across northern tree and shrublines in the Northwest Territories, Canada. I identified three local treeline site types representing gradients in site productivity and topographic variation using a PCA. Across treeline site types, I observed differences in C:N ratios and litter layer depth indicative of rates of soil carbon decomposition and inputs. SOC storage was slightly higher in mesic woodland sites compared to sloped woodland and tundra sites. Within treeline types, SOC storage increased slightly with increasing tree stem density and canopy height. My results suggest that the indirect effects of microtopography and broader scale terrain factors including slope position, and drainage characteristics are important mediators in SOC storage at northern treelines. I also linked fine-scale measurements of canopy height, productivity and stem density from drone imagery to medium resolution satellite imagery. Both canopy height and stem density 130 were negatively associated with greenness indices, and both attributes were more strongly correlated with spectral indices that emphasize shortwave-infrared reflectance. How can vegetation structure be mapped across the Canadian forest-tundra ecotone using Landsat and ICESat-2 data? In Chapter 4, I used lidar data from the Ice, Cloud and land Elevation Satellite (ICESat-2) to map canopy presence and canopy height across 180 million hectares of the Canadian forest-tundra ecotone. Random Forest models were used to predict 98th percentile canopy height from the ICESat-2 Land and Vegetation Height product (ATL08) using a suite of covariates from the Landsat satellite archive (2012-2021) and the ASTER Digital Elevation Model at 30 m spatial resolution. I assessed model accuracy using data from the Land, Vegetation and Ice Sensor (LVIS), a large-footprint airborne lidar system. The overall accuracy of the canopy presence classification was 89%, and canopy presence was detected with 88% accuracy. Independent validation of vegetation height using LVIS showed an overall R2 value of 0.53 and an RMSE of 2.23. These models showed improved performance against existing canopy height products for shorter vegetation in forest-tundra ecotone. Finally, I used these methods to map the northern limit of continuous 3 m forest across Canada and show that it has close agreement with previous mapping of the 1:1 forest-tundra boundary by Timoney et al. (1992). This work demonstrates the potential for combining spaceborne lidar and optical remote sensing at high latitudes to understand variation in horizontal and vertical vegetation structure. 131 How has vegetation structure changed and are changes consistent with northward movement of the forest limit? In Chapter 5, I used the modeling approach from Chapter 4 to quantify change in vegetation height from 1985 to 2021 using Landsat time-series data. I predicted canopy presence and height across six decadal time periods at 30 m spatial resolution. Change between time periods was used to classify nine stable and transitional vegetation classes. I linked patterns in vegetation change types to disturbance history and quantified retreat and advance of the northern forest limit. This mapping revealed divergent patterns in forest and shrub expansion across the ecotone. Shrub expansion was widespread across the ecotone, while forest expansion was concentrated below, or close to the forest limit. Increasing vegetation structure was strongly associated with fire history as 80% of mapped vegetation changes occurred in disturbed areas. The majority of forest growth and new forest expansion occurred in fires that burned over 40 years ago. Overall, the extent of forested area increased by 0.9% and the forest limit warmed by 1.08˚C, receiving on average 25 mm more annual precipitation. These results highlighted the high frequency of stand infilling in old fire scars below the treeline, as well as stable northern forest limits over large parts of the ecotone. My results improve interpretation of spectral greening observed at the northern margin of the boreal forest, and show that expansion of tall and low-stature vegetation exhibit different spectral responses over time. 132 How well does the vegetation change from the satellite record correspond with long-term change mapped using high spatial resolution historic air photos? In Chapter 6, I used high-resolution air photographs captured in the 1930’s and 1970’s and contemporary satellite images from the 2020’s to map fine-scale forest and shrub expansion across four transects crossing the northern treeline. I found that the likelihood of shrub and forest expansion was greater in close proximity to, or above, the historic forest limit at three sites. Forest advance was most limited at the northernmost site, which was drier, contained white spruce woodlands, and had steeper topographic gradients. Vegetation dynamics at the southernmost sites were driven by large fires and recovery in closed black spruce forests. To better understand how well multispectral satellite indices can capture these dynamics, I also compared these changes to Landsat time-series classification developed in Chapters 4 and 5. Agreement in the type and magnitude of change varied by vegetation type, and transect. Correlation was higher for increases in forest cover and when using the 1970’s historical photos versus the 1930’s photos. This research highlighted differences in tree and shrub expansion driven by climate and fire histories, and provided a direct comparison of vegetation change using two types of change detection approaches. 7.2 Key innovations 7.2.1 Expanding soil carbon observations in continuous permafrost In several northern treeline sites, soil carbon storage is greater in the tundra compared to the forest (Clemmensen et al., 2015; Parker et al., 2021; Wilmking et al., 2006). This implies that an advance of woody vegetation into the tundra could release large amounts 133 of soil carbon to the atmosphere (Parker et al., 2015). These results are also supported by experimental evidence of “rhizome priming effects” in permafrost soils (Friggens et al., 2025). To better understand the magnitude of carbon losses from tree and shrubline advance there is a critical need for observations across a range of permafrost and vegetation conditions throughout the circumpolar region (Clemmensen et al., 2021b). To date, many observations have been made at alpine treelines with no permafrost (Clemmensen et al., 2021a; Devos et al., 2022; Hartley et al., 2012; Parker et al., 2015), or in discontinuous permafrost (Wilmking et al., 2006; Ziegler et al., 2017). In Chapter 3, I address this research gap by measuring soil carbon storage across local treelines for a small area of the Mackenzie Delta region underlain by continuous permafrost, which are currently underrepresented in the literature. My results did not show large differences in SOC storage within or between the treeline site types, and SOC storage increased slightly with increasing tree stem density and canopy height. In this research, I also show how drone imagery can be used to quantify vegetation structure and microtopography, two important drivers of carbon storage. These methods are broadly applicable to other treeline sites and could help standardize “forest” versus “tundra” vegetation structure across diverse site types. 7.2.2 Mapping northern vegetation structure In Chapters 4 and 5, I generated continuous maps of vegetation structure across northern Canada at 30 m spatial resolution. This research builds on previous work by Matasci et al. (2018a) who used airborne lidar data and Landsat time-series to map forest structural attributes using k-nearest neighbour imputation across the forested ecozones of Canada. However, airborne lidar data is not frequently acquired close to the treeline and these 134 models were trained on taller canopies with a wider range of heights (2-46 m). This may lead to regional biases in areas with fewer or no training samples, as the imputation approach can only predict exact values from its training data. My approach uses spaceborne lidar data with coverage across the entire forest-tundra ecotone (Figure 4-2), and modeled height distributions align with expectations for forests in this region (Figure 4-5). These methods can fill knowledge gaps in assessing carbon storage across Canada’s northern boreal forests. Canopy height is an important forest structural attribute used to assess aboveground biomass and forest carbon stocks (Kurz et al., 2013; Lefsky, 2010; Lefsky et al., 2002). In Canada, stand level forest inventories are routinely collected across temperate and southern boreal forests, but northern boreal forests have more limited and spatially focused monitoring programs (Kurz et al., 2013). Satellite remote sensing provides a means to model canopy height and biomass in a standardized manner for regions that would be difficult to access otherwise. Monitoring boreal carbon storage is especially important given significant carbon losses expected to occur over the next century from thawing permafrost (Abbott et al., 2016) and wildfires (Turetsky et al., 2011). Increasing vegetation productivity at the treeline may be partially offset these losses and should be accounted for in national estimates of the carbon budget. 7.2.3 Understanding spectral greening trends at the treeline This dissertation also helps contextualize decades of work mapping spectral greenness trends across the circumpolar region (Berner et al., 2020; Olthof et al., 2009; Ranson et al., 2011; Walker et al., 2003; Wang & Friedl, 2019). It is sometimes assumed that 135 increasing greenness measured using NDVI or EVI is indicative of advancing boreal forests and treeline expansion (Berner & Goetz, 2021). In Chapter 3, I show direct evidence that Landsat pixels with more tree stems and greater canopy height are actually associated with lower spectral greenness at the treeline (Figure 3-7). This pattern is also observed using ICESat-2 measurements in Chapter 5, where taller-stature vegetation is correlated with lower greenness and brightness values across the ecotone (Figure 5-2). These observations are supported by other work demonstrating poor correlation between treeline forest structure and standard greening metrics from multispectral satellites (Loranty et al., 2018; Walker et al., 2021b). Overemphasizing the role of single indices on complex land cover changes occurring in the forest-tundra may lead to inflated or unrealistic estimates of forest advance from remote sensing (Timoney & Mamet, 2020). My research provides alternative methods to quantify structural vegetation change using additional spectral indices and spaceborne lidar measurements 7.2.4 Multi-scale validation of Landsat change detection In Chapter 6, I use spatially detailed historic air photographs and contemporary high-resolution satellite imagery to validate models of structural vegetation change developed in Chapter 5. This work is novel because many remote sensing studies use withheld samples of training data to validate model predictions that are not completely independent from training data (Coops et al., 2021). Using similar training and validation data can inflate the reported accuracy of model compared to unseen validation data. However, these techniques are often used due to practical considerations in obtaining completely independent validation. Chapter 6 provides a direct link between two frequently used change detection approaches – high-spatial resolution image interpretation and Landsat 136 time-series classification. I show that correlation between Landsat time-series and long-term photo interpretation depends on the type of vegetation change, as well as the time-period analysed. My research emphasizes that multiple types of change detection across spatial scales (i.e. satellite remote sensing, drone surveys, field-observations) are needed to capture complex vegetation dynamics in the forest-tundra (Beamish et al., 2020). 7.3 Limitations and future directions 7.3.1 Use of space-for-time substitution in ecology Recently, Evans et al. (2025) critiqued the assumptions that underlie space-for-time substitution methods that are frequently used in climate change ecology. Specifically, 1) that climate is the most important determinant of an ecological process, and 2) that there is no lag between climate change and the variable of interest. In Chapter 3, I used a space-for-time substitution to predict how soil carbon storage could change with advancing tree and shrublines. This approach assumed that carbon storage at tundra sites will become more similar to present day forested sites following vegetation expansion. However, equilibrium between vegetation composition and soil carbon storage is not guaranteed. It is possible that there are lagged and non-linear effects between these variables (such as an initial “pulse” of soil carbon loss that is recovered over longer periods) that I was unable to capture. To improve reliability of ecological forecasts Evans et al., (2025) recommend establishing more long-term warming experiments, common garden trials and multi-date observations to better estimate lag times. These considerations are also broadly applicable to many remote sensing models that predict future forest distribution based on present day relationships between climate, topography and existing forested areas (Montesano et al., 2024). In Chapters 4 and 5, 137 my models of structural vegetation change do not use gridded climate data as model inputs. This accounts for disequilibrium effects between forest distribution and climate that may overestimate northward forest advance (Mengtian et al., 2017), reflecting potential restrictions on forest expansion related to soil conditions (Timoney, 2023), and low reproductive capacity of northern conifers (Walker et al., 2012; Brown et al., 2019). Future remote sensing studies should also use caution when using gridded climate projections to predict the future distribution of forests. 7.3.2 Improving reference height datasets In Chapters 4-6 I use ICESat-2 ATL08 segments as training data for Random Forests models. However, single-photon spaceborne lidar measurements are noisy relative to airborne lidar scanning data, with higher levels of uncertainty for complex and low-stature vegetation (Queinnec et al., 2021). In this dissertation I used the “out of the box” ATL08 Land and Vegetation Height product, which relies on the DRAGANN algorithm to classify photon returns as signal or noise and aggregates photons into 20 m segments (Neuenschwander & Pitts, 2019). However, there is still a significant amount of noise in this data product that leads to overall overestimation of low canopy heights that is propagated in the Random Forests models. Recent studies using raw geolocated photon returns from ICESat-2 (ATL03 product) have improved the signal finding algorithm to reduce uncertainty at regional scales (Queinnec et al., 2021; Zhang & Liu, 2023). Due to the size of my study area, it was beyond the scope of my dissertation to develop or apply these computationally intensive methods to my workflow. However, future work using ICESat-2 over regional study areas should consider additional processing and quality control steps to reduce noise in canopy height measurements before modeling. 138 7.4 Closing statements Ongoing climate change poses immediate and concerning challenges for northern communities across Canada. A wide range of environmental impacts are already underway, disrupting local livelihoods and economies (Cameron, 2012; Meredith et al., 2019). At the national scale, changes in high-latitude permafrost, vegetation and hydrology also have significant implications for uptake and sequestration. The primary goal of this dissertation was to develop remote sensing tools to improve our understanding of ongoing changes in northern vegetation structure and carbon storage. I accomplish this using data across a range of spatial and temporal scales. In Chapter 3, I combined very high spatial-resolution drone imagery with measurements of soil carbon to assess variation in carbon storage across northern treelines. In Chapters 4 and 5, I used satellite images and spaceborne lidar to model change in vegetation height across the entire Canadian forest-tundra ecotone over the past 40 years. Finally, in Chapter 6 I used a rich dataset of historic air photographs and contemporary satellite imagery to map forest and shrub expansion at four distinct treeline sites. My dissertation achieves several research objectives: 1) improving methods to monitor northern vegetation change at the national scale using remotely sensed data, 2) understanding the role of climate and wildfires in driving vegetation change across the ecotone, 3) performing robust, multi-scale validation of remotely sensed models. 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Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Coops, Nicholas C.
Mapping vegetation structure and carbon dynamics across the Canadian forest-tundra ecotone using multi-scale remote sensing
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