Forestry, Faculty of
DSpace
UBCV
Nuijten, Rik Johannes Gerardus
Doctor of Philosophy - PhD
University of British Columbia
To maintain a (social) license to operate, which depends on support from local communities, society, and regulatory bodies, managers in natural resource extraction are increasingly engaged in ecological restoration and reclamation, as well as continuous assessments of ecosystem recovery. This necessitates information on ecosystem status throughout the planning, implementation, and evaluation phases of restoration. Over the last decade, digital aerial photogrammetry (DAP) using drone-based optical imagery has emerged as a cost-effective technique for assessing various ecosystem attributes, such as plant functional type composition. However, its effectiveness for early successional forest ecosystems remains under-explored. The primary goal of this dissertation is to evaluate the role of drone-based DAP in providing spatially explicit, valuable, and cost-effective insights into ecosystem recovery by characterizing a wide range of ecosystem attributes. I acquired three-dimensional (3D) point clouds and multispectral ortho imagery for pipeline right-of-ways (ROW) and wildfire-disturbed sites in Northwest Alberta, Canada. The strengths and limitations in characterizing vegetation structure were examined by comparing modelled canopy heights with field measurements, and analyzing maps derived from point clouds using unsupervised learning techniques. Further, I assessed the capacity to map dominant plant species and functional types, through logistic regression modelling, utilizing metrics that describe spectral reflectance, vegetation structure, and habitat characteristics. A framework was introduced to incorporate reference sites, facilitate species composition comparisons between sites from drone-derived maps, and support ecological interpretation of spatial patterns. I also examined the uncertainty and complexity of information from drone approaches (including alternative technologies to DAP) based on a systematic literature review and sentiment analysis. Lastly, I determined the spatial coverage at which drone approaches can be more cost-effective than field-based approaches, incorporating data acquisition, processing, and analysis costs. This dissertation aims to encourage broader adoption of drone-based remote sensing in ecosystem recovery assessments and to foster interdisciplinary collaboration, particularly in areas where costs, information uncertainty, and complexities can be effectively managed and reduced.
Characterizing Ecosystem Attributes Relevant for Restoration Assessment in Early Successional Forests using Drone-based Remote Sensing Approaches by Rik Johannes Gerardus Nuijten MSc, Utrecht University, 2019 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) June 2024 © Rik Johannes Gerardus Nuijten, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Characterizing Ecosystem Attributes Relevant for Restoration Assessment in Early Successional Forests using Drone-based Remote Sensing Approaches submitted by Rik Johannes Gerardus Nuijten 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 Cindy E. Prescott, Professor Emeritus, Forest and Conservation Sciences, The University of British Columbia Supervisory Committee Member Hisham Zerriffi, Associate Professor, Forest Resources Management, The University of British Columbia Supervisory Committee Member Jeanine M. Rhemtulla, Associate Professor, Forest and Conservation Sciences, The University of British Columbia University Examiner Adam Wei, Professor, Earth, Environmental and Geographic Sciences, The University of British Columbia University Examiner Karen Anderson, Professor, Environment and Sustainability Institute, The University of Exeter External Examiner Additional Supervisory Committee Members: Suzanne W. Simard, Professor, Forest and Conservation Sciences, The University of British Columbia Supervisory Committee Member iii Abstract To maintain a (social) license to operate, which depends on support from local communities, society, and regulatory bodies, managers in natural resource extraction are increasingly engaged in ecological restoration and reclamation, as well as continuous assessments of ecosystem recovery. This necessitates information on ecosystem status throughout the planning, implementation, and evaluation phases of restoration. Over the last decade, digital aerial photogrammetry (DAP) using drone-based optical imagery has emerged as a cost-effective technique for assessing various ecosystem attributes, such as plant functional type composition. However, its effectiveness for early successional forest ecosystems remains under-explored. The primary goal of this dissertation is to evaluate the role of drone-based DAP in providing spatially explicit, valuable, and cost-effective insights into ecosystem recovery by characterizing a wide range of ecosystem attributes. I acquired three-dimensional (3D) point clouds and multispectral ortho imagery for pipeline right-of-ways (ROW) and wildfire-disturbed sites in Northwest Alberta, Canada. The strengths and limitations in characterizing vegetation structure were examined by comparing modelled canopy heights with field measurements, and analyzing maps derived from point clouds using unsupervised learning techniques. Further, I assessed the capacity to map dominant plant species and functional types, through logistic regression modelling, utilizing metrics that describe spectral reflectance, vegetation structure, and habitat characteristics. A framework was introduced to incorporate reference sites, facilitate species composition comparisons between sites from drone-derived maps, and support ecological interpretation of iv spatial patterns. I also examined the uncertainty and complexity of information from drone approaches (including alternative technologies to DAP) based on a systematic literature review and sentiment analysis. Lastly, I determined the spatial coverage at which drone approaches can be more cost-effective than field-based approaches, incorporating data acquisition, processing, and analysis costs. This dissertation aims to encourage broader adoption of drone-based remote sensing in ecosystem recovery assessments and to foster interdisciplinary collaboration, particularly in areas where costs, information uncertainty, and complexities can be effectively managed and reduced. v Lay Summary Managers engaged in natural resource extraction are increasingly involved in ecological restoration and assessments. This requires detailed information about the ecosystem's state and reference conditions, through attributes like vegetation structure and species composition. Conventionally, information is gathered through labor-intensive botanical surveys, which struggle to adequately represent spatial variability. Over the last decade, drone-based remote sensing, particularly digital aerial photogrammetry (DAP), has emerged as a cost-efficient solution. It provides spatially explicit data at a resolution compatible with the scale of early successional plants; however, its effectiveness in such a context remains underexplored. The dissertation explores the capabilities and limitations of DAP in assessing ecosystem recovery on pipeline right-of-ways and wildfire-disturbed sites in Northwest Alberta. It presents methods for evaluating vegetation structure, plant type composition, spatial patterning, and cross-site comparisons. Furthermore, it discusses the potential of other drone-based remote sensing techniques to improve the reliability, complexity, and cost-efficiency of ecosystem recovery assessments. vi Preface The research objectives and questions presented in the dissertation were developed through collaboration with my supervisory committee. The work presented in Chapters 3 and 4 has been published in peer-reviewed journals, while Chapters 5 and 6 have recently been submitted for review. In these publications, I performed the conceptualization of the research, development of methods, formal analysis, visualization, interpretation, and manuscript preparation for publication. The co-authors provided critical feedback and recommendations through discussions, as well as comments and revisions on the manuscripts before submission. Chapter 3: Nuijten, R. J. G., Coops, N. C., Watson, C., & Theberge, D. (2021). Monitoring the structure of regenerating vegetation using drone-based digital aerial photogrammetry. Remote Sensing, 13:1942â1965. https://doi.org/10.3390/rs13101942 Chapter 4: Nuijten, R. J. G., Coops, N. C., Theberge, D., & Prescott, C. E. (2024). Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring. Science of Remote Sensing, 9:100114. https://doi.org/10.1016/j.srs.2023.100114 Chapter 5: Nuijten, R. J. G., Coops, N. C., Prescott, C. E. & Theberge, D. (2024) Informing reclamation success in forested landscapes using drone-based plant community assessments: Enhancing vegetation assessments and reference site selection. In review. vii Chapter 6: Nuijten, R.J.G., Coops, N.C., Zerriffi, H. & Theberge, D. (2024) Advances in drone remote sensing for restoration assessments: Information uncertainty, complexity and cost-effectiveness. In review. In addition, I contributed to the following report that is relevant to the scope of this dissertation: Chadwick, A. J.; Goodbody, T. R. H.; Bater, C. W.; Martens, L. A.; Nuijten, R. J. G.; Smith-Tripp, S.; Grubinger, S.; Irwin, L.; Arkin, J.; Hervieux, A.; and Coops, N.C. (2022) Best Practice Guide to Acquisition of 3D Imagery from RPAS. Department of Forest Resource Management, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia. https://irss-ubc.github.io/GOA_BPG_PUB viii Table of Contents Abstract ....................................................................................................................................................... iii Lay Summary .............................................................................................................................................. v Preface ......................................................................................................................................................... vi Table of Contents ..................................................................................................................................... viii List of Tables .............................................................................................................................................. xi List of Figures ........................................................................................................................................... xiv List of Abbreviations ................................................................................................................................ xx Acknowledgements ................................................................................................................................. xxii Dedication ............................................................................................................................................... xxiv Chapter 1: Introduction and Background ................................................................................................ 1 1.1 The Need for Restorative Activities in the Context of Natural Resources Extraction ............ 1 1.2 Information Needs for Forest Ecosystem Recovery Assessments .......................................... 3 1.3 Remote Sensing in Recovering Forest Ecosystems ................................................................ 7 1.4 Research Objectives and Questions ...................................................................................... 16 1.5 Dissertation Overview ........................................................................................................... 17 Chapter 2: Study Area and Data ............................................................................................................. 19 2.1 Background Study Region .................................................................................................... 19 2.2 Study sites ............................................................................................................................. 24 2.3 Data Collection ...................................................................................................................... 33 2.4 Data Processing ..................................................................................................................... 46 Chapter 3: Monitoring the Structure of Regenerating Vegetation using Drone-Based Digital Aerial Photogrammetry ....................................................................................................................................... 49 3.1 Background and Motivation .................................................................................................. 49 3.2 Materials and Methods .......................................................................................................... 51 ix 3.3 Results ................................................................................................................................... 55 3.4 Discussion ............................................................................................................................. 71 3.5 Conclusions ........................................................................................................................... 76 Chapter 4: Estimation of Fine-Scale Vegetation Distribution Information from Drone-Generated Imagery and Structure to Aid Restoration Monitoring ........................................................................ 77 4.1 Background and Motivation .................................................................................................. 77 4.2 Materials and Methods .......................................................................................................... 79 4.3 Results ................................................................................................................................... 89 4.4 Discussion ............................................................................................................................. 98 4.5 Conclusion........................................................................................................................... 105 Chapter 5: Informing Reclamation Success in Forested Landscapes using Drone-based Plant Community Assessments: Enhancing Vegetation Assessments and Reference Site Selection ......... 107 5.1 Background and Motivation ................................................................................................ 107 5.2 Materials and Methods ........................................................................................................ 109 5.3 Results ................................................................................................................................. 114 5.4 Discussion ........................................................................................................................... 123 5.5 Conclusions ......................................................................................................................... 127 Chapter 6: Advances in Drone-based Remote Sensing for Restoration Assessments: Information Uncertainty, Complexity, and Cost-Effectiveness ................................................................................ 129 6.1 Background and Motivation ................................................................................................ 129 6.2 Assessing Early Ecosystem Recovery using Drones: a Literature Review ......................... 130 6.3 Information Uncertainty and Complexity ........................................................................... 138 6.4 Cost-Effectiveness Examined Through a Case Study: Reclamation in Alberta .................. 145 6.5 Opportunities and Operational Considerations for Use of Drone Remote Sensing for Ecosystem Recovery Assessments ................................................................................................... 156 x 6.6 Conclusions ......................................................................................................................... 160 Chapter 7: Conclusions .......................................................................................................................... 162 7.1 Dissertation Objectives ....................................................................................................... 162 7.2 Research Innovations and Significance ............................................................................... 167 7.3 Research Limitations and Future Directions ....................................................................... 169 7.4 Closing Statement ............................................................................................................... 173 Bibliography ............................................................................................................................................ 175 xi List of Tables Table 2.1 Summary of the study sites. The ecosite types were derived from the Derived Ecosite Phase (DEP; Alberta Agriculture and Forestry 2020). The two wildfire sites within the Chinchaga region were treated as a unified site due to their similar age and site conditions. ....................................... 26 Table 2.2 A summary of data used for the multi criteria analysis for site selection. .................................. 31 Table 2.3 Relative environmental conditions in two regeneration niches. The table was adapted from Margolis and Brand (1990) and complementary resources including Barker (2018)1, Burkle et al. (2015)2, Caners and Lieffers (2014)3, Flemming et al. (2023)4, Gärtner et al. (2011), Rowland et al. (2009)6, Stern et al. (2018)7, Taylor et al. (2013)8, Trepanier et al., (2021b)9, and Macdonald et al. (2015)10. ............................................................................................................................................ 33 Table 2.4 Characteristics of botanical survey, area coverage by drone-derived imagery, and measured (native) species richness (i.e., count), including richness averaged across field transects and total richness by site. ................................................................................................................................. 35 Table 2.5 Plant species observed on sampled transects, including representative plant functional type and study sites of occurrence, which include Chinchaga A (Ch_A), Chinchaga B (Ch_B), and Little Smoky (LS). ...................................................................................................................................... 39 Table 2.6 Summary of flight parameters, conditions, and data specifications for collection of RGB imagery with various sensors. ........................................................................................................... 43 Table 2.7 Summary of flight parameters, conditions, and data specifications for the collection of multispectral imagery with Micasense Rededge-MX. ...................................................................... 44 Table 2.8 Required personnel for sampling a site pair of 40 ha total based on time measures .................. 45 Table 2.9 Drone configurations used in the cost-effectiveness analyses. Data acquisition times of scenario 1 were measured in the field, whereas acquisition times for scenario 2 were estimated. ................. 46 Table 3.1 DAP metrics, descriptions, and categories. Metrics selected for cluster analysis are indicated with an asterisk (*)............................................................................................................................ 54 xii Table 3.2 Pearson correlation coefficients (rp) across the five selected structure metrics, based on 1 m2 hexagons. All p < 0.05. Metrics and associated abbreviations and descriptions are listed in Table 3.1. .................................................................................................................................................... 57 Table 3.3 Final structure classes with corresponding descriptions of vegetation structure and observed life forms, based on the structure metrics, point-cloud visualizations, and field observations. .............. 63 Table 4.1 A list of assessed plant functional types and key plant groups at various taxonomic levels, which includes the number of present samples after data cleaning. The last column lists the structure classes, corresponding to descriptions found in Chapter 3, indicative of the plant functional types and key groups. ...................................................................................................... 82 Table 4.2 The predictor variables used in the species distribution models (SDMs). For variables indicated with 1 both mean and standard deviation were calculated. Variables indicated with 2 are based on the outputs of unsupervised clustering analysis. ............................................................................... 86 Table 4.3 Mean and standard deviations of performance metrics derived from cross validation, including precision, recall (i.e., true positive rate), specificity (i.e., true negative rate) and f-score. ............... 90 Table 5.1 Summary of plant diversity and composition at three reclamation (pipeline rights-of-way) and reference (wildfire-disturbed) sites, based on field-based samples and drone-based maps. The Simpsonâs evenness scores range from 0â1, with 1 indicating highest evenness. The ANOSIM R-statistic ranges from -1â1, with 0 indicating perfect site similarity. ............................................... 115 Table 6.1 A list of reviewed research articles and identified ecosystem attributes: (1) fractional vegetation cover, (2) plant functional type composition, (3) indicator species, (4) invasive species, (5) productivity (health/biomass), (6) spatial mosaic, (7) species composition, (8) species richness, (9) micro topography, and (10) vegetation structure. ........................................................................... 132 Table 6.2 Characteristics of the four scenarios assessed in the case study, including the number of field plots per data acquisition. Each data acquisition covers a pipeline right-of-way (ROW) and wildfire-disturbed site, each encompassing 20 hectares. ................................................................ 147 xiii Table 6.3 Time estimates for processing and analysis steps for a site pair of 40 ha including 12 transects. 1Time goes up proportionally to the number of transects (e.g., to 20 or 24 transects). .................. 149 Table 6.4 A breakdown of fixed and variable costs in $CAD associated with four cost factors by scenario, to be used with the equation above. Each data acquisition (a) covers a pipeline right-of-way (ROW) and wildfire-disturbed site, each encompassing 20 hectares, with a variable number of transects (t). ........................................................................................................................................................ 150 Table 6.5 Rates for labour, equipment and accommodation in $CAD. .................................................... 150 xiv List of Figures Figure 1.1 Alternatives within the Restorative Continuum compared with natural ecosystem development (assuming linear development) including the novel ecosystem concept, reclamation, as well as partial and full recovery following restoration. This illustration was adopted from Bradshaw (1984). ................................................................................................................................................. 3 Figure 1.2 The ecosystem recovery wheel, including key ecosystem attributes, developed by the Society of Ecological Restoration (SER). This image was adapted from Gann et al. (2019). ........................ 5 Figure 1.3 An example of an ecosystem recovery ranking using the five-star system. This image was adapted from Gann et al. (2019). ........................................................................................................ 6 Figure 1.4 Examples of common remotely piloted aerial systems (RPAS) including (a) multi-rotor platforms with integrated red-green-blue (RGB) camera (a) and interchangeable payload (b), a fixed-wing platform (c), and a system for high-precision geo-spatial positioning (d). ...................... 8 Figure 1.5 Spectral signatures of various wetland species found in Spitsbergen (A) and multispectral reflectance (from RapidEye satellite sensor) of various wetland types found in Newfoundland and Labrador, Canada (B). The spectral bands from the RapidEye satellite are highlighted in A. Images were adapted from Amani et al. (2018) and Zagajewski et al. (2017). ............................................. 11 Figure 1.6 Illustrations of a DAP point cloud for a section of a wildfire-disturbed site demonstrating various post-processing steps, including: (B) Noise removal and reducing (i.e., subsampling) points; (C) Ground detection; and (D) Height normalization. .......................................................... 14 Figure 1.7 Illustrations of digital aerial photogrammetry (DAP) including: (A) Tie-point generation and image matching; (B) Bundle block adjustment (including triangulation); (C) geometric correction and mosaicking of the original photographs using a digital surface model. ..................................... 15 Figure 1.8 Illustration capturing dissertationâs research questions. ............................................................ 17 Figure 2.1 Locations of the right-of-way (ROW) and wildfire-disturbed analogous sites in northwest Alberta, Canada, including current woodland caribou (Rangifer tarandus caribou) ranges. ........... 22 xv Figure 2.2 Overview of the three right-of-way sites with evidence of disturbances and restoration activities. Subplots a and c include fresh ATV tracks. Subplot f shows old truck tracks. Subplot b includes timber rollback and piling for access/line-of-sight control. Subplots d and e include plantings of conifer seedlings. .......................................................................................................... 28 Figure 2.3 Overview botanical survey plot. ................................................................................................ 35 Figure 2.4 An illustration of vegetation height recording. This image was from Chen et al. (2017). ........ 37 Figure 2.5 Supportive illustrations for visual estimation of foliage cover developed by Terry and Chilingar (1955). .............................................................................................................................. 38 Figure 3.1 Methodological flow diagram of data pre-processing, initial analysis (i.e., DAP height assessment), and cluster analysis. ..................................................................................................... 52 Figure 3.2 Comparison of maximum digital aerial photogrammetry (DAP) and maximum in situ height samples by life form: (A) conifers, (B) deciduous (incl. shrubs), (C) graminoids, and (D) perennials. Graphs show the number of samples, regression lines (with 95% confidence interval), Spearman correlation coefficients (rs), mean height offsets (Å), root mean square errors (RMSE), and normalized RMSE. All p-values < 0.05. .................................................................................... 56 Figure 3.3 Line graph of the gap statistic by number of clusters (k) based on agglomerative hierarchical clustering and 325 pre-clusters. The optimal (k) is found at nine. .................................................... 58 Figure 3.4 Dendrogram and associated heatmap showing structure metrics scaled from zero to one. They are based on 325 k-means pre-clusters and agglomerative hierarchical clustering. The nine final clusters are color-coded. ................................................................................................................... 58 Figure 3.5 Distance bi-plot of the first two principal components (PCâs) based on hexagons and associated structure metrics. The plot includes percentages of explained variance by PC as well as the strength and direction of each structure metric. The nine final clusters are color-coded. .......... 60 xvi Figure 3.6 Boxplots showing the five structure metrics used during two-step clustering analysis by cluster. The letters indicate whether clusters are significantly different according to Dunnâs post hoc test. All p-values < 0.01. ............................................................................................................ 62 Figure 3.7 Point-cloud cross-sections by cluster and class, including orthomosaic and classified hexagons. The hexagons are colored according to the final classes described in Table 3.3. The cross-sections were 0.5 x 5 m2. ................................................................................................................................ 65 Figure 3.8 Barplots of (a) land cover percentages and (b) mean patch sizes (m2) by structure class and site. Study site characteristics for (A) Chinchaga A, (B) Chinchaga B, and (C) Litle Smoky are summarized in Table 2.1 and class descriptions in Table 3.3. .......................................................... 66 Figure 3.9 Land cover maps of the three study sites, including (A) Chinchaga A, (B) Chinchaga B, and (C) Little Smoky, showing the final structure classes. Class descriptions are summarized in Table 3.3. .................................................................................................................................................... 68 Figure 3.10 Entropy-based local spatial association (ELSA) scores by structure class and site, including (A) Chinchaga A, (B) Chinchaga B, and (C) Litle Smoky. ELSA scores are based on individual 1 m2 hexagons and a search radius of 3.3 m. The letters indicate whether clusters are significantly different according to Dunnâs post hoc test. All p-values < 0.01. .................................................... 69 Figure 3.11 Maps of the three study sites, including (A) Chinchaga A, (B) Chinchaga B, and (C) Litle Smoky, showing entropy-based local spatial association (ELSA) scores. The scores are based on all classified 1 m2 hexagons and a local search radius of 3.3 m. Zero indicates a random distribution was not present and one indicates a random distribution. ................................................................ 71 Figure 4.1 Methodological flow diagram of data collection and pre-processing, the development of various binary presence-absence models, as well as mapping of plant composition. ...................... 80 Figure 4.2 Cover percentages by site of selected key plant groups, at various taxonomic levels (left), and plant functional types (right) based on the field sample data set. ..................................................... 83 xvii Figure 4.3 Relative co-occurrence matrices for selected key plant groups, at various taxonomic levels (left), and plant functional types (right) based on the field sample data set. .................................... 83 Figure 4.4 Variables ranked by importance for presence-absence models of the key plant groups (left), and plant functional types (right). ..................................................................................................... 91 Figure 4.5 SHAP variable importance plots for all logistic regression-based presence-absence models. .. 92 Figure 4.6 SHAP dependence plots for the three most important variables by model. The x-axis is the value of that variable, whereas the y-axis represents the impact on the modelâs output. The colors correspond to the values of a second variable that may have an interaction with the main variable. .......................................................................................................................................................... 95 Figure 4.7 Logistic regression-based composition maps of key plant groups at right-of-way (ROW) Chinchaga A (i), ROW Chinchaga B (ii), ROW Little Smoky (iii), wildfire Chinchaga A (iv), wildfire Chinchaga B (v) and wildfire Little Smoky (vi). ................................................................ 96 Figure 4.8 Cover percentages for key plant groups (left) and plant functional types (right) at each site, retrieved from the final mapping product. ........................................................................................ 97 Figure 5.1 Maps displaying drone-based imagery and analyzed maps for a section of a pipeline right-of-way (ROW), including: (A) red-green-blue (RGB) ortho imagery, (B) false color composite from multispectral ortho imagery, (C) maps of plant functional types, and (D) maps of vegetation structure. ......................................................................................................................................... 111 Figure 5.2 Comparisons of plant functional type cover across site pairs derived from drone-based maps and field-based samples, including drone-based Similarity Percentage (SIMPER) values within round brackets. *Wildfire Chinchaga A and B are displayed separately but were treated as a unified reference in further analyses. .......................................................................................................... 116 Figure 5.3 Comparisons of vegetation structure type cover across site pairs derived from drone-based maps, including SIMPER values within round brackets. *Wildfire Chinchaga A and B are displayed separately but were treated as a unified reference in further analyses. ........................................... 118 xviii Figure 5.4 Five fragmentation statistics (FRAGSTATS) detailing spatial patterns and patch dimensions across mapped plant functional types. All values were normalized. .............................................. 120 Figure 5.5 Five fragmentation statistics (FRAGSTATS) detailing spatial patterns and patch dimensions across mapped structure types. All values were normalized. ......................................................... 121 Figure 5.6 Compositional similarity of mapped plant functional types within the Chinchaga wildfire references, following spatial partitioning as in Section 5.2.3.1, visualized with Non-metric Multidimensional Scaling (NMDS). In addition, their corresponding 90% confidence interval is shown. The right panel compares plant type cover within and outside the confidence interval using Welchâs t-tests, where * indicates a p-value < 0.05. ....................................................................... 122 Figure 6.1 General trends in investigated drone- and field-based studies, including (a) the total of publications per year, (b) proportion of publications by continent, (c) proportion of publications by ecosystem attribute, and (d) proportion of papers by remote sensing sensor. ................................ 135 Figure 6.2 Additional trends in investigated 53 drone- and 28 field-based publications. ......................... 137 Figure 6.3 Sentiment index (SI) for various ecosystem attributes related to information uncertainty and complexity for drone- and field-based studies. The number of the used text annotations and publications are included in parenthesis. ........................................................................................ 140 Figure 6.4 Cost per hectare, broken down by cost factor, across four different acquisition scenarios. These scenarios include (1) a standard drone, (2) an advanced drone, (3) a standard field-based, and (4) an intensive field-based scenario (4). .................................................................................................. 152 Figure 6.5 Cost ranges per hectare with uncertainty ranges and break-even points across four different acquisition scenarios. These scenarios include (1) a standard drone, (2) an advanced drone, (3) a standard field-based, and (4) an intensive field-based scenario. .................................................... 153 Figure 6.6 The relative change of break-even points following cost changes, where cost factors were iteratively changed by ±10%. Note that a break-even point is the number of data acquisitions where a drone scenario becomes more cost-effective than a field-based scenario. The contrasted scenarios xix include: a standard drone (1), an advanced drone (2), a standard field-based (3), and an intensive field-based scenario (4). .................................................................................................................. 155 xx List of Abbreviations ANOSIM Non-parametric Analysis of Similarities ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer CHM Canopy Height Model COV Coefficient of Variation DAP Digital Aerial Photogrammetry DTM Digital Terrain Model ELSA Entropy-based Local Spatial Association ExGI Excessive Greenness Index FN False Negative FP False Positive GCP Ground Control Point GNSS Global Navigation Satellite System GPS Global Positioning System GRI Green Ratio Index GSD Ground Sampling Distance IMU Inertial Measurement Unit k Number of clusters (groups) LiDAR Light Detection and Ranging NDRE Normalized Difference Red Edge Index NDVI Normalized Difference Vegetation Index NIR Near Infrared NIRv Near-Infrared Reflectance of Terrestrial Vegetation (index) NMDS Nonmetric Multidimensional Scaling PCA Principal Component Analysis xxi PSRI Plant Senescence Reflectance Index %P Percentage of Points in Point Cloud r Pearson Correlation Coefficient R2 Coefficient of Determination RE Red Edge RGB Red-green-blue RI Rumple Index RMSE Root Mean Square Error ROW Right-of-Way RPAS Remotely Piloted Aerial Vehicles rs Spearman Correlation Coefficient RTK Real-time Kinematic Positioning SHAP SHaply Additive explanation SI Sentiment Index SIMPER Similarity Percentage Sp Single species Spp Multiple species TP True Positive UAS Uncrewed/Unmanned Aerial Systems xxii Acknowledgements Foremost, I would like to thank Nicholas Coops for his continuous support and guidance throughout the completion of this research. I appreciate the productive environment that he cultivates, of which I was fortunate to be a part of. Particularly noteworthy is his commitment to fostering connections among students, other academics, and industry partners, not only within the faculty but also through seminars and conferences. I extend my gratitude to the members of my supervisory committeeâCindy Prescott, Susanne Simard, and Hisham Zerriffiâfor providing guidance and introducing me to the fields of ecological restoration, forest ecology, and decision analytics. Their mentorship was instrumental in conceptualizing and executing the research in this dissertation. I thank Catherine Watson and Dustin Theberge, former and present employees of TC Energy, for shaping the project and providing needed context. Acknowledgement is also extended to TC Energy and the Natural Sciences and Engineering Research Council of Canada for funding this research. Thank you to all members of the Integrated Remote Sensing Studio for their friendship and support. They went above and beyond to introduce me to Vancouver and make me feel at home, through the many fun activities we did together. I will cherish the memories that we made. They have also motivated me to push further, shared ideas, and provided valuable knowledge. At times, mere exposure was enough to spark inspiration. In particular, I want to thank Andrew Chadwick, Cam McClelland, Chris Mulverhill, Daisy Zhao, Gonzalo Gavilan, Liam Irwin, and Samual xxiii Grubinger as they travelled with me to Alberta (in 2019 and 2021) and bend over backwards to make this project a success. I would like to thank friends, old and newâthe ones that have supported me from abroad, defying time and distance, and the ones close by. They were my support during the pandemic, called me up, and took me into the mountains and forests. Thank you to my parents, Erna and Dries, my sister Maud, and my partner and the most recent addition to this journey, Lourdes. Finally, I would like to acknowledge that work was carried out across the territories of the Beaver First Nation, Dene Thaâ First Nation, and Métis people. I would also like to acknowledge that the UBC Vancouver campus, where I conducted this research, is located on the traditional, ancestral, and unceded territory of the xÊ·mÉÎ¸kÊ·ÉyÌÉm (Musqueam) people. xxiv Dedication To my Family and Friends1 Chapter 1: Introduction and Background 1.1 The Need for Restorative Activities in the Context of Natural Resources Extraction Canada is home to over 347 million hectares of forested land, of which almost 60% is primary forest (Food and Agriculture Organization of the United Nations 2020). These forested ecosystems provide many essential good and services to the population regionally, nationally and globally, including the provision of natural resources, protection against erosion and natural disasters, as well as opportunities for recreation and cultural inspiration (Ciccarese et al. 2012). Global demand for natural resources in forested areas, including timber, natural gas and minerals, is expected to grow within the next two decades (British Petroleum 2020), which has potential implications on the provision of these services as well as on biodiversity through changes in landscape fragmentation, species invasion, soil destabilization, as well as alteration of water, nutrient and carbon cycles (Kayes et al. 2020). As a result, minimizing the extraction impacts and biodiversity conservation are needed. To maintain a (social) license to operate, which depends on the support of local communities, society and regulatory bodies, managers involved in natural resource extraction are increasingly involved in implementing measures to address environmental concerns and continuous monitoring (Young et al. 2022). A relatively new umbrella term, the Restorative Continuum (Figure 1.1), contextualises the range of restorative activities which aim to support recovery of ecosystem integrity, referring to the capacity of an ecosystem to support and sustain biodiversity (i.e., species composition and structure) and characteristic ecological functioning (Gann et al. 2019). 2 Within this framework, the most ambitious objective is ecological restoration, typically aiming for the full recovery of an ecosystem, as depicted in Figure 1.1. Ecological restoration involves the restoration of ecological functioning and native biodiversity after a disturbance, incorporating a reference ecosystem (Gann et al. 2019). However, partial ecosystem recovery of ecosystem attributes may be sufficient, depending on the prioritization of specific ecosystem attributes (i.e., values) and constraints related to resources, technicalities, and environment (Gann et al. 2019). Habitat restoration, for instance, aims to restore specific ecosystem attributes that create favorable habitat for valued flora or fauna. Reclamation (sometimes also referred to as remediation or rehabilitation) involves ecological repair activities that aim to partly restore ecosystem function and is more common in industrial settings (Powter et al. 2012; Dance 2015; TC Energy 2020a). The goal of reclamation, as defined in the United States, is âthat the site is hospitable to organisms that were originally present or others that approximate the original inhabitantsâ (Young et al. 2022). In Canada, the definition is similar but may put greater emphasis on the compatibility of reclaimed lands with the surrounding environment (AEP 2010). Traditionally, historical references were utilized to define the original ecosystem condition (defined as the initial ecosystem structure and function of a system; Bradshaw, 1984). However, there is a growing trend toward selecting and utilizing proximate and appropriate reference ecosystems, encompassing various successional stages, and engineering novel ecosystems based on desired ecosystem services, such as carbon sequestration, recreation, and water supply (Hobbs et al. 2006; Hiers et al. 2012; Martin 2017). Note that a novel ecosystem includes new species combinations and sometimes changed ecosystem functioning (Hobbs et al. 2006). Reasons to choose a novel ecosystem over partial or full restoration relates to the unfeasibility of reversing 3 disturbance, considering the economic costs of restoration as well as socioeconomic costs of not restoring (Murcia et al. 2014). Figure 1.1 Alternatives within the Restorative Continuum compared with natural ecosystem development (assuming linear development) including the novel ecosystem concept, reclamation, as well as partial and full recovery following restoration. This illustration was adopted from Bradshaw (1984). 1.2 Information Needs for Forest Ecosystem Recovery Assessments Ecological assessments and monitoring are important to assess outcomes following restorative activities. These assessments require information on various ecosystem attributes which capture the status of the ecosystem, ecological threats (e.g., erosion, invasive species), as well as ecosystem functioning (e.g., nutrient cycling). The Restoration Recovery Wheel (Figure 1.2), which is a tool from the Society of Ecological Restoration, provides an overview of ecosystem attributes (such as 4 species diversity and vegetation structure amongst many more) that can be assessed at restoration sites (Gann et al. 2019). Using this tool, the level of recovery of an ecosystem is ranked using a five-star system by ecosystem attribute, as highlighted by Figure 1.3. Moreover, assessments of these attributes can play a key role in characterizing reference conditions (i.e., restoration targets) and in planning mitigation measures (Hiers et al. 2012; Young et al. 2022). Notably, in industrial settings, such as surface mining, gas exploration, and gas transportation through pipelines, ecological assessments are commonplace, as they are essential to maintain operational approval and to plan restorative activities. In addition, financial resources are relatively abundant in this context (Martins et al. 2020). 5 Figure 1.2 The ecosystem recovery wheel, including key ecosystem attributes, developed by the Society of Ecological Restoration (SER). This image was adapted from Gann et al. (2019). 6 Figure 1.3 An example of an ecosystem recovery ranking using the five-star system. This image was adapted from Gann et al. (2019). Conventionally many of the attributes collected to describe the ecosystemâs status are acquired through botanical surveys (Wortley et al. 2013; Young et al. 2022). Botanical surveying, which may comprise quadrat and line-point intercept methods, may be challenging in hard-to-reach areas like wetlands or places with numerous fallen trees (Anderson and Gaston 2013). In addition, the costs of surveys vary. Costs increase with increased sampling density, a focus on a greater number of ecosystem attributes, or with expanded spatial, temporal, and thematic scales (Shuman and Ambrose 2003; Braun and Reynolds 2012). Lastly, field measures can be averaged and extrapolated across project sites; however, this may not be sufficient to capture spatial variability and patterns (Hernandez-Stefanoni and Ponce-Hernandez 2006). 7 1.3 Remote Sensing in Recovering Forest Ecosystems In recent decades, the democratization of remote sensing data has empowered ecologists to explore ecosystem recovery and processes from new spatial and temporal perspectives (Cruzan et al. 2016; Tay et al. 2018; Robinson et al. 2022; Gonzales et al. 2022; Schad and Fischer 2023). This capability is enabled at global and regional scales through the use of spaceborne and airborne data, particularly relevant for repetitive monitoring. However, when studying ecosystems at finer scales, such as near the plant level, the emergence of drones, also known as remotely piloted aerial vehicles (RPAS), has become a promising remote sensing platform. These drones can acquire data on-demand at spatial resolutions finer than 10 cm. In the following section, I will provide an overview of drone platforms and introduce two pertinent remote-sensing data types: spectral imagery and three-dimensional data. 1.3.1 Remotely Piloted Aerial Systems (RPAS) RPAS, or drones, have potential in ecology for assessing vegetation characteristics and plant community development at very fine spatial resolutions, following on-demand data collection (Robinson et al. 2022). Drones provide ecologists with an airborne remote sensing platform that is able to perform inspections and acquire data close to the ground, while being operated from a nearby location. They are capable of navigating challenging terrains, thereby reducing the necessity and risks associated with field surveys. Among many applications, data acquired from drone platforms can be employed to examine vegetation structure (Nuijten et al. 2021), plant functional vegetation types (Villoslada et al. 2020), plant health trends (Robinson et al. 2022), aboveground biomass (Zhou et al. 2023), indicator plants (Buters et al. 2019b; Chadwick et al. 2022a), and invasive species (Qian et al. 2020). They may also be used to study habitat 8 characteristics that may shape the identity of plant communities such as (micro) topography (Ikkala et al. 2022) and surface moisture (Khanal et al. 2017; Moudry et al. 2019; Watts et al. 2023). Figure 1.4 Examples of common remotely piloted aerial systems (RPAS) including (a) multi-rotor platforms with integrated red-green-blue (RGB) camera (a) and interchangeable payload (b), a fixed-wing platform (c), and a system for high-precision geo-spatial positioning (d). 9 Drones encompass fixed-wing and multi-rotor designs and can be equipped with a range of sensor types (Figure 1.4). Fixed-wing drones have longer flight range, however multi-rotor platforms provide greater maneuverability, making them more suitable for missions close to obstacles such as trees (Schad and Fischer 2023). Available sensors include conventional red-green-blue (RGB), multispectral, hyperspectral, and thermal devices from which detailed composite images can be derived. Depending on the sensor, vegetation can be characterized through differences in spectral reflectance, image texture, and shapes. Drones can also enable three-dimensional reconstructions of the environment using Light Detection and Ranging (LiDAR), which is an active form of remote sensing, or fine-spatial resolution drone photos (Vierling et al. 2008; DâUrban Jackson et al. 2020). The latter involves acquisition of many overlapping photos and subsequent processing using photogrammetric techniques (i.e., digital aerial photogrammetry; DâUrban Jackson et al. 2020). Despite increased attention in drone remote sensing for ecosystem recovery assessments in the scientific domain, their application in the contexts of ecological restoration and reclamation is nascent (Robinson et al. 2022; McKenna et al. 2023). This limitation may be attributed to unfamiliarity with complexities in data acquisition and processing, uncertainty of information, comprehensiveness of information, and cost-effectiveness. For instance, their effectiveness might be constrained in ecosystems with variable vegetation densities and high species diversity, such as early to mid-seral forests (Moudry et al. 2019; Durgan et al. 2020; Wilson et al. 2022), and in estimating certain ecosystem attributes (Almeida et al. 2021). Furthermore, there may be regulatory and ethical concerns (Coops et al. 2019; Taylor 2023). Nevertheless, the potential of drones is anticipated to further expand, driven by advances in sensor technologies and computational capabilitiesâproviding new types of data, improving the reliability of data 10 collection, and improving accessibility (i.e., costs, user-friendliness of software, protocols; Chadwick et al. 2022b). 1.3.2 Spectral Imaging Multispectral and hyperspectral remote sensing, as illustrated in Figure 1.5 (del Río-Mena et al. 2020; Robinson et al. 2022; Rudge et al. 2022), enables the characterization of vegetation across various spatial scales due to their distinctive spectral signature. Specifically, the derived spectral imaging data can correlate with vegetation characteristics such as photosynthetic activity, canopy cover, plant functional types, and to a lesser extent with species abundance, species traits, and species/functional richness (Curran 1989; Madonsela et al. 2017; Samiappan et al. 2019; Skidmore et al. 2021; Fassnacht et al. 2022). Healthy green vegetation absorbs light in the blue spectrum (around 450â485 nm) and the red spectrum (around 625â690 nm) during photosynthesis, largely associated with chlorophyll-a and chlorophyll-b. Simultaneously, in the near-infrared region (around 730â1200 nm), healthy vegetation strongly reflects light. This is attributed to the cellular structure of leaves. Other pigments that contribute to changes in absorption within the blue and green (500â565 nm) regions include carotenoids (carotenes and xanthophylls) and anthocyanins (Sims and Gamon 2002). 11 Figure 1.5 Spectral signatures of various wetland species found in Spitsbergen (A) and multispectral reflectance (from RapidEye satellite sensor) of various wetland types found in Newfoundland and Labrador, Canada (B). The spectral bands from the RapidEye satellite are highlighted in A. Images were adapted from Amani et al. (2018) and Zagajewski et al. (2017). Multispectral and hyperspectral sensors have different advantages and limitations. Hyperspectral sensors have finer spectral resolution and wider spectral range (typically 400â2500 nm), but coarser spatial resolutions. Multispectral cameras capture wider spectral bands (typically 4â10 bands) at fine spatial resolution (Adam et al. 2010). Figure 1.5 (right) illustrates how different wetland types can be distinguished using multispectral data (from the RapidEye satellite sensor; Amani et al. 2018). However, Figure 1.5 (left) emphasizes that reflectance can vary within species/plant types, as it is affected by plant health/stress, plant morphology (e.g., leaf density), and scattered light from proximate surfaces (Zagajewski et al. 2017). This within-species variance may pose limitations, particularly for multispectral sensors. Further investigation is needed to determine the extent to which species in regenerating wetlands and forests can be effectively separated using such datasets, and whether additional volumetric data can enhance this capability. 12 1.3.3 Three-Dimensional Remote Sensing Data 3D remote sensing data can provide additional insights on vegetation structure, habitats, and ecosystem functioning (Vierling et al. 2008; DâUrban Jackson et al. 2020). Several remote sensing technologies can produce 3D structural data of vegetation and the Earthâs surface, including photogrammetric approaches and LiDAR. However, these data typically capture only the morphometrics of the upper canopy and, depending on the technology and canopy density, structures below the canopy. Therefore, it may be more accurate to refer to 2.5D data. LiDAR, typically acquired from airborne platforms, is commonly used for characterizing terrain, producing Digital Elevation Models (DEMs), assessing forest inventory attributes, and characterizing habitat structure at regional and even local scales (Vierling et al. 2008; White et al. 2016). LiDAR is an active form of remote sensing utilizing laser pulses to measure ranges (i.e., distances) and producing point clouds, capturing multiple vertical vegetation strata and terrain features depending on the number of laser returns and intensity of the sensor. This is achieved by measuring the time taken by a pulse to travel from the sensor to a target, reflect back, and return to the sensor (Lim et al. 2003). However, airborne LiDAR acquisition is associated with relatively high costs, and spatial resolutions may not always be suitable for fine-scale vegetation assessments. To address this limitation, integrating LiDAR with drones is feasible, though the high sensor cost may be a barrier (Buters et al., 2019; DâUrban Jackson et al., 2020). Structure-from-Motion photogrammetry, also referred to as digital aerial photogrammetry (DAP) when drone-based, is a low-cost computer vision technique that enables the reconstruction of detailed surfaces, represented as point clouds or digital surface models, and orthomosaics 13 (Westoby et al. 2012; Goodbody et al. 2019). Other benefits include the high spatial resolution of reconstructions and the availability of multi-spectral information (Vastaranta et al. 2013). However, it's important to note that DAP uses sequences of photographs from conventional RGB or multispectral cameras, limiting its capability to characterize lower components of the shaded canopy. Increasing computing power, automated and simplified processing workflows, development of open-source software, and decreasing computer costs are making DAP increasingly accessible for ecological monitoring (Colomina and Molina 2014; Forsmoo et al. 2019). This includes software to perform required pre-processing, as highlighted in Figure 1.6 (Isenburg 2012; Roussel et al. 2020). However, research on characterization of short vegetation structures has principally been focused on DAP height validation and mapping of vegetation structure in relatively homogenous or sparsely vegetated environments, such as savannas (Cunliffe et al. 2016; Chen et al. 2017; van Iersel et al. 2018a). Further improving the understanding of capacity of rapidly deployed DAP datasets to characterize short stature vegetation structure (e.g., < 2m) within naturally regenerating ecosystems (i.e., high variability in vegetation cover and structure) is important. 14 Figure 1.6 Illustrations of a DAP point cloud for a section of a wildfire-disturbed site demonstrating various post-processing steps, including: (B) Noise removal and reducing (i.e., subsampling) points; (C) Ground detection; and (D) Height normalization. The DAP processing steps, as illustrated in Figure 1.7, encompass the following stages (Granshaw 1980; Murtiyoso et al. 2018): 1. Detection of common features in multiple photographs taken from various vantage points, such as those captured by a forward-moving drone. This is followed by the estimation of image positions and orientations, extended with further optimization of the cameraâs internal calibration parameters (i.e., bundle block adjustments; Panel A). 15 2. Triangulation to calculate the 3D positions of points based on their projections in two or more images (Panel B). 3. Generation of a digital surface model from the generated point cloud through interpolation (Panel B). 4. Geometric correction and mosaicking of the original photographs to produce an orthomosaics (Panel C). It is important to note that the 3D reconstructions may lack real-world scales unless Global Navigation Satellite System (GNSS) measures are incorporated. Typically, a drone platform integrates a GNSS unit for geospatial positioning, and if necessary, ground control points (GCPs) are utilized. GCPs, which are on-ground targets visible in the captured photographs, are measured with a handheld GNSS device to ensure accurate scale and georeferencing. Note that corrections of GNSS measures, using differential stations, are needed to improve positioning accuracies to up to 15 centimeters (Cho et al. 2023; Assiri et al. 2023). Figure 1.7 Illustrations of digital aerial photogrammetry (DAP) including: (A) Tie-point generation and image matching; (B) Bundle block adjustment (including triangulation); (C) geometric correction and mosaicking of the original photographs using a digital surface model. 16 1.4 Research Objectives and Questions Over the last decade, digital aerial photogrammetry (DAP) using drone-based optical imagery has emerged as a cost-effective technique for assessing various ecosystem attributes, such as plant functional type composition and structure. However, its effectiveness for characterizing early successional forest ecosystems (which are either recently disturbed or maintained at this stage of succession) remains under-explored. The primary goal of this thesis is therefore to examine the role of drone-based DAP in providing spatially explicit, valuable, and cost-effective insights into ecosystem recovery by characterizing a wide range of ecosystem attributes. This involves the recovery of structure, biodiversity, and ecological functions. My main research question is: What role can drone-based DAP play in providing spatially explicit, valuable, and cost-effective insights into ecosystem recovery by characterizing a comprehensive array of ecosystem attributes? To answer this question, the following four sub questions were formulated: 1. To what extent can the structure of early regenerative vegetation be characterized using DAP? 2. To what extent can keystone plant communities be mapped using DAP derived spectral and structure data? 3. To what extent can conventional field sampling and drone maps be used to investigate how vegetation across pipeline ROWs mimic regeneration patterns following natural disturbance (e.g., wildfire), and what are their intrinsic differences? 4. How do drone-based remote sensing approaches compare to conventional field surveying in terms of information uncertainty, complexity, and cost-effectiveness, and to what degree can these information characteristics be improved? 17 1.5 Dissertation Overview This dissertation is organized into six Chapters (Chapters 2â7). Figure 1.8 provides a conceptual overview of the core research questions covered in Chapters 3 to 6. Figure 1.8 Illustration capturing dissertationâs research questions. Chapter 2 outlines the thesis's main focus areas, offers an overview of acquired remote sensing and field datasets, and explains the photogrammetric processing workflows. Chapter 3 (Q1) examines DAP's capacity to characterize vegetation structure in early successional forests. 18 Chapter 4 (Q2) investigates DAP's ability to map dominant plant species and functional types using spectral reflectance metrics and vegetation structure maps. Chapter 5 (Q3) demonstrates how established ecological statistics can be applied to drone-based maps for comparing species/plant type composition, highlights differences between field-based and drone approaches, and discusses the use of wildfires as a reference in restoration. Chapter 6 (Q4) provides insights into the information uncertainty, complexities, and cost-effectiveness of drone-acquired information regarding ecosystem recovery, in comparison to conventional field-based approaches. Alternative drone remote sensing technologies are also included in this investigation. Chapter 7 concludes the dissertation by synthesizing the work, highlighting innovations, and discussing its limitations and future research opportunities. 19 Chapter 2: Study Area and Data 2.1 Background Study Region In North America, the Province of Alberta in western Canada is of particular interest due to the substantial impact of infrastructure associated with natural resource extraction, as well as disturbances from wildfires and insect-related events, on forested landscapes (Whitman et al. 2022). From 2012 to 2020, the average annual area disturbed by wildfires and insect activities (generally mountain pine beetle infestations) was 267,000 ha and 2,333,000 ha, respectively. Conversely, timber harvesting accounted for an average annual disturbance of 91,000 ha (Government of Alberta 2022). The exact surface area impacted by the mining, oil, and gas industry remains uncertain, although Timoney and Lee (2001) estimated that the total length of seismic lines alone was 1.5â1.8 million km. These infrastructural networks can have significant impacts on the biodiversity of flora and fauna. For example, the region overlaps with the range of threatened woodland caribou (Rangifer tarandus caribou), whose movement patterns and predator-prey interactions can be influenced by forest fragmentation (Smith et al. 2000; Dyer et al. 2001). Some linear infrastructures, including seismic lines and operational pipelines, may become corridors for human movement. On pipeline rights-of-way (ROWs), this includes not only public recreation but also access for companies to inspect and maintain their infrastructure. This also requires occasional clearing and mowing to facilitate operations (Eyre et al. 2015). This presents substantial challenges to land managers who strive towards lessening their impact (Young et al. 2022). 20 2.1.1 Restorative Activities Reclamation on pipeline ROWs in Alberta involve actions to minimize land disturbance and revegetation following a number of reclamation criteria (AEP 2010). In forested areas, these reclamation criteria can be related to total canopy cover, cover ratios for plant functional types (e.g., woody and herbaceous), vegetation height, and ratings related to invasive species presence (AEP 2010). Land disturbances are minimized by limiting footprints, stockpiling and backfilling of soil, the restoration of drainage, and access management (Government of Alberta 2010; Barker 2018; TC Energy 2020a). Access management, which includes the placement of logs and planting conifers, is focussed on discouraging recreation as well as predator movements. Moreover, erosion is managed by staking willows (Salix spp.; TC Energy, 2014, 2020a). In woodland caribou range pipeline operators are mandated to implement habitat restoration, with federal requirements defining project-specific Caribou Habitat Restoration and Offset Measures and Monitoring Plans (CHROMMP; TC Energy 2020a). Restoration requirements can be related to visual line-of-sight, seedling density, characteristic species presence, as well as species and plant functional type diversity (covering a greater range compared to reclamation; TC Energy 2020a). Lastly it is important to note that ecosystem recovery on pipeline ROWs, largely relies on natural regeneration. In contrast, surface mining, for instance, necessitates more intensive restoration activities, as mining drastically alters soil characteristics. Furthermore, restoration on mining sites typically involves establishing a heterogeneous topography, relocating soil materials, and planting a diverse range of tree species with varying stress tolerances, habitat preferences (such as pH 21 levels), and successional statuses (Macdonald et al. 2015). However, these sites may be less likely to be subjected to anthropogenic disturbance after decommissioning. 2.1.2 Ecosystem Assessments Assessed ecosystem attributes, which relate to Albertaâs reclamation criteria, primarily focus on vegetation communities and structure. These attributes are commonly evaluated in connection with offsite conditions, which are usually nearby undisturbed areas with similar habitat type as the ROW (Flemming et al. 2023). The surveys are performed using circular in situ plots of 3.99 m radius (50 m2) or 1.79 m radius (10 m2), which include visual estimates of species diversity. Additionally, ROWs are visually inspected from helicopters, typically to identify erosion issues and invasive species (Filicetti et al. 2019; TC Energy 2020a). Assessments are conducted at intervals of 1-, 2-, and 5-years following pipeline installation. However, in woodland caribou range, such intervals may extend, for example, up to 15 years, in accordance with project approval conditions. 2.1.3 Focus Areas The focus areas for this research are located in the forested regions of the Province of Alberta, Canada (Figure 2.1). Of these, three are situated on pipeline ROWs, while the remaining three lie in areas disturbed by wildfires, serving as natural analogues (or reference sites). These sites overlap with two woodland caribou ranges, which I have used to name the study sites: Chinchaga and Little Smoky. 22 Figure 2.1 Locations of the right-of-way (ROW) and wildfire-disturbed analogous sites in northwest Alberta, Canada, including current woodland caribou (Rangifer tarandus caribou) ranges. 2.1.4 Ecoregions, Climate, and Land Management The Chinchaga sites are located in the lower boreal highlands ecoregion, while the Little Smoky sites are situated in the upper foothills ecoregion (near the Rocky Mountains). The lower boreal highlands, with elevations ranging from 400â1075 m, are typified by extensive wetlands and peatlands within flat areas and mixedwood forests situated on hill slopes. These forests are predominantly composed of aspen (Populus tremuloides), balsam poplar (Populus balsamifera), black spruce (Picea mariana) and white spruce (Picea glauca). In contrast, the upper foothills, 23 with elevations between 950â1750 m, host a more diverse mix of ecosystems. They are largely dominated by coniferous forests, particularly lodgepole pine (Pinus contorta) and spruce stands. Deciduous and mixedwood forests occur on south and west-facing slopes, poor-to-rich fens in valleys, and shrubby grasslands on the driest sites (Alberta Parks 2015). Land management and climate varies between the regions. The Little Smoky has been extensively disturbed by clear-cut logging whereas the Chinchaga plain has relatively few forest-harvest tenures and is partially protected (Hervieux et al. 1996; Canadian Parks and Wilderness Society 2003). Little Smoky has a longer growing season (5â15 days more with temperatures above 5°C) and less extreme winter temperatures compared to the Chinchaga region (Alberta Climate Information Service 2023). 2.1.5 Vegetation Common native pioneers (establishing within the first 5 years) in the regions include fireweed (Chamerion angustifolium), aspen, black spruce (on moisture sites), white spruce (on drier sites), paper birch (Betula papyrifera), and bluejoint grass (Calamagrostis canadensis; Gärtner et al., 2011; Peters et al., 2006; Willoughby et al., 2018, 2016). Moreover, non-native forbs are known to be a large pioneer component on reclaimed lands (Azeria et al. 2020; Trepanier et al. 2021b, a), potentially due to abundant seed sources and connectivity within the landscape. Note that historically non-native species such as timothy grass (Phleum pratense), alsike clover (Trifolium hybridum), red clover (Trifolium pratense), white clover (Trifolium repens), and the native grass fringed brome (Bromus ciliates) were widely used for reclamation (Hardy BBT Limited 1989; Azeria et al. 2020). 24 Few species play an important role in Alberta's boreal region for facilitating woody plants like willow, spruce, and aspen. These include haircap mosses (Polytrichum spp) that prefer good drainage, sphagnum mosses that thrive in areas of poor drainage and high soil moisture, sedges (Cyperaceae spp.), as well as species that encourage the presence of free-living nitrogen-fixing bacteria like alders (Alnus spp; Gärtner, Lleffers, and Macdonald 2011; Koropchak et al. 2012; Urlia et al. 2020). Late-successional understorey species in both regions include Vaccinium spp., Labrador tea (Rhododendron groenlandicum), lowbush cranberry (Viburnum edule), creeping dogwood (Cornus canadensis), horsetail (Equisetum spp.), wild rye (Elymus spp.), and lichen. Other important forb and grass components include various asters (Asteraceae spp.) and downy ryegrass (Leymus innovatus), respectively (Alberta Environment 2010; Alberta Parks 2015; Trepanier et al. 2021b). 2.2 Study sites The dissertation focuses on three ROWs with operational pipelines (i.e., reclamation sites) as well as three proximate, similarly aged wildfire-disturbed reference sites. This approach enabled, as a proof-of-concept, comparisons of reclamation sites against those disturbed by wildfires, suggested as recovery references in Chapter 5. Below I describe the characteristics of the pipeline ROWs (Section 2.2.1) and wildfire-disturbed sites (Section 2.2.2), including the underlying assumptions for their use. 25 2.2.1 Pipeline Right-of-Ways (ROW) Three pipeline ROW sites were selected across the Nova Gas Transmission Ltd. System (NGTL) while aiming to cover multiple ecoregions, a range of pipeline ages, and varying slope and elevation. All were located within proximity to roads (for field and drone control access). Details are summarized in Table 2.1. 26 Table 2.1 Summary of the study sites. The ecosite types were derived from the Derived Ecosite Phase (DEP; Alberta Agriculture and Forestry 2020). The two wildfire sites within the Chinchaga region were treated as a unified site due to their similar age and site conditions. Chinchaga A Chinchaga B Chinchaga A and B Little Smoky Little Smoky Reclamation site Reclamation site Reference site Reclamation site Reference site Disturbance Type Pipeline ROW Pipeline ROW Wildfire Pipeline ROW Wildfire Ecoregion Lower Boreal Highlands Lower Boreal Highlands Lower Boreal Highlands Upper Foothills Upper Foothills Disturbance Feature Name Chinchaga Lateral Loop Chinchaga Lateral Loop Lovet Creek Fir (2018) and Battle Complex Fire (2019) Unknown Deep Valley Fire (2006) Years since Disturbance 10 5 A=3; B=2 14 15 Elevation (m) 720 785 760â765 1180 1210 Slope (°) and Aspect Level Level Level 7.5, NW 1.5, NW Ecosite Types (Pre-Disturbance) d; mesic/ medium (low-bush cranberry) d; mesic/ medium (low-bush cranberry) h; subhydric/ poor (bog) d; mesic/ medium (low-bush cranberry) h; subhydric/ poor (bog) d; mesic/poor (Labrador tea-mesic) e; mesic/medium (tall bilberry/ arnica) d; mesic/poor (Labrador tea-mesic) e; mesic/medium (tall bilberry/ arnica) Relative Wind Direction in Summer Parallel to Pipeline Parallel to Pipeline n/a Perpendicular to Pipeline n/a 27 2.2.1.1 Pre-Disturbance Ecosite Types Ecosites (defined by moisture regime and nutrient content) that were found at the study sites prior to disturbance (according to Albertaâs Ecosite Phase map) include ecosite types d (mesic with poor-to-medium nutrient content) and e (mesic with medium nutrient content), as detailed in Table 2.1 (Alberta Agriculture and Forestry 2020). These ecotypes are associated with fine loam-clay soils. Additionally, ecotype h was identified, characterized by the presence of organic layers, subhygric conditions, and low nutrient content. 2.2.1.2 Disturbances The pipelines were constructed 5, 10 and 15 years prior to data collection (Table 2.1). Spatially explicit information regarding construction methods, pipeline locations, and ongoing anthropogenic disturbances was unavailable or non-existent. However, based on discussions with TC Energy, it can be assumed that the pipelines listed in Table 2.1 were constructed using minimal surface disturbance techniques, including soil stockpiling, winter construction, and the use of lightweight machinery (Barker 2018). Notably, pipelines older than 15 years existed on the right-of-ways, which were possibly constructed using different methods. Drone imagery provided insights into current disturbances, revealing recent ATV tracks across all sites and older tracks on ROW Little Smoky, as shown in Figure 2.2. 28 Figure 2.2 Overview of the three right-of-way sites with evidence of disturbances and restoration activities. Subplots a and c include fresh ATV tracks. Subplot f shows old truck tracks. Subplot b includes timber rollback and piling for access/line-of-sight control. Subplots d and e include plantings of conifer seedlings. 29 2.2.1.3 Restorative Activities Spatially explicit information regarding implemented restorative activities to date was limited, however, among the sites, ROW Chinchaga B exhibits evidence of restorative activities, such as the rollback and piling of timber for access and line-of-sight management. Additionally, grids of planted conifer seedlings are visible at this site (Figure 2.2). 2.2.2 Wildfire Analogues I suggest the use of proximate, similarly aged, naturally disturbed sites as analogues due to the absence of historical references traditionally used in ecological restoration. Furthermore, I found the use of nearby undisturbed plant communitiesâcommon in the Albertan contextâundesirable for this research. This stems from ecological considerations, as early to mid-successional and mature forest ecosystems differ significantly in structure, biodiversity, and function, and from the primary technical limitation of digital aerial photogrammetry (DAP), which is limited to characterizing the outer envelope of vegetation. Ultimately, I have chosen to use wildfires as naturally disturbed references, prevalent in the region (Government of Alberta 2021). The ecosystems are adapted to wildfires, especially when fire intervals are normal (> 30 years) and fire intensities are low to moderate (Baltzer et al. 2021). Under such conditions, the recovery of native plant species richness and cover, particularly at low-productivity sites, is expected to be rapid (Burkle et al. 2015). Moreover, they can create site characteristics that, depending on severity and extent, provide favorable conditions for natural regeneration of woody species, such as willow, spruce, and aspen. This is related to the exposure of mineral soils (including seedbeds) following the removal of soil organic layers, the increase of soil moisture due to the absence of 30 evapotranspiration, and the rise of soil temperatures beyond 5°C (Gärtner et al. 2011; Trepanier et al. 2021b). 2.2.2.1 Wildfire-Analogue Selection Process The selection of analogues was based on ROW site conditions (e.g., ecoregion, elevation, aspect, years since disturbance, pre-disturbance vegetation communities) while ensuring they were not affected by timber harvest prior to the fires and were located near access roads. The age differences between the ROW sites and analogues ranged from 1 to 7 years at the time of data collection. The site selection process is outlined below and was guided by geospatial layers listed in Table 2.2: 1. Fires within the same ecoregions as the pipeline ROW sites were considered. 2. Fires were selected based on a similar age to the pipeline restoration sites (preferably a difference as small as possible, although the maximum age difference had to be 7 years to include sufficient candidates). 3. Fires within 2000 meters of roads were retained. 4. Fire sites that were not harvested or burned within 50 years prior to disturbance were included. Areas with post-fire salvage logging were not considered. 5. A Topographic Wetness Index (TWI) was derived from the DEM. Fire sites with TWI values similar to the ROW sites were retained. 6. Pre-disturbance vegetation communities (i.e., ecosites) were derived from Albertaâs Derived Ecosite Phase. Fire sites (or parts of them) with similar ecosite type were retained. 31 Table 2.2 A summary of data used for the multi criteria analysis for site selection. Dataset Methods Description Reference Historical Wildfire Perimeter Data Digitized from aerial photos or satellite imagery. Polygons Government of Alberta (2021) Albertaâs digital elevation model (DEM) Aerial photography and photogrammetric techniques 25-m Raster Alberta Environment and Parks (2017) Derived Ecosite Phase Digitized from aerial photos (Albertaâs Vegetation Inventory) and LiDAR-based maps Polygons Alberta Agriculture and Forestry (2020) Timber harvesting polygons Digitized from aerial photos or satellite imagery. Not publicly available. Polygons Government of Alberta, (2021) Natural Regions and Subregions of Alberta Extrapolated from vegetation samples, topographic, and climatic data. Polygons Natural Regions Committee (2006) Access and Facility Roads Not applicable Polylines Government of Alberta (2020) 2.2.2.2 Fire Severity For the Little Smoky area, the fire severity was considered moderate with estimated tree survival rate between 25â50%. This assessment was made by visually counting both live and burned trees in drone-derived ortho imagery. In contrast, the Chinchaga sites exhibited mixed severity. Some sections had relatively high tree survival rates (surpassing 50%), while other sections experienced low survival rates (below 25%). Moreover, certain sections showed minimal evidence of tree presence before the wildfire, characterized by a scarcity of mature trees, whether alive or dead. Specifically for the Chinchaga, this suggests that mineral soil may be exposed in some areas, while patches with thicker organic layers may have persisted (Burkle et al. 2015; Baltzer et al. 2021). 32 2.2.3 Comparison of Disturbance Characteristics It is important to be aware of that vegetation patterns following disturbances exhibit variability influenced by disturbance type and severity, pre-disturbance vegetation, time since disturbance, soil characteristics (e.g., moisture regime, nutrient content, and compaction), and invasive species cover (Gärtner et al. 2011; Burkle et al. 2015; Baltzer et al. 2021). I formulated assumptions regarding disturbance characteristics relevant to natural regeneration, as detailed in Table 2.3. For example, it is conceivable that on burned sites, micro topography is better preserved compared to pipeline ROWs, primarily due to the access required for pipeline construction and maintenance. Additionally, the soil organic matter might be better preserved on pipeline ROWs, as it is typically stockpiled and backfilled during construction. In contrast, on burned sites, especially those of high severity, the organic layers can be lost (Baltzer et al. 2021). Note that the disturbance characteristics for pipeline ROWs (Table 2.3) was inferred from publications on disturbances from surface mining, clear-cut harvesting, and seismic lines. 33 Table 2.3 Relative environmental conditions in two regeneration niches. The table was adapted from Margolis and Brand (1990) and complementary resources including Barker (2018)1, Burkle et al. (2015)2, Caners and Lieffers (2014)3, Flemming et al. (2023)4, Gärtner et al. (2011), Rowland et al. (2009)6, Stern et al. (2018)7, Taylor et al. (2013)8, Trepanier et al., (2021b)9, and Macdonald et al. (2015)10. Regeneration Niche Burned Right-of-way Abiotic variables Light intensity High High Nutrient availability9 Moderateâhigh Lowâmoderate Micro topography3,4,5 High Moderate Available organic material2,9,10 Lowâmoderate Moderate Soil moisture availability6 Moderateâhigh Moderateâhigh Soil temperature5,9 High High Wind speed7 Moderateâhigh High Biotic variables Initial competition dynamics9 Moderate High Internal seed availability1,8 Moderateâhigh Moderateâhigh 2.3 Data Collection Two data collection campaigns were conducted for this dissertation, both in August, timed to coincide with peak biomass and phenological events. In the initial 2019 campaign, botanical surveys covering 12.5 ha of pipeline ROWs were conducted by members of UBC's Integrated Remote Sensing Studio (IRSS), while drone data collection was outsourced to Midwest Surveys. In the second campaign in 2021, I conducted both botanical surveys and drone data collection, covering a total area of 69 ha affected by wildfires for the natural analogy. Chapter 3 exclusively utilizes data from the 2019 campaign, Chapters 4 and 5 draw from both campaigns, and Chapter 6 incorporates data solely from the 2021 campaign. 34 2.3.1 Botanical Surveys Botanical survey data served multiple objectives, including the evaluation of vegetation height in drone imagery and the interpretation of maps produced using unsupervised techniques (in Chapter 3), the training of presence-absence models for species mapping (in Chapter 4), and providing measures of species richness/evenness (in Chapter 5). During the 2021 campaign, plant species, along with their associated height and cover percentage, were sampled within 2x2-m2 quadrats. During the 2019 campaign, sampling was performed using 1x1-m2 quadrats (these were aggregated to match the 2x2-m2 quadrats in Chapter 4 and 5). The quadrats were grouped along 20-meter transects (Figure 2.3) to facilitate the visual validation of spatial alignment with drone imagery, minimizing alignment errors, and spread across dominant vegetation types. Site stratification by vegetation types was performed during the initial field visit, facilitated by in situ sketch mapping with the assistance of a small drone (DJI Mini) for visual inspection. Subsequently, transect positions were refined to include a diverse range of species and maximize variation in terms of canopy cover and site conditions. Moreover, on pipeline ROWs, transects were placed approximately orthogonal to the pipeline to account for anticipated variations in species composition and structure resulting from edge effects (Temperton et al. 2004). In total 22 field transects were placed across all the sites, with an average distance of 110 m between transects, as specified in Table 2.4. 35 Figure 2.3 Overview botanical survey plot. Table 2.4 Characteristics of botanical survey, area coverage by drone-derived imagery, and measured (native) species richness (i.e., count), including richness averaged across field transects and total richness by site. Chinchaga A (Reclamation site) Chinchaga B (Reclamation site) Chinchaga A and B (Reference site) Little Smoky (Reclamation site) Little Smoky (Reference site) Site Area (ha) 2.6 8.2 17 and 28 1.6 24 Number of Field Transects and 2x2-m2 Quadrats 2 (20) 3 (30) 6 (60) and 5 (50) 2 (20) 4 (40) Average Inter-Transect Distance (m) 59 220 72 and 38 91 186 Species Richness (Average and Overall) 17 (28) 12 (23) 24 (57) 15 (22) 13 (27) Native Species Richness (Average and Overall) 13 (22) 11 (21) 23 (52) 13 (20) 13 (27) 36 The start and end positions of the transects were recorded using a Trimble Geo7X ground station and later post-processed using differential GNSS. However, it is worth noting that in remote and forested environments these measures may not provide sufficient accuracy, as pointed out by Forlani et al. (2018). To address this concern, during the 2019 campaign, I recorded proximate features around plots that were easily identifiable from drone imagery. Alternatively, in the 2021 campaign, transects were marked using spray paint before drone acquisition. This aided in refining the transect positions. At each grid cell a species was recorded if it covered at least 5% of the quadrat, with additional metrics being recorded when vegetation height did not exceed 2 m. In some cases, higher taxonomic levels were recorded due to uncertainty and time constraints. Bryophyte species could not be identified. Additionally, the cover of exposed soil and dead wood were recorded. Cover estimation was supported by illustrations shown in Figure 2.5. Lastly, the maximum height of the canopy was recorded by species using a measuring pole marked every 5 cm, following the protocol of Chen et al. (2017) and shown in Figure 2.4. Note that irregular protruding branches were not included, as they were not perceived as being part of the dominant canopy. 37 Figure 2.4 An illustration of vegetation height recording. This image was from Chen et al. (2017). 38 Figure 2.5 Supportive illustrations for visual estimation of foliage cover developed by Terry and Chilingar (1955). 39 2.3.1.1 Plant Species In total, 22 field transects were established across all sites, with an average distance of 110 meter between transects, as detailed in Table 2.4. The collected dataset comprised 98 taxonomic units. Among them, 27 taxonomic units were present in a minimum of 20 quadrats, indicating their prevalence in the study regions. The number of native species varied across the pipeline ROW and reference sites. On the ROW sites, an average of 11â13 native species were identified while on the reference sites, 13â23 were observed. The number of invasive species found in transects on the ROWs ranged from 1â4, while for the Chinchaga wildfires, there was an average of 1 invasive species per transect, and none on the Little Smoky wildfire. A detailed species list can be found in Table 2.5. Table 2.5 Plant species observed on sampled transects, including representative plant functional type and study sites of occurrence, which include Chinchaga A (Ch_A), Chinchaga B (Ch_B), and Little Smoky (LS). Plant Functional Types Observed Species Occurrence Occurrence ROWs Wildfires Conifers Black Spruce (Picea mariana) Ch_A, Ch_B Ch_A, Ch_B, LS Lodgepole Pine (Pinus contorta) Ch_A, Ch_B, LS LS White Spruce (Picea glauca) Ch_A, LS Ch_A, Ch_B, LS Shrubs Aspen Poplar (Populus tremuloides) Ch_A, Ch_B Ch_A, Ch_B, LS Alders (Alnus spp.) - Ch_A, Ch_B Bearberry (Arctostaphylos uva-ursi) - Ch_A Balsam Poplar (Populus balsamifera) Ch_B, LS Ch_A, Ch_B, LS Bog Rosemary (Andromeda polifolia) - Ch_B Birch (Betula spp.) Ch_B Ch_A, Ch_B, LS Black huckleberry (Gaylussacia baccata) - LS Canada buffaloberry (Shepherdia Canadensis) Ch_A - Cranberry (Vaccinium vitis-idaea) Ch_A Ch_A, Ch_B, LS Creeping saltbush (Atriplex Semibaccata)1 - Ch_A Fire cherry (Prunus virginiana) - LS Gaultheria (Gaultheria sp.) - LS Honeysuckle (Lonicera dioica) LS - Labrador Tea (Rhododendron groenlandicum) Ch_A Ch_A, Ch_B, LS Lowbush cranberry (Viburnum edule) - Ch_B Prickly wild rose (Rosa acicularis) - Ch_A, Ch_B Wild Rose (Rosa acicularis) Ch_A, Ch_B Ch_A, Ch_B 40 Skunk currant (Ribes glandulosum) - Ch_A, Ch_B Wild redcurrant (Ribes triste) - Ch_A Willows (Salix spp.) Ch_A, Ch_B, LS Ch_A, Ch_B, LS Tall-growing forbs Asters (Asteraceae spp.) Ch_A, Ch_B, LS Ch_A, Ch_B, LS Blueberry (Vaccinium myrtilloides) LS Ch_A, Ch_B, LS Common nettle (Urtica dioica) - Ch_A Cow parsnip (Heracleum maximum) - Ch_A Yarrow (Achillea spp.) Ch_A, Ch_B Ch_A, Ch_B False hellebore (Veratrum viride) - LS Fireweed (Chamerion angustifolium) Ch_A, Ch_B, LS Ch_A, Ch_B, LS Narrowleaf hawksbeard (Crepis tectorum)1 - Ch_A Greater Can. St. Johnâs wort (Hypericum majus) - LS Goosefoots (Chenopodium spp.) Ch_A, LS Ch_A Indian Paintbrush (Castilleja spp.) LS - Meadow Hawkweed (Hieracium caespitosum)1 Ch_A - Perennial Sow Thistle (Sonchus arvensis)1 Ch_A - Spurred Gentian (Halenia deflexa) - Ch_A Wild strawberry (Fragaria virginiana) Ch_A, LS Wild vetch (Vicia americana) Ch_A, Ch_B Ch_A, Ch_B Willowherbs (Epilobium) - Ch_B Yellow Rattle (Rhinanthus minor) Ch_B, LS - Yellow Sweet Clover (Melilotus officinalis)1 Ch_B - Low-growing forbs Bedstraw (Galium sp.) - Ch_A Broad-leaved Peppergrass (Lepidium latifolium)1 Ch_A - Calypso orchid (Calypso bulbosa) - Ch_A, Ch_B Creeping Thistle (Cirsium arvense)1 Ch_A - Clematis (Clematis sp.) - Ch_A Cloudberry (Rubus chamaemorus) Ch_B Ch_A, Ch_B, LS Clovers (Trifolium spp.)1 Ch_A, Ch_B, LS Ch_A Coltsfoots (Petasites spp.) - Ch_A, Ch_B, LS Creeping dogwood (Cornus canadensis) - Ch_A, Ch_B, LS Dandelion (Taraxacum officinale)1 Ch_A, LS Ch_A, Ch_B Dwarf Raspberry (Rubus arcticus) - Ch_A Horsetail (Equisetum arvense) Ch_A, Ch_B, LS Ch_A, Ch_B, LS Grass-of-Parnassus (Parnassia palustris) - Ch_A Three-leaf Solomn-seal (Maianthemumtrifolium) - Ch_A, Ch_B Twinflower (Linnaea borealis) - Ch_A, Ch_B Northern Green Orchid (Platanthera aquilonis) - Ch_B Wild Raspberry (Rubus ideaus) - Ch_A, Ch_B Spotted touch-me-not (Impatiens capensis) - Ch_A Showy Everlasting (Antennaria pulcherrima) - Ch_A Tall Bluebells (Mertensia paniculata) - Ch_A, Ch_B Western mountain-ash (Sorbus scopulina) - LS White Peavine (Lathyrus palustris) Ch_A - Grass-like species Baltic Rush (Juncus arcticus) Ch_B - Bulrush (Schoenoplectus sp.) LS - Barleys (Hordeum jubatum) - Ch_A Bluejoint grass (Calamagrostis Canadensis) Ch_A, LS Ch_A, LS Common cattail (Typha latifolia) Ch_B - Yellow Foxtail (Setaria pumila)1 - Ch_A Hairy wildrye (Leymus innovatus) Ch_B Ch_A, Ch_B Kentucky bluegrass (Poa pretense) Ch_A, Ch_B, LS Ch_A, Ch_B, LS Grasses (Poaceae spp.) Ch_A, Ch_B, LS Ch_A, Ch_B, LS 41 Prairie cordgrass (Spartina pectinata) - Ch_A Reed Canary Grass (Phalaris arundinacea) Ch_B, LS Ch_A, Ch_B Timothy (Phleum pratense) - Ch_A Sedges (Carex spp.) Ch_A, LS Ch_A, Ch_B, LS Mosses Spp. not recorded Ch_A, Ch_B, LS Ch_A, Ch_B, LS 2.3.2 Drone Imagery Collection In late summer (AugustâSeptember) of 2019 and 2021, images were captured using a quadcopter drone (DJI Phantom 4 Pro/Phantom 4 RTK/DJI Matrice M210 RTK V2). A 20-megapixel RGB sensor was used to acquire imagery at 1â3.3 cm resolution (sensors listed in Table 2.6), while a Micasense RedEdge-MX 1.2-megapixel multispectral monochromatic sensor collected imagery at 2.2â5 cm resolution (Table 2.7), capturing reflectance values at five distinct spectral bands: blue (443â507 nm), green (533â587 nm), red (652â684 nm), red edge (705â729 nm) and near infrared (785â899 nm). For radiometric correction of the multispectral images, a calibrated reflectance panel was used to take calibration images before and after each flight, as described in Agisoft LLC (2023) and Micasense (2023). This enabled the conversion of raw digital numbers (DN) captured by the sensor into reflectance values, expressed as a percentage of reflected light relative to the incident light. Additionally, a downwelling light sensor was used to correct for changing light conditions during the flight, such as the movement of the sun and shifting cloud cover. For the multispectral sensor, both image sidelap and frontlap were configured to yield an 80% overlap which is necessary to account for reduced light energy reaching the camera at wide angles (MicaSense 2023). Image overlap was generally higher during RGB data collection (Table 2.6), which is required for the construction of detailed point clouds of short-stature vegetation as demonstrated by Cruzan et al. (2016) and Tu et al. (2020). Most multispectral images were collected during overcast weather, however, unavoidable sunny conditions at the ROW Little 42 Smoky site resulted in shading (Table 2.7). Weather conditions were more variable across RGB image acquisition (Table 2.6). To ensure accurate scale and georeferencing of the volumetric reconstructions and orthomosaics, I utilized Ground Control Points (GCPs) during the 2019 campaign. GCPs were strategically positioned along the ROWs at approximately 200-meter intervals, resulting in a total of 4, 10, and 6 GCPs at ROW Chinchaga A, B, and Little Smoky, respectively. This aligns with Lundine (2018), who recommends 4â6 GCPs per hectare to reduce geometric distortion to within 5â10 cm. Alternatively, imageâ geospatial positions were adjusted using correction factors derived from a mobile Real-Time Kinematic (RTK) base station (2021 field campaign), as depicted in Figure 1.4. 43 Table 2.6 Summary of flight parameters, conditions, and data specifications for collection of RGB imagery with various sensors. Chinchaga A (Pipeline ROW) Chinchaga B (Pipeline ROW) Chinchaga A (Wildfire) Chinchaga B (Wildfire) Little Smoky (Pipeline ROW) Little Smoky (Wildfire) Platform Phantom 4 Pro Phantom 4 Pro Phantom 4 RTK Phantom 4 RTK / Matrice 210 RTK Phantom 4 Pro Matrice 210 RTK Sensor Phantomâs camera Phantomâs camera Phantomâs camera Phantomâs camera / Zenmuse X5S Phantomâs camera Zenmuse X5S Acquisition Date 2019-08-14 2019-08-24 2021-08-22 2021-08-23 2019-09-17 2021-08-19 Weather Conditions Overcast Overcast Mix. clouds Mix. clouds Sunny Overcast Flight Altitude (m) 90 125 50 38 110 54 Forward and Side Overlap (%) 75 - 80 85 - 80 85 - 85 85 - 85 90 - 80 85 - 85 GSD Orthomosaics (cm) 2.5 3.3 1.4 1 2.4 1.5 Point Density (pts m-2) 885 950 1915 3760 1637 1190 44 Table 2.7 Summary of flight parameters, conditions, and data specifications for the collection of multispectral imagery with Micasense Rededge-MX. Chinchaga A (Pipeline ROW) Chinchaga B (Pipeline ROW) Chinchaga A (Wildfire) Chinchaga B (Wildfire) Little Smoky (Pipeline ROW) Little Smoky (Wildfire) Platform Phantom 4 Pro Phantom 4 Pro Matrice 210 RTK Matrice 210 RTK Phantom 4 Pro Matrice 210 RTK Acquisition Date 2019-09-03 2019-08-25 2021-08-21 2021-08-23 2019-09-17 2021-08-19 Weather Conditions Overcast Overcast Overcast Overcast Sunny Overcast Flight Altitude (m) 60 65 45 40 70 42 Forward and Side Overlap (%) 80 - 80 80 - 80 80 - 80 80 - 80 80 - 80 80 - 60 GSD Orthomosaics (cm) 4.4 4.7 3 2.2 5 2.9 2.3.3 Measuring Duration of Data Collection For cost-effectiveness calculations performed in Chapter 6, I recorded the sampling duration for 8 transects, including travelling time between transects, using a two-person team to ensure safety in remote locations. Additionally, I measured the time per site spend on exploration and initial transect placement, which was averagely an hour per site. In turn, personnel requirements for sampling at a site pair of 40 ha (includes a 20 ha reclamation and reference site) were estimated from these measures and presented in Table 2.8. 45 Table 2.8 Required personnel for sampling a site pair of 40 ha total based on time measures Scenario 1: Drone-based Scenario 2: Advanced drone-based Scenario 3: Field-based Scenario 4: Intensive field-based No. of transects 12 12 20 24 Two Field technicians 2 days 2 days 3.3 days 4 days Pilot 2 days 0.8 day None None The drone imagery was collected simultaneously with field sampling by a single pilot. Image acquisition times were retrieved from the timestamps on the first and last image captured each flight using the basic camera setup as previously described in 2.3.2 (Scenario 1 in Table 2.8). In addition, I measured the time required to set up the drone and camera equipment (~45 min per site) and utilize a reflectance calibration panel (~15 min per site), which is essential for correcting multispectral imagery. Hypothetical acquisition times for an advanced camera setup (Scenario 2 in Table 2.9), with higher image resolution and capture speeds, were estimated using flight planning software and corrected using the measured acquisition times from Scenario 1. The flight planning software Map Pilot Pro (Drones Made Easy 2022) was used to estimate times using the parameters in Table 2.9. Estimates of Scenario 2 were then adjusted based on the average percentual difference between time estimates and measures in Scenario 1. 46 Table 2.9 Drone configurations used in the cost-effectiveness analyses. Data acquisition times of scenario 1 were measured in the field, whereas acquisition times for scenario 2 were estimated. Specification (1) Standard drone scenario (2) Advanced drone scenario Platform(s) DJI Phantom 4 RTK DJI Matrice 210 v2 RTK DJI Matrice 300 RTK Multispectral camera Micasense RedEdge-MX Micasense RedEdge-P â¢ Resolution 2.2â3 cm 2.2â3 cm â¢ Image overlap Front: 80%; Side: 80% Front: 80%; Side: 80% RGB camera Phantom 4 RTK Integrated Zenmuse P1 Camera â¢ Resolution 1â1.5 cm 1â.5 cm â¢ Image overlap Front: 90%; Side: 85% Front: 90%; Side: 85% 2.4 Data Processing 2.4.1 Photogrammetric Processing Point clouds and orthomosaics were generated from the acquired imagery using photogrammetric techniques within Agisoft Metashape (v1.6.3). The steps involved, such as image alignment, dense point cloud generation, and orthorectification, are detailed in Section 1.3.3 and depicted in Figure 1.7. To ensure centimeter-accurate alignment between data products from the two imaging sensors, it was necessary to align the RGB and multispectral imagery and optimize image positions in a single operation in Agisoft Metashape. This process resulted in alignment offsets of less than 5 cm; however, it increased the requirements for computing power and random-access memory (RAM, requiring 128 gigabytes). Notable settings in Agisoft Metashape included medium alignment accuracy, high point cloud reconstruction quality, mild depth filtering, and very high DEM quality, consistent with the best practices developed by the U.S. Geological Survey (Over et al. 2021). . 47 2.4.2 Point Cloud Pre-Processing Point cloud pre-processing involved several key steps, including noise filtering, ground filtering, normalization, and point cloud thinning, as illustrated in Figure 1.6. An accurate definition of terrain is crucial when deriving structure metrics, particularly for lower-stature vegetation. To identify ground points in the digital aerial photogrammetry (DAP) point clouds, I employed a conservative approach using triangulated irregular network (TIN) densification. This workflow aligns with the methodology proposed by Isenburg (2012). In essence, this method constructs a sparse triangular surface model based on the lowest points within a specified search window. Subsequently, spikes are removed, and points are iteratively added based on specific criteria, such as maximum distance from the surface and angle. The parameters used were step (5 m), spike (50 cm), offset (10 cm), and bulge (0â¦). To further improve ground point filtering two additional steps were implemented. The first step (prior to TIN) included pre-filtering points using the excessive greenness index (ExGI) removing the points with an ExGI value above the 35th percentile. Following TIN, ground points were refined using iterative surface lowering (ISL) to eliminate points near bulges that could potentially represent vegetation. In summary, ISL utilizes the ground points from TIN densification and iteratively generates a digital terrain model (DTM) through Delaunay triangulation, followed by the removal of points located above the DTM (Anders et al. 2019). Consistent point densities are required to derive point-based metrics of vegetation structure. Point densities vary between data acquisitions, as shown in Table 2.6, but also within each site depending on vegetation cover. Therefore, all point clouds were thinned after normalization, keeping only the 48 highest point per 4Ã4 cm2. Following normalization and thinning, point clouds were tessellated using a 1-m grid. To avoid the effects of branches extending into the measurement areas from vegetation in surrounding grids, a noise reduction step was executed at the grid scale. This step was guided by a series of trial-and-error experiments, employing a tool developed by Isenburg (2012) which identifies isolated points using a 3D grid. Spectral vegetation indices were derived from the RGB and multispectral orthomosaics for Chapter 4 and aggregated using a 2x2-m2 grid (deriving mean and standard deviations). Pixels containing any markings (i.e., spray paint) applied during field sampling were manually removed. Grid cells containing vegetation taller than 2 m were ignored. Aside from radiometric correction, no adjustments were made to the orthomosaics for atmospheric effects. The rationale behind this approach is that, by exclusively utilizing vegetation indices in Chapter 4, the impact of differences in illumination and camera characteristics (e.g., dynamic range) is mitigated to some extent (Matsushita et al. 2007; Bahadur K.C. 2009). However, a limitation is that reflectance may not be equally affected across different wavelengths. Lastly, for modelling purposes (Chapter 4) local shading maps were generated using digital surface models derived from the normalized point clouds and known location of the sun at various times of the day (9 AM, noon, and 3 PM) for multiple days throughout the core growing season (June, July, and August) following methodologies from Morgan-Wall (2023). These insolation surfaces were aggregated using a 2x2-m2 grid. 49 Chapter 3: Monitoring the Structure of Regenerating Vegetation using Drone-Based Digital Aerial Photogrammetry1 3.1 Background and Motivation In terrestrial ecosystems, quantitative indicators that capture vegetation structure are commonly used, e.g., cover by vertical stratum, cover by life form, and canopy height (Gibbons and Freudenberger 2006). Vegetation structure is relatively static, efficient, and rapid to measure, and has been associated with successional stages (Gibbons and Freudenberger 2006). It can also be used as an indirect indicator for processes such as controlling erosion or improving water quality (Prach et al. 2019). Moreover, the spatial arrangement of vegetation structure can be associated with topography and ecological processes (DâUrban Jackson et al. 2020), and provide insights into recovery patterns (Levin 1992). Measuring forest structure, however, especially of short-stature vegetation over large areas that have undergone disturbance, is difficult and constrained by resources (e.g., time, costs, and expertise). Traditional field sampling is typically undertaken manually using quadrats positioned with Global Navigation Satellite Systems (GNSS) which is demanding on project resources especially when large, remote, or hazardous areas need to be covered (Anderson and Gaston 2013). In addition, it is challenging to define spatial, temporal, and thematic scales at which monitoring is carried out, resulting in trade-offs and indicators not being well-aligned with restoration objectives as well as processes of succession (Levin 1992; Wheatley and Johnson 2009). 1 Nuijten, R. J. G., Coops, N. C., Watson, C., & Theberge, D. (2021). Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry. Remote Sensing, 13:1942â1965. https://doi.org/10.3390/rs13101942 50 Digital aerial photogrammetry (DAP) can provide information about vegetation structure at reduced costs, more frequent intervals, and higher spatial resolutions compared to airborne and spaceborne remote sensing platforms. The advantages of DAP reconstructions are their high level-of-detail and the ability to incorporate spectral information (Cunliffe et al. 2016; van Iersel et al. 2018a; Quirós Vargas et al. 2019). Others have shown that DAP is capable of characterizing the structure of small plants (James and Robson 2014; Zahawi et al. 2015; Cunliffe et al. 2016; Chen et al. 2017; van Iersel et al. 2018a); however, this has only been explored in relatively homogeneous or open environments. 3.1.1 Study Objectives In this study, I examine the capacity of DAP-based vegetation structure metrics to map classes of regenerating vegetation structure to inform ecological management and restoration. DAP data were acquired on three natural gas pipeline rights-of-ways (ROWs) in northwest Alberta, Canada. First, DAP-derived vegetation heights were compared to in situ heights of various vegetation life forms (depicted in Figure 3.1). Second, vegetation structure metrics were calculated over a regularized surface over the ROW, representing vegetation cover, vegetation height, height distribution, variability, and surface complexity. Classes of structure were then developed using a two-stage clustering analysis and patterns of structure were evaluated across sites using indicators of local spatial associations. Understanding the capacity of rapidly deployed DAP datasets to characterize short-stature vegetation structure (i.e., < 2 m) within environments with high complexity and variability of vegetation cover is important, as timely mitigation measures are 51 desired within these areas of ongoing anthropogenic disturbance. Similarly, the developed workflow aims to inform users of these data of potential in these applications. 3.2 Materials and Methods 3.2.1 Data and Study Sites This chapter only uses data from the 2019 field campaign conducted on pipeline ROWs, as described in Section 2.3. 3.2.2 Point Cloud Processing Following drone data collection, processing comprised various key steps: structure-from-motion photogrammetry, ground detection/filtering, height normalization, point cloud thinning, tessellation, and noise filtering as described in Section 2.4. 52 Figure 3.1 Methodological flow diagram of data pre-processing, initial analysis (i.e., DAP height assessment), and cluster analysis. 3.2.3 DAP Height Assessment To confirm that short-stature vegetation heights can be accurately reconstructed in the DAP point clouds, I investigated the relationship between in situ and DAP height, which was assessed by life form. First, the in situ heights (measured in 1x1- m2 quadrats) were summarized into 2x2 m2 cells to reduce positioning errors. Second, for each 2-m cell the maximum DAP and in situ height were derived, stratified by life form, and compared using regression and Spearman correlation coefficients. In addition, mean height offsets (i.e., bias), root-mean-square errors (RMSEs), and normalized RMSEs, were calculated as described by Chen et al. (2017) 53 3.2.4 Structure Metrics Previous analysis of vegetation structure using DAP datasets has incorporated metrics describing the 3D point distribution, which are related to vegetation height, height variability, and canopy cover (White et al. 2015; Goodbody et al. 2016). Most of these metrics have been shown to be representative of forest structure at the stand level, however, their applicability at finer spatial resolutions and for short-stature vegetation is less well demonstrated. I summarized the DAP point clouds using a hexagonal tessellation, with each hexagon being 1 m2, on the assumption that hexagons better represent natural vegetation features compared to a grid. The metrics derived within each 1 m2 hexagon are listed in Table 3.1 and were selected to describe key characteristics of vegetation structure. High multicollinearity between height metrics, as well as reduced dispersion in height variability between different short vegetation types was anticipated. I also computed metrics of canopy surface complexity, the Rumple index, which represents the area of a canopy surface (estimated using Delaunay triangulation) divided by the area of a projected flat surface (Jenness 2004). Metrics of point density, i.e., vegetation cover, by vertical height strata and variability were also calculated (Guo et al. 2017). All metrics were tested for multicollinearity and removed from analysis if inter-correlation (r) > 0.8. Selected metrics include maximum height (Max), coefficient of variation (COV), Rumple index, percentage points between 2.5 and 25 cm (%P2.5_25), and total vegetation cover (%P25_200). Maximum height was preferred over percentile heights as it retains treetops. Principal Component Analysis (PCA) was performed on the initial hexagon-based summaries to analyze the distribution of variance across the selected metrics and assess their contribution to variance (Wold et al. 1987). 54 Table 3.1 DAP metrics, descriptions, and categories. Metrics selected for cluster analysis are indicated with an asterisk (*). Metric Description Category Pn nth percentile point height. Height Mean Mean point height. Height Max* Maximum point height. Height SD Standard deviation of point heights. Height Variability Skew, Kurt Skewness and kurtosis of point heights. Height Variability COV* Coefficient of variation is the ratio of standard deviation to mean height. Height Variability RI* Rumple index is the ratio of canopy surface area, calculated using Delaunay triangulation, to projected ground area. Surface Complexity %P_below2.5, %P2.5_25*, %P25_50, %P50_75 % Points, i.e., density, within lower height strata. For example, between 2.5 and 25 cm. Cover %P25_200* % Points, i.e., density, between 25 and 200 cm representing total vegetation cover. Cover 3.2.5 Two-Step Clustering Analysis I clustered the selected metrics over the landscape to produce a classification of vegetation structure to inform restoration activities. Two-step clustering approaches have been shown to be highly effective on large remotely sensed datasets (Chiu et al. 2001; Tamura et al. 2014; Perles et al. 2014), improving scalability and reducing computing times compared to conventional multi-variate clustering by incorporating a pre-clustering method, in my case, the k-means++ algorithm (Tamura et al. 2014). Subsequently, agglomerative hierarchical clustering was performed to group the pre-clusters using the Ward linkage algorithm and Euclidian distance (Ward 1963). From the initial pre-clusters, an optimal number of clusters was defined by the gap statistic which compares 55 total intra-cluster variation with a hypothetical distribution showing no natural grouping. The optimal number of clusters (k) is where the gap statistic is maximized but k is minimized. To assess the uniqueness of the derived clusters, I performed Dunnâs post hoc multiple pairwise comparisons that evaluate, for each metric, whether clusters are significantly different (Kruskal and Wallis 1952; Dunn 1964). In addition, exploratory data analysis through visualization was used to assess cluster characteristics, which included: a heatmap and dendrogram organized by hierarchical relations, boxplots of metrics, and cross-sections of the point clouds. Based on these assessments, similar clusters were merged to result in a final classification of structure. To interpret the indicative vegetation features and types in each cluster, such as a dominant life form, level of regeneration, or dominant species, I overlaid and assessed the seven transects. 3.2.6 Cover and Spatial Arrangement Lastly, to demonstrate how class cover and spatial arrangement can be used to evaluate the state of regeneration, I summarized and mapped measures of local spatial association of the structure classes. To do so I utilized a novel entropy-based local indicator of spatial association (ELSA), introduced by Naimi et al. (2019), that can be applied to categorical data. I used a search radius of 3.3 m. 3.3 Results 3.3.1 DAP Height Assessment The relationship between maximum in situ and DAP height was investigated by life form (conifer, deciduous, graminoid, and perennial) using transect-based cells. Figure 3.2 shows strong 56 correlations (rs > 0.75) for all lifeforms, except for graminoid species (rs = 0.26) with p-values < 0.05. However, DAP heights were consistently underestimated, expressed by large mean height offsets, ranging from 35.6 cm for perennials and 54.4 cm for conifers. According to the slope of the linear regression lines, height offsets of deciduous vegetation samples are consistent throughout all height strata, while offsets of samples representative of the other three life forms are greater for short vegetation (< 75 cm) than tall vegetation. Offsets between DAP and in situ height were consistent across study sites ranging from an average offset of 44.1 cm for site Chinchaga B to 47.0 cm for site Chinchaga A. Figure 3.2 Comparison of maximum digital aerial photogrammetry (DAP) and maximum in situ height samples by life form: (A) conifers, (B) deciduous (incl. shrubs), (C) graminoids, and (D) perennials. Graphs show the number of samples, regression lines (with 95% confidence interval), Spearman correlation coefficients (rs), mean height offsets (Å), root mean square errors (RMSE), and normalized RMSE. All p-values < 0.05. 3.3.2 Clustering Analysis I extracted structure metrics, examined multicollinearity, and performed a clustering analysis including k-means++ pre-clustering and agglomerative hierarchal clustering. Table 3.2 shows cross-correlations of the five selected structure metrics. Correlations are typically much lower than 57 rp = 0.8. Max and RI (rp = 0.80) and Max and %P25_200 (rp = 0.78) are the exception. Again, all p-values were < 0.05. Table 3.2 Pearson correlation coefficients (rp) across the five selected structure metrics, based on 1 m2 hexagons. All p < 0.05. Metrics and associated abbreviations and descriptions are listed in Table 3.1. Max RI COV %P2.5_25 %P25_200 Max 1.0 RI 0.80 1.0 COV -0.38 -0.20 1.0 %P2.5_25 -0.20 -0.15 -0.44 1.0 %P25_200 0.78 0.52 -0.54 -0.39 1.0 Figure 3.3 illustrates the optimal number of clusters (k) selected based on 325 pre-clusters was nine, which is where intra-cluster variation is maximized while a small number of clusters is maintained. Sharp increases of the gap statistic were found up to a k of seven. The dendrogram in Figure 3.4 shows the similarity between pre-clusters according to a distance matrix and agglomerative hierarchical clustering. The dendrogram consists of stacked branches that partition the pre-clusters into more homogeneous clusters. This means, for instance, that clusters one and eight were more similar than clusters eight and four. Furthermore, the heatmap in Figure 3.4 shows the structure metrics for the pre-clusters, all scaled from zero to one. The metrics facilitated cluster characterization and interpretation, for example, cluster nine could be distinguished based on %P25_200 (close to 1) and Max (between 0.6 and 1). Note that metrics of a few clusters showed more similarity with distant clusters than with neighboring clusters, for example, clusters three with eight. 58 Figure 3.3 Line graph of the gap statistic by number of clusters (k) based on agglomerative hierarchical clustering and 325 pre-clusters. The optimal (k) is found at nine. Figure 3.4 Dendrogram and associated heatmap showing structure metrics scaled from zero to one. They are based on 325 k-means pre-clusters and agglomerative hierarchical clustering. The nine final clusters are color-coded. 59 Principal components 1 and 2 described 54% and 28% of the total variance in the dataset (Figure 3.5). The direction of the vectors, or loadings, indicates how metrics drive the separability of the clusters. COV and %P2.5_25 were most influential in terms of discriminatory power among clusters and drove separability among clusters associated with relatively short-stature vegetation and homogenous canopy heights. The stronger correlated metrics, which included Max, RI, and %P25_200, drove separability of the clusters with larger vegetation and more heterogeneous structures. In more detail, COV drove the separability of cluster two and six, and %P2.5_25 drove the separability of cluster three, seven, and eight. Cluster three and eight, which show high overlap along the vector associated with %P2.5_25 in Figure 3.5, could be separated from each other based on Max, RI, and %P25_200. 60 Figure 3.5 Distance bi-plot of the first two principal components (PCâs) based on hexagons and associated structure metrics. The plot includes percentages of explained variance by PC as well as the strength and direction of each structure metric. The nine final clusters are color-coded. Figure 3.6 compares the structure metrics across nine clusters. Generally, three groupings of clusters can be discerned, principally based on maximum height. The first group, consisting of cluster one and nine, had a standardized height around 0.75 (~150 cm). The second group, including cluster four and five, was approximately 0.37 (~80 cm) in height, and the remaining 61 clusters shorter than 0.2 (~40 cm). The first group also showed relatively high RI values and was representative of relatively tall vegetation and complex canopy structures. The second group showed relatively high %P25_200 values and was characteristic of mediumâtall vegetation and high canopy cover. In addition, RI and COV indicated a relatively smooth canopy for clusters in the second group. The third group was characterized by relatively low or no canopy cover, indicated by %P2.5_25 and %P25_200, and a smooth to extremely smooth canopy surface, indicated by RI and COV. Within the three groups, I found subtle differences across metrics related to canopy cover and height variability. In the first and second groups, one of the clusters was characteristic of a more closed and flatter canopy, clusters nine and four, respectively, while their counterparts had a more open canopy. Differences between clusters in the third group were small, although the clusters were still significantly different (p < 0.01). I found clusters characterized by the absence of vegetation structure, including cluster two and six, as well as clusters with very short vegetation (three, seven, and eight). Clusters in the third group can be characterized in more detail with the height and cover between 2.5 and 25 cm greater for cluster six compared to two, likely indicating some presence of very short-stature vegetation. Clusters three, seven, and eight all had high total canopy cover, but cluster seven stood out as %P2.5_25 was close to 1, which is representative of dense, very short vegetation. 62 Figure 3.6 Boxplots showing the five structure metrics used during two-step clustering analysis by cluster. The letters indicate whether clusters are significantly different according to Dunnâs post hoc test. All p-values < 0.01. 3.3.3 Final Structure Classes I merged the clusters into final classes based on the structure metrics, point-cloud visualizations, and field observations. The final classes, including structural descriptions and indicative life forms, are summarized in Table 3.3, and representative point clouds are illustrated in Figure 3.7 using cross-sections. Relatively tall and complex vegetation was represented by classes one and two. Field observations indicated the presence of woody vegetation, predominantly willow (Salix spp.) shrubs, was characteristic of class one. Single occurrences of aspen poplar and lodgepole pine were also found. Class two was characterized by tall (> 50 cm) graminoids and perennials including yellow sweet clover and white clover. Moreover, a few noxious weed species were recorded in the in situ transects overlapping with class two. The difference in canopy height heterogeneity between underlying clusters, described in the previous paragraph, was related to the inclusion of more exposed soil and short vegetation, e.g., graminoids. Figure 3.7 illustrates that clusters one and five were found on boundaries of vegetation patches or include single isolated plants. 63 Classes three to six were characteristic of short-stature vegetation with homogenous canopy height and vegetation structure absent. Class three, which is based on two merged clusters, was characterized by the presence of graminoid species and short perennials such as white clover. The cross-sections in Figure 3.7 illustrate strong similarity among classes four, five, and six, all of which characterized the absence of vegetation structure, however, differences exist. Class four is absent of vegetation structures and characterized by exposed soil, mosses, and short grass. Class five includes mosses, short grasses, and small seedlings (approximately 30 to 40 cm). Class six is representative of short graminoids and perennials, that sometimes grew together with small seedlings, including aspen, black spruce, and willow. Table 3.3 Final structure classes with corresponding descriptions of vegetation structure and observed life forms, based on the structure metrics, point-cloud visualizations, and field observations. Class Cluster(s) Structural Description Observed Life Forms 1 1 and 9 Tall structures and most complex canopy Woody (e.g., willows) 2 4 and 5 Mid-tall and moderate complexity Tall graminoids and forbs 3 3 and 8 Short and moderate complexity Short grasses and forbs (e.g., clovers) 4 2 No structures and no complexity Short grasses, moss, and bare soil 5 6 No structures and minimal complexity Short grasses, moss, and conifer seedlings 6 7 Very short and lack of complexity Short grasses, forbs, and conifer seedlings 7 n/a Structures >2m were excluded n/a 3.3.4 Land Cover and Spatial Arrangement Figure 3.8 (left) shows the proportions of structural classes by site. Total cover by relatively large and complex structures, presumably woody vegetation, was highest at site Chinchaga A (Lower 64 Boreal Highlands) with 35.2% followed by site Little Smoky (Upper Foothills) and Chinchaga B (Lower Boreal Highlands) with 27.6% and 13.9%, respectively. Site Chinchaga A was mostly covered by young woody vegetation shorter than 1.5 m (class one; 22%) followed by short as well as mid-tall vegetation structures representing graminoids and perennials (class two and three; 37.1%). In contrast, site Little Smoky had a high cover of developed woody vegetation (class seven; 19.6%) and absent or near-absent vegetation structures (class four and five; 40.5%) but relatively low cover by classes representative of short graminoids and perennials (classes two, three, and six; 31.8%) and young woody vegetation (class two; 8%). Cover percentages found at site Chinchaga B were similar to site Little Smoky except for the cover by short dense vegetation structures (class six; 20.1%), being more than twice as high as the developed woody vegetation cover (class seven; 7.5%). 65 Figure 3.7 Point-cloud cross-sections by cluster and class, including orthomosaic and classified hexagons. The hexagons are colored according to the final classes described in Table 3.3. The cross-sections were 0.5 x 5 m2. 66 Patch size distributions of the structural classes are shown in Figure 3.8 (right) and indicate that across all sites the largest mean patch size occurred for class seven, ranging from 10.6 m2 for site Chinchaga A to 26.3 m2 for site Little Smoky. At site Chinchaga A, the second largest mean patch size was for class one, representing young regenerating woody vegetation. At sites Chinchaga B and Little Smoky, there were also a number of areas of class four with vegetation structures absent with mean patch sizes of 8.3 and 13.7 m2, respectively. In addition, relatively large patches with very short vegetation structures and homogenous canopy heights, representative of class six, were found at site Chinchaga B. The mean patch size of class six at this site was 6.1 m2. The remaining classes at all sites had mean patch sizes ranging from 2.4 to 4.3 m2. Figure 3.8 Barplots of (a) land cover percentages and (b) mean patch sizes (m2) by structure class and site. Study site characteristics for (A) Chinchaga A, (B) Chinchaga B, and (C) Litle Smoky are summarized in Table 2.1 and class descriptions in Table 3.3. 67 Figure 3.9 shows maps of the structural vegetation classes across the three sites based on the classified 1-m2 hexagons. Multiple adjacent ROWs, including an approximately 30-m-wide ROW of a pipeline constructed in 2009 and a 10-m-wide ROW, were observed at site Chinchaga A with varying compositions of vegetation structure. The narrow ROW was predominantly covered by large patches with tall and complex vegetation structures representative of classes one and seven, with an anticipated trajectory towards a mature forest. A small section with mid-to-tall vegetation representative of class two, presumably tall graminoids and perennials, was found in the east section. A complex mosaic of structure classes was found on the wide ROW. Relatively large patches with regenerating woody vegetation representative of class one were observed that connected both sides of the ROW at three sections. Patches representative of very short but dense vegetation structures, characterized by class six, connected both sides of the ROW at the east section. Long, narrow continuous patches representative of class two, characterizing tall graminoids and perennials, were found in the middle of the wide ROW. Site Chinchaga B included an approximately 75-m wide ROW on which multiple parallel sections with distinctive vegetation structures were found. The southwestern half of the ROW, which included a power line, had large amounts of class four (vegetation structure absent) and six (very short but dense vegetation structures). This half was largely separated by vegetation representative of class seven. The northeastern section, which includes three pipelines, included long, narrow stretches of young regenerating woody vegetation representative of class one. Stretches of tall perennials and graminoids representative of class two were also observed. Lastly, a few relatively large patches of class four were found adjacent to the forest edge. 68 At site Little Smoky, which included an approximately 26-m-wide ROW of a single pipeline, two major areas were observed with a high cover of predominantly a single structure class. A large continuous area with vegetation representative of class seven was found in the western section (left), while vegetation characteristic of class four (structures absent) was predominantly found in the eastern section (right). Figure 3.9 Land cover maps of the three study sites, including (A) Chinchaga A, (B) Chinchaga B, and (C) Little Smoky, showing the final structure classes. Class descriptions are summarized in Table 3.3. 69 Measures of local spatial association between structure classes by site, using entropy-based local spatial association (ELSA) scores ranging from zero to one are shown in Figure 3.10. At site Chinchaga A, relatively low ELSA scores were found for class one and seven indicating both classes occur generally in patches, having a median of 0.47 and 0.25, respectively. Class four, which is closely followed by class five, had the highest scores, indicating hexagons representative of absent or almost-absent vegetation structures were generally randomly distributed at site Chinchaga A. Contrarily, at sites Chinchaga B and Little Smoky, class four showed relatively low ELSA scores of 0.37 and 0.2, respectively, indicating hexagons with vegetation structures absent typically occurring in patches. At site Chinchaga B, young woody vegetation (class one) and short graminoids and perennials (class three) show the same level of random distribution, both having a median ELSA score of approximately 0.55. At site Little Smoky, classes three and six had the highest scores with medians around 0.5. Figure 3.10 Entropy-based local spatial association (ELSA) scores by structure class and site, including (A) Chinchaga A, (B) Chinchaga B, and (C) Litle Smoky. ELSA scores are based on individual 1 m2 hexagons and a search radius of 3.3 m. The letters indicate whether clusters are significantly different according to Dunnâs post hoc test. All p-values < 0.01. 70 Figure 3.11 shows maps of the three study sites indicating the level of local spatial association based on all classified hexagons. At site Chinchaga A, relatively low ELSA scores were found at multiple locations on the narrow ROW. The wide ROW showed generally high ELSA scores but included some sections with high patchiness. In the west section (left), patches were representative of regenerating or more developed woody vegetation. In the east section (right), patches were found of class six, representative of very short but dense vegetation structures, that largely formed a connection between the ROWâs boundaries. At site Chinchaga B, multiple parallel stretches, as well as large sections adjacent to the ROWâs boundaries, had low ELSA scores. At the southwestern half of the ROW (bottom), areas of bare ground surface were highly patchy, becoming more random towards the eastern (right) locations. Lastly, at site Little Smoky, two extensive sections with high patchiness were present in the southwest (left) and northeast (right). 71 Figure 3.11 Maps of the three study sites, including (A) Chinchaga A, (B) Chinchaga B, and (C) Litle Smoky, showing entropy-based local spatial association (ELSA) scores. The scores are based on all classified 1 m2 hexagons and a local search radius of 3.3 m. Zero indicates a random distribution was not present and one indicates a random distribution. 3.4 Discussion The past five years have seen considerable progress on the application of DAP for characterizing short vegetation structures for monitoring, however, research has principally been focused on DAP 72 height measurement or mapping of structures in sparsely vegetated landscapes such as savannas (Cunliffe et al. 2016; Chen et al. 2017; van Iersel et al. 2018a). This study focused on areas of on-going anthropogenic disturbance in temperate coniferous and boreal forest ecoregions which are generally densely vegetated and include heterogeneous mosaics of early successional vegetation types. The incorporation of metrics derived over a regularized surface, describing vegetation height, cover, variability, and surface complexity, allowed a more detailed characterization of vegetation patterns than studies that simply utilize the canopy height model such as Cunliffe et al. (2016). Concurrently, innovations in drone design are resulting in systems with longer flight range and speed, improved autopilot and safety features, and greater maximum payloads (Crutsinger et al. 2016; Pádua et al. 2017). Furthermore, drones are becoming more user-friendly due to plug-and-play sensor systems, improved smartphone-operated mission planning software, and cloud processing (Crutsinger et al. 2016). New drone-specific aviation legislation and streamlined permit systems have been implemented by Canadian, American, and, as of 2021, European aviation safety agencies, making it easier and safer for organizations to start deploying drones (European Union Aviation Safety Agency 2020; Transport Canada 2020). In my study, DAP heights were underestimated and show reduced height variability, which is in line with findings of Chen et al. (2017). This can, in part, be explained by height error introduced during data collection, DAP processing, or ground filtering, being close to short-stature vegetation height. Observed RMSE values ranged from 38â58 cm for various life forms, while Chen et al. (2017) found values in the range of 20â46 cm for two height strata (0â0.5 m and 0.51â2.0 m) at 73 comparable sites with respect to forest type, disturbance type, and successional stage. Different flight parameters could have caused such a discrepancy. An investigation by Tu et al. (2020) found that small image pitch angle, high solar elevation, and low flight altitudes ideally between 25 and 50 m (while retaining high image overlap) improved structure measurements of tree crops. I acknowledge that high flight altitudes (90 to 180 m), chosen to comply with visual line-of-sight requirements and limit data acquisition time, may have reduced data quality in my study. In addition, image pitch angles from the DJI Phantom 4 acquired imagery were highly variable. High bidirectional reflectance was observed above exposed soil at ROW sites Chinchaga A and B and heavy shadowing was found at site Little Smoky. My study suggests that errors in vegetation height can partially be explained by vegetation characteristics. The large height offsets found for conifers suggest that DAP has difficulties resolving seedlings that ideally stand out more clearly and form an unambiguous structure class. This could be caused by a combination of flight altitude, illumination conditions, and, as a result, issues with the identification of treetops during image matching. Moreover, automated ground filtering can become problematic in densely vegetated areas (Zahawi et al. 2015; Graham et al. 2019) which could explain the large normalized RMSE found for graminoids. Novel flight protocols including oblique imagery such as those developed by James and Robson (2014) and Cunliffe et al. (2016) can produce very detailed reconstructions of graminoid and shrub canopies as well as terrain, however, this will require multiple consecutive flights. I recommend further investigation of optimal flight parameters for short-stature vegetation, which should also consider battery performance and flight time. 74 Despite these technological issues, direct measurements of canopy attributes allowed us to distinguish three broad groupings of structure associated with young woody vegetation, large perennials, and graminoids as well as remaining short-stature vegetation. Subtle differences in metrics describing height variability and canopy cover allow for the classification of the shortest vegetation structures representative of meadow grasses, seedlings, short shrubby, and herbaceous perennials. Previously, Guo et al. (2017) demonstrated that such metrics enabled the characterization of forest stand structure from LiDAR. However, early successional vegetation shows much less layering along the vertical dimension but higher structural variation along the horizontal dimension compared to mature forests, which may explain the success of comparable metrics for the classification of short vegetation structures from DAP. Note that DAP typically has a higher spatial resolution in comparison to airborne LiDAR point clouds but is limited to characterization of the top canopy. Hexagon-based clustering analysis using ELSA provided maps of vegetation structures at relevant spatial and thematic scales for ecologists and restoration professionals, requiring limited field sampling for validation and interpretation purposes. Traditional studies based on field sampling are constrained by time, costs, and resources, which may result in arbitrarily chosen scales of observation, not well-matched with scales of ecological processes or environmental variables (Levin 1992; Wheatley and Johnson 2009). Such risks are now partially averted, as underlying environmental variables and processes can be worked out after data acquisition based on shape, location, and patterns of patches of particular structure classes. 75 Some unambiguous restoration objectives are analogous to vegetation structure (Prach et al. 2019). Such objectives, including soil stabilization and undesirable species control, can be monitored using combined measures of structural cover, patch size, and spatial arrangement. Areas of potential erosion could be identified based on cover without structures in combination with associated levels of patchiness as illustrated by Figure 3.9. Mapped shapes and patterns can support restoration professionals by describing and identifying underlying factors such as existing erosion, terrain wetness, seedbank issues, or soil conditions. In addition, where erosion control includes willow or shrub staking (Barker 2018), vegetation establishment can potentially be tracked using the developed tools. One of the shortcomings of my approach is that other ecosystem attributes like species composition and invasive species cannot be determined. However, restoration professionals may find that specific structural vegetation classes are more likely to represent undesirable species or, if showing high patchiness, limit successional development towards a treed environment. For instance, they may decide to focus on further inspecting patches with structures representative of tall grasses-forbs if past field observations suggest that important invasives are often found in this structure class. Future studies should investigate the potential for the application of off-the-shelf multispectral and hyperspectral drone-based sensors for improving effective discrimination between plant communities. Where a structural or spectral dataset alone may not be able to resolve the boundaries of a plant community, a combination of the two may improve discrimination as demonstrated by Cao et al. (2018). In addition, it is important to investigate the consistency of the derived information under varying flight parameters and develop novel flight protocols considering simplicity, cost-effectiveness, and quality of derived information. Considering that there is a 76 growing push for the restoration and assessment of sites impacted by resource extraction, as well as interest in monitoring of present-day reference sites within the ecological restoration community (Gann et al. 2019), I suggest that drones will play an increasingly important role in forest restoration projects in the future. 3.5 Conclusions My approach demonstrates how drone-based DAP can be used to characterize short-stature vegetation structures on sites displaying a complex mosaic of vegetation types typical for temperate and boreal coniferous forests in Alberta, Canada. Extraction of classes representative of mid-to-tall vegetation structures, such as young woody vegetation, tall graminoids, and perennials, is relatively straightforward based on metrics describing canopy height and surface complexity, while more carefully selected metrics describing canopy cover and height variability are necessary to obtain classes of most short-stature vegetation structures. The spatial arrangement of mapped structures can improve the understanding of vegetation patterns, which could help to describe the driving environmental variables and processes of ecological succession within the landscape. In return, this could support the evaluation in relation to restoration objectives, as well as prioritization of mitigation measures and future monitoring. I anticipate that optimized flight protocols, optimal weather conditions, and enhanced sensors can further improve the capability of DAP to characterize early regenerative vegetation structures. 77 Chapter 4: Estimation of Fine-Scale Vegetation Distribution Information from Drone-Generated Imagery and Structure to Aid Restoration Monitoring2 4.1 Background and Motivation Detailed maps of vegetation are critical to facilitate restoration planning, implementation, and monitoring (Robinson et al. 2022). Broad-scale mapping of plant functional types (i.e., forests, grassland, marshes, etc.) using remote sensing is possible due to correlations between reflectance and functional traits including pigmentation, canopy structure, as well as leaf water and nitrogen content (He et al. 2015). Vegetation distribution models may include remotely sensed metrics describing habitat suitability such as spatial climate variables and topographic features derived from, for example, NASAâs Soil Moisture Active Passive (SMAP) mission and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation data (Entekhabi et al. 2009; Fujisada et al. 2011) in addition to spectral information. However, ecological restoration generally requires information at high spatial and thematic resolutions including presence-absence or abundance maps of a taxonomic group (e.g., species, genus or family), maps of plant assemblages based on ecological function (e.g., nutrient cycling, height stratum, or fuel load), as well as plant productivity (Robinson et al. 2022). Using traditional remote-sensing platforms (i.e., piloted airborne and spaceborne) such information can be difficult to acquire, particularly in areas with heterogeneous mosaics of early successional vegetation types and high species richness. 2 Nuijten, R. J. G., Coops, N. C., Theberge, D., & Prescott, C. E. (2024). Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring. Science of Remote Sensing, 9:100114. https://doi.org/10.1016/j.srs.2023.100114 78 A growing body of literature utilizes digital aerial photogrammetry (DAP) data products to detect plant functional types, or even taxa, in heterogeneous environments with high species richness, such as grasslands and early successional forests. For example, conifer seedlings have successfully been detected using RGB-derived orthomosaics and deep learning approaches in regenerating boreal forests in Alberta, Canada (Fromm et al. 2019; Chadwick et al. 2020). Alexandridis et al. (2017) mapped the distribution of milk thistle (Silybum marianum) on abandoned farmland colonized by various grass and forb species with an overall accuracy of 96%. Other applications mapping various plant functional types, generally 3â7 types, are more common (Cruzan et al. 2016; Cunliffe et al. 2016; van Iersel et al. 2018b; Cao et al. 2018; Villoslada et al. 2020). According to Bradter et al. (2020), classifications of functional types yield higher accuracies than those of dominant species using 13-band multispectral imagery. This is attributed to the ability of remote sensing data to detect not only the plant species present but also the site conditions (e.g., plant co-occurrence and exposed soil areas). However, Cao et al. (2018) classified various dominant mangrove species with an overall accuracy of 88.7%, including three shrub and one swamp fern species, using drone-derived digital surface models and hyperspectral imagery. 4.1.1 Study Objectives For vegetation maps to be relevant for ecological restoration practitioners, high thematic resolution is essential, particularly for baseline surveys in restoration sites and complex early seral forests, known for their species richness and heterogeneous vegetation mosaics. The challenge in these environments lies in creating detailed thematic classes from remote-sensing spectral reflectance data, due to similar spectral signatures across species and the large number of potential classes present (e.g., numerous plant species and visible conditions like exposed soil). Drones with 79 integrated multispectral cameras currently stand out as one of the most adaptable and practical remote-sensing technologies for this purpose. However, optimized use of data provided by DAP approaches, in conjunction with field data, is necessary to improve class separability. This chapter explores vegetation mapping using drone-based DAP data at two aggregation levels: (1) broader plant functional types, and (2) key plant groups at the genus or family level. My study investigates which aggregation level yields the most accurate results and whether the number of vegetation classes derived from such drone photogrammetric data can be expanded beyond previous studies. A novel workflow is developed, incorporating drone-based multispectral orthomosaics and layers describing vegetation structure and microclimate (i.e., local ground illumination conditions), provided by DAP approaches, to potentially enhance class separability. Additionally, the study assesses the importance of spectral variation in the presence-absence models. After developing and testing the models, I evaluate the utility of resulting maps for assessing restoration sites in comparison to wildfire-disturbed sites, which served as natural analogues, to inform adaptive management strategies. 4.2 Materials and Methods Figure 4.1 demonstrates my overall workflow, which included (1) data collection and pre-processing, (2) evaluation of logistic regression models predicting presence-absence of various plant functional types and key plant groups at lower taxonomic levels, and (3) model deployment across study sites and implementation of a novel mapping routine based on probability scores. 80 Figure 4.1 Methodological flow diagram of data collection and pre-processing, the development of various binary presence-absence models, as well as mapping of plant composition. 4.2.1 Data and Study Sites The sampling data set contained 98 taxa, sampled at the lowest possible taxonomic level, of which 27 taxa were found in at least 20 quadrats. I refined the list of modelled species based on their abundance in the site set, their common usage in restorative activities such as willow (Salix spp) and spruce species (Picea spp), and their ability to characterize site conditions and native plant 81 communities as described by Willoughby, Downing, and Meijer (2016) and Willoughby et al. (2018). For example, native communities on wet soils often include black spruce (Picea mariana), bog cranberry (Vaccinium vitis-idaea) and Labrador tea (Rhododendron groenlandicum), as well as peat and Sphagnum mosses (bryophytes). Native flowering forbs, of which aster species (Asteraceae spp) are a large component, are often found on slightly drier and richer sites (Willoughby et al. 2016, 2018). One of the selected groups, clover (Trifolium spp), characterizes agronomic plant communities. In the past, restorative activities merely focussed on rapid revegetation and used agronomic seed mixes. Such communities are now undesirable but remain a challenge for newly reclaimed sites due to ingress from adjacent disturbance and competitive advantages (TC Energy 2014, 2020a). In total 8 key plant groups were selected for vegetation mapping, including five at genus level, one at family level, as well as bryophytes and grasses, as shown in Table 4.1. Based on findings by Bradter et al. (2020), classifications based on plant functional types were expected to be more accurate than classifications of dominating species. Therefore, taxa were also grouped into six plant functional types including bryophytes, grasses, low-growing forbs (largely representing agronomic species), shrubs (deciduous), tall forbs, and tree seedlings (conifer). In addition, bare soil was included in the vegetation maps and treated as an additional key plant grouping and plant functional type. 82 Table 4.1 A list of assessed plant functional types and key plant groups at various taxonomic levels, which includes the number of present samples after data cleaning. The last column lists the structure classes, corresponding to descriptions found in Chapter 3, indicative of the plant functional types and key groups. No. of species included No. of present samples Key plant groups Plant func. types Indicative struc. classes Low-growing forbs 3 spp 96 - Included 3, 4, 5, 6 Shrubs >= 10 spp 171 - Included 1, 2, 3, 5, 6 Tall-growing forbs >= 2 spp 91 - Included 2, 3, 5, 6 Trees 3 spp 83 - Included 1, 2, 3, 5, 6 Bryophytes >= 4 spp 104 Included Included 3, 4, 5, 6 Grasses >= 7 spp 85 Included Included 2, 3, 5, 6 Bare soil None 19 Included Included 4 Asteraceae spp >= 2 spp 71 Included - 2, 3, 5, 6 Picea spp 2 spp 79 Included - 1, 2, 3, 5, 6 Rhododendron spp 1 spp 73 Included - 2, 3, 5, 6 Salix spp >= 1 spp 51 Included - 1, 3, 5, 6 Trifolium spp 2 spp 38 Included - 3, 4, 6 Vaccinium spp 3 spp 70 Included - 2, 3, 5, 6 All plant functional types and key plant groups were found on both ROW and wildfire sites, as shown in Figure 4.2. However, low-growing forbs and grasses were more abundant on right-of-ways, whereas bryophytes and shrubs were generally more abundant on the wildfire sites. High co-occurrence (>50 % of samples co-occurring with another grouping) was found between bryophytes and Picea spp, Rhododendron spp, as well as Vaccinium spp (Figure 4.3). In addition, bryophytes were often found together with bare soil. 83 Figure 4.2 Cover percentages by site of selected key plant groups, at various taxonomic levels (left), and plant functional types (right) based on the field sample data set. Figure 4.3 Relative co-occurrence matrices for selected key plant groups, at various taxonomic levels (left), and plant functional types (right) based on the field sample data set. 84 I used several sources of data in my analysis, which included multispectral imagery and insolation layers derived from a digital surface model. In addition, I used a fine-scale vegetation structure map of the region, which identified six structural types of regenerating vegetation and was created in Chapter 3. The map was created using an unsupervised clustering approach that incorporated various point-cloud-based height metrics, such as maximum height. These metrics were originally derived at a 1-m2 spatial resolution and then aggregated to 2x2 m2 by counting the number of classes (up to four classes per pixel) and creating binary presence-absence variables for each class. To incorporate local illumination affecting growing conditions, local shading maps were generated using digital surface models and the location of the sun (Morgan-Wall 2023). Insolation surfaces, aggregated at 2x2 m2, were then derived for every first day of the week during the core growing season (June, July, and August). 4.2.2 Predictor Variables A total of 15 predictor variables was produced at 2-m spatial resolution including 10 spectral variables as well as 5 variables based on reconstructions of vegetation structure describing structural composition and growing conditions, as listed in Table 4.2. The produced insolation surfaces were generated for 9 AM, noon and 3PM across the core growing season. Spectral variables include the normalized difference vegetation index (NDVI) which is a commonly used index related to vegetation cover and photosynthetic activity (Cruzan et al. 2016). In addition to NDVI, I selected three other multispectral-derived indices including near-infrared reflectance of vegetation (NIRv), normalized difference red edge index (NDRE) and plant senescence reflectance index (PSRI). NIRv, which is the product of NDVI and total NIR reflectance, better reflects the proportion of reflectance attributable to the vegetation in the pixel (Badgley et al. 2017). NDRE 85 incorporates the red-edge region, which may improve classification accuracies due to its sensitivity to carotenoids, chlorophylls and other pigments (Villoslada et al. 2020). Finally, PSRI is sensitive to species-specific plant phenology based on the ratio between carotenoids and leaf chlorophylls, which changes during fruiting and senescence. In addition to multispectral reflectance data, the RGB-derived normalized green ratio index (GRI) was included, which is typically used to detect phenological changes and canopy photosynthesis rate (Ide and Oguma 2010). Mean and standard deviation within the 2x2 m2 cells were derived for all spectral vegetation indices. The Pearson correlation coefficients (rp) for all variables were below 0.75, with an average rp of 0.21 across all variables and 0.36 specifically for spectral indices. The NDVI-NDRE and NDVI-PSRI index combinations exhibited highest correlations (rp â 0.75). 86 Table 4.2 The predictor variables used in the species distribution models (SDMs). For variables indicated with 1 both mean and standard deviation were calculated. Variables indicated with 2 are based on the outputs of unsupervised clustering analysis. Predictor variables Type Data source Equation Reference Green ratio index (GRI) 1 Biophysical RGB ortho. GB + G + R Ide and Oguma (2010) Near-infrared reflectance of vegetation (NIRv) 1 Biophysical Multispectral ortho. NIR â RNIR + R â NIR Badgley, Field, and Berry (2017) Normalized Difference Red Edge Index (NDRE) 1 Biophysical Multispectral ortho. NIR â RENIR + RE Gitelson and Merzlyak (1994) Normalized Difference Vegetation Index (NDVI)1 Biophysical Multispectral ortho. NIR â RNIR + R Tucker (1979) Plant Senescence Reflectance Index (PSRI)1 Biophysical Multispectral ortho. R â GNIR Merzlyak et al. (1999) Insolation 9 AM Microclimate Digital surface model (DSM) n/a Morgan-Wall (2022) Insolation 12 PM Microclimate Digital surface model (DSM) n/a Morgan-Wall (2022) Insolation 3 PM Microclimate Digital surface model (DSM) n/a Morgan-Wall (2022) Structure class count2 Structure classes Point cloud n/a Nuijten et al. (2021) Structure class occurrence2 Structure classes Point cloud n/a Nuijten et al. (2021) 4.2.3 Logistic Regression Modelling and Mapping Routine Logistic regression was used to generate plant-occurrence models, followed by a mapping routine based on probability scores. Logistic regression models have high computational efficiency, reduce the likelihood of overfitting using small data sets, and produce probability distributions which can be compared across different models (James et al. 2021). To account for class 87 imbalance, models used class weights inversely proportional to class frequencies. My implementation used the limited-memory Broyden-Fletcher-Goldfarb-Shanno method and maximum 1000 iterations (Pedregosa et al. 2011). Models were assessed using 5-fold cross validation, each randomly withholding 20 percent of the samples for independent model evaluation. Performance metrics reported in this study include precision, recall, specificity, and the F-score: ðððððððððððððððððð = ðððððððð + ð¹ð¹ðð ð ð ððððð ð ð ð ð ð = ðððððððð + ð¹ð¹ð¹ð¹ ðððððððððððððððððððððð = ððð¹ð¹ððð¹ð¹ + ð¹ð¹ðð ð¹ð¹ ðððððððððð = 2 â ðððððððððððððððððð â ð ð ððððð ð ð ð ð ð ðððððððððððððððððð + ð ð ððððð ð ð ð ð ð where FP is false positives, FN is false negatives, TP is true positives and TN is true negatives. The F-score, which ranges from 0â1, describes the modelsâ balanced ability to accurately capture actual positive cases (i.e., recall) as well as the ratio of correct positives produced by the model out of all predictions (i.e., precision). Final presence-absence models, from which predictor variable importance and wall-to-wall probability predictions were derived, were trained using all available samples. The vegetation survey data were filtered on the seven structure clusters to maintain only observations of key plant groups and plant functional types having corresponding structural classes, as illustrated in Chapter 3. Likewise, when applying the models over the entire area, presence-absence predictions were not undertaken on 2x2-m2 cells with inconsistent structure 88 classes. SHaply Additive exPlanation (SHAP), a model-agnostic tool for explaining the magnitude and direction of the variableâs global effect on model outputs, was used to calculate variable importance to understand the overall contribution of classes of vegetation structure, which were represented by seven binary predictor variables, i.e., presence-absence, as well as other predictor variables listed in Table 4.2 (Cha et al. 2021). Final maps were created based on the probability scores provided by the logistic regression models at two thematic resolutionsâplant functional types and key plant groups as listed in Table 4.1. Based on Bradter et al. (2020), it was assumed models of plant functional types would achieve higher accuracies compared to models based on plant assemblages at low taxonomic ranks (e.g., species or genus). This assumption was incorporated into the mapping routine, which verifies, for each pixel, agreement between the probabilities of âkey plant groupsâ and the associated plant functional type. In an iterative process, key plant groups sorted from highest to lowest probability are accepted based on the following criteria: (1) the associated plant functional type (e.g., shrubs) has the highest probability score amongst other functional types; and (2) the difference between probabilities of the plant functional type and the associated âkey plant groupingâ (e.g., Vaccinium spp) is smaller than 0.35. If none of the âkey plant groupsâ was selected during the first iteration, the routine was repeated using the plant functional type with the second highest probability score. The threshold value of 0.35 was selected based on empirical evaluation of mapping products in a trial-and-error process, involving high resolution orthomosaics and expert knowledge. 89 Lastly, to demonstrate how cover of key plant groups and plant functional types can be used to evaluate the state of regeneration vegetation, I produced measures of Bray-Curtis dissimilarity to quantify compositional dissimilarity between paired sites (Virtanen et al. 2020). 4.3 Results 4.3.1 Performance Presence-Absence Models In total, I developed 13 logistic regression models for plant functional types, key plant groups, and bare soil. Six of the models have an F-score larger than 0.6, including shrubs (F-score = 0.79), low-growing forbs (0.70), bryophytes (0.69), Trifolium spp (0.67), bare soil (0.64), and Rhododendron spp (0.62). Of these models, shrubs, bryophytes and low-growing forbs have highest precision scores (0.69â0.93), whereas Trifolium ssp, Vaccinium spp, shrubs, and Rhodondendron spp have highest recall (0.68 â 0.79). The poorest classification models were for grasses (F-score = 0.46), Asteraceae spp (0.48), Picea spp (0.54) and conifer tree seedlings (0.60), with lowest precision for Asteraceae spp (0.44) and lowest recall for grasses (0.46). The plant functional type models for low-growing forbs, shrubs, tall forbs, and trees have higher accuracies than the associated key plant groups at lower taxonomic levels, with an average F-score of 0.67 compared to 0.57 (Table 4.3). The number of false positives produced by the models of key plant groups is relatively high, having an average precision of 0.52 compared to 0.70 for models of plant functional types. However, specificity, which describes the proportion of the data setâs negatives correctly identified, is relatively high for most key plant groups, particularly for Trifolium spp, Salix spp and Rhodondendron spp. 90 Table 4.3 Mean and standard deviations of performance metrics derived from cross validation, including precision, recall (i.e., true positive rate), specificity (i.e., true negative rate) and f-score. Classification system Presence-absence model Precision Recall Specificity F-score Plant func. types Low-growing forbs 0.69 (0.09) 0.71 (0.13) 0.61 (0.12) 0.70 (0.10) Shrubs 0.93 (0.06) 0.70 (0.11) 0.69 (0.29) 0.79 (0.08) Tall-growing forbs 0.60 (0.10) 0.55 (0.15) 0.64 (0.14) 0.57 (0.11) Trees 0.56 (0.07) 0.65 (0.17) 0.64 (0.11) 0.60 (0.10) Used in both Bryophytes 0.73 (0.07) 0.66 (0.07) 0.60 (0.16) 0.69 (0.03) Grasses 0.48 (0.05) 0.46 (0.17) 0.59 (0.13) 0.46 (0.13) Bare soil 0.64 (0.11) 0.68 (0.32) 0.58 (0.33) 0.64 (0.21) Key plant groups (i.e. lower taxa) Asteraceae spp 0.44 (0.02) 0.54 (0.10) 0.57 (0.08) 0.48 (0.05) Picea spp 0.50 (0.13) 0.60 (0.23) 0.63 (0.10) 0.54 (0.16) Rhododendron spp 0.58 (0.19) 0.68 (0.08) 0.64 (0.19) 0.62 (0.14) Salix spp 0.49 (0.11) 0.65 (0.12) 0.72 (0.11) 0.55 (0.07) Trifolium spp 0.58 (0.15) 0.79 (0.16) 0.79 (0.10) 0.67 (0.15) Vaccinium spp 0.53 (0.08) 0.71 (0.11) 0.60 (0.08) 0.60 (0.09) 4.3.2 Variable Importance Figure 4.4 shows the predictor variables ordered by importance based on SHAP values, which demonstrates the inclusion of classes of vegetation structure was important for all models. In addition, structure-class count was of high importance for most models, except bryophytes and tall forbs. Higher class counts were driving present predictions for bare soil, tree seedlings, low-growing forbs, shrubs, Rhododendron spp, Salix spp, Vaccinium spp and Picea spp. Low average illumination values across the core growing season, at 9 AM, 12 PM and 3 PM, were driving presence of bryophytes as well as tree seedlings and Picea spp. With respect to variable importance, the standard deviation of the spectral metrics at 2-m resolution were generally more important than mean values as their combined average ranks were 7.6 and 9.4, respectively, as derived from Figure 4.4. Generally, mean NIRv and standard deviations of NDVI and NIRv were the most important spectral index-derived variables. Importance of mean 91 NIRv was ranked 5th or higher for most models, except for low-growing forbs, shrubs, tree seedlings, Trifolium spp and Picea spp. Small mean PSRI values, which is sensitive to the ratio between carotenoids and leaf chlorophylls and changes during fruiting and senescence, were partly driving present-predictions of low-growing forbs. A large mean NDVI value was associated with the presence of Trifolium spp (Figure 4.5). Generally, NDRE, which incorporates the red-edge region, and RGB-derived GRI were important for presence-absence models of herbaceous plants, which includes Asteraceae ssp, grasses, Trifolium spp, as well as low-growing and tall forbs. Key plant groups and plant functional types that were characterized by the inclusion of exposed soil and minimal vegetation structure, such as bryophytes, tree seedlings and bare soil, saw relatively high importance of the standard deviation of PSRI (Figure 4.4). SHAP dependence plots in Figure 4.6 illustrating interaction effects across a selection of predictor variables, show that interaction effects are common between vegetation indices (e.g., NDVI-PSRI) as well as between illumination variables. Figure 4.4 Variables ranked by importance for presence-absence models of the key plant groups (left), and plant functional types (right). 92 Figure 4.5 SHAP variable importance plots for all logistic regression-based presence-absence models. 93 94 95 Figure 4.6 SHAP dependence plots for the three most important variables by model. The x-axis is the value of that variable, whereas the y-axis represents the impact on the modelâs output. The colors correspond to the values of a second variable that may have an interaction with the main variable. 96 Figure 4.7 Logistic regression-based composition maps of key plant groups at right-of-way (ROW) Chinchaga A (i), ROW Chinchaga B (ii), ROW Little Smoky (iii), wildfire Chinchaga A (iv), wildfire Chinchaga B (v) and wildfire Little Smoky (vi). 97 Figure 4.8 Cover percentages for key plant groups (left) and plant functional types (right) at each site, retrieved from the final mapping product. 4.3.3 Output Mapping Routine The developed models were applied across the study sites at a 2-m resolution, as shown in Figure 4.7 and Figure 4.8. Based on these site summaries, compositional dissimilarity was assessed across ROW and post-wildfire analogue sites using BrayâCurtis dissimilarity (BC). Similarity of plant functional type cover was highest (BC = 0.28) between ROW Chinchaga A and its wildfire analogue, followed by Chinchaga B (0.49). Similarity was lowest between the ROW and the wildfire site in the Little Smoky range (0.69). Overall, the fractional differences across sites for the various classes were smallest for grasses, with total cover being 1.3â4.8 times higher compared to the wildfire sites. Fractional differences were largest for tall forbs (11.9â71.6 times less cover) and low-growing forbs (3.1â9.7 times more cover). The direction of fractional difference varied most for bryophytes and conifer tree seedlings. For example, bryophytes had lower cover on the 98 ROW Chinchaga B site compared to the post-wildfire analogue (1.5 times) but higher cover on the ROW Chinchaga A and Little Smoky and sites (1.2â3.5 times). For key plant groups, the BC dissimilarity scores were 0.44 for ROW Chinchaga A, 0.49 for ROW Chinchaga B, and 0.69 for ROW Little Smoky. Some key plant groups, including Salix spp (1.3â2.7 times more cover) and Trifolium spp (3â9.7 times), cover relatively large extents of most ROW sites. For example, at the Chinchaga B site the total cover of Trifolium spp was 34.8% while cover at the wildfire site was 3.6%. However, Salix spp cover (6.9 versus 12.8%) was lower on the ROW Little Smoky site. Classes covering relatively small extents of the ROW sites include the forb class Asteraceae spp (11.9â71.7 times less cover) as well most woody classes, including Picea spp (1.2â2.5 times), Rhododendron spp (2.7â9 times) and Vaccinium spp (12.1â57.7 times). Note that Picea spp had greater cover on the ROW Smoky site compared to the analogue (6.3 versus 15.9%). This is largely in line with coverage estimates found in the field transects, illustrated in Figure 4.2. 4.4 Discussion Robinson et al. (2022) recently highlighted the potential for drones to map vegetation composition and structure for restoration ecology and management. Several studies have demonstrated the use of high-resolution drone imagery for such purposes, typically mapping a few (2â7) ecologically relevant plant functional types or individual plants such as conifer seedlings or an invading species (Cruzan et al. 2016; Cunliffe et al. 2016; Alexandridis et al. 2017; van Iersel et al. 2018b; Cao et al. 2018; Chadwick et al. 2020; Villoslada et al. 2020). My work has integrated remote sensing products produced by high spatial resolution drone imagery of spectral reflectance, vegetation structure and solar illumination conditions to map plant composition. I produced maps at higher 99 thematic resolution compared to previous studies, in order to assess a wider range of ecosystem attributes. This aids assessments of composition using an appropriate reference ecosystem and identification of unwanted vegetation encroaching a habitat, providing a basis for restoration ecologists to manage sites and implement mitigation measures (Gann et al. 2019). 4.4.1 Presence-Absence Models Presence-absence models were produced for various plant functional types and key plant groups at lower taxonomic levels (where feasible), as well as bare soil. The models of plant functional types were more accurate than key plant groups, with average F-scores of 0.67 and 0.58, respectively. This was attributed to their higher precision, reflecting a greater proportion of present predictions being correct. Spatial maps were created based on probability scores and a mapping routine which verifies agreement between the associated models, for example between the model for Rhododendron spp and shrubs. This allows us to take advantage of the strengths of models at different thematic scales, where the plant functional types generally produce relatively fewer false positives, and the key plant groups (lower taxa) produce fewer false negatives in some models. I found that standard deviations of NDVI and NIRv were the most important variables overall, and in some instances, those of PSRI as well. The standard deviation of PSRI, which is sensitive to the ratio of bulk carotenoids to chlorophyll, was important for discriminating native flowering species (i.e., Asteraceae spp), woody vegetation (including Vaccinium spp, Rhododendron spp and Picea spp), and bryophytes. These higher standard deviations may be caused by blooming events observed in Asteraceae spp, berry production in Vaccinium spp, and senescing in Rhododendron spp. However, spectral variation captures not just variations within plants themselves but also 100 within their habitats, such as the presence of exposed soil and co-occurring species (Fassnacht et al. 2022). For instance, exposed soil was commonly observed adjacent to Picea spp and bryophytes, while field samples indicated that Rhododendron spp often co-occurred with bryophytes. 4.4.2 Probability-Based Mapping Routine My results were produced at two thematic scales â plant functional types and key plant groups â revealing patterns of emerging desired and undesired vegetation and enabling pair-wise site comparisons. For example, cells with a high probability of Trifolium spp occurrence, which is undesired as it represents pasture vegetation and may limit tree establishment, are more abundant on the pipeline ROWs and visually patchier. In contrast, traditional field studies do not reveal the extents and patterning of taxa and functional vegetation types unless a very dense sample network has been set up and subjective site-level cover estimates are recorded (Levin 1992; Wheatley and Johnson 2009). BrayâCurtis dissimilarity scores suggest that the ROW Chinchaga A site is most similar to its post-wildfire analogue, having a score of 0.28 and 0.44 for plant functional types and key plant groups, respectively. The fractional difference in cover is relatively small, i.e., smaller than a factor of 2, for 25% of the key plant groups which include bryophytes and grasses. The cover of the remaining key plant groups was relatively dissimilar between the ROW and wildfire sites, having a fractional difference larger than a factor of 2.6. Of these groups, Astereacea spp (0.3 versus 21.5%) and Vaccinium spp (0.3 versus 14.5%) had very little cover. The low coverage of Asteraceae spp, an important component of native wildflower communities, is concerning as it deviates from post-101 wildfire site composition and has an important function for bumble bee species (Pengelly and Cartar 2010). Other large differences were found for Salix spp (24.4 versus 9.3%) and Rhododendron spp (4.5 versus 11.9%). A larger component of Rhododendron spp may be desired based on post-wildfire composition, however, this may not be concerning as Salix spp and Picea spp replace some of its function. Both shrub taxa and conifers can limit line-of-sight which potentially improves caribou habitat (Filicetti et al., 2019). In addition, large Salix spp and Picea spp cover may be a result of plantings performed to stabilize soil and increase vegetation structure (TC Energy 2014, 2020a). The cover of bryophytes, grasses, Picea spp and Salix spp was relatively similar between the ROW Chinchaga B site and its post-wildfire analogue, having a fractional difference smaller than a factor of 2. The remaining groups had a fractional difference larger than a factor of 3.8. Despite comparable Salix spp cover, which is desirable, the establishment of shrubs (16.1 at ROW versus 35.7% at wildfire) was limited compared to the post-wildfire analogue. The largest differences in cover were found for Trifolium spp (34.8 versus 3.6%), Asteraceae spp (1.8 versus 21.5%), Rhodondendron spp (3.1 versus 11.9%) and bare soil (6.1 versus 1.2%). High coverage of Trifolium spp is particularly undesirable as it characterizes agronomic plant communities that can inhibit tree establishment (TC Energy 2014, 2020a). Only cover of Salix spp was relatively similar between the ROW Little Smoky site and its post-wildfire analogue, where cover of 50% of the key plant groups had a fractional difference larger than a factor 4. Like the paired Chinchaga B site, Picea spp (6.3 versus 15.9%) and Vaccinium spp (0.3 versus 17.3%) were almost completely absent on the ROW, however, the associated 102 differences were largest for the paired Little Smoky site. In addition, shrubs (9.4 versus 50%) and conifer tree seedlings (6.3 versus 15.9%) covered relatively small extents of the ROW site. In contrast, grasses (19.8 versus 4.1%), bryophytes (43.5 versus 12.5%) and Trifolum spp (17 versus 4.5%) covered a large extent of the ROW site. High bryophyte cover is desirable as bryophytes fulfill important ecosystem functions in temperate and boreal forests. Visual assessment of the mapping product suggests that line-of-sight has not been reduced due to a lack of regenerating woody vegetation (Bonan and Shugart 1989). 4.4.3 Model Improvement: Bias and Predictor Variables Spectral signals and vegetation structure change throughout the growing season due to changing photosynthetic activity, phenological events and leaf structures (Dandois and Ellis 2013; Nuijten et al. 2019). My drone imagery, collected in August which is late summer, captured species with distinct spectral signals, notably those in blooming phases or showing early signs of senescence. Further research is necessary to examine how the developed models will perform when applied to imagery collected at different times of the growing season. Model accuracies may improve by incorporating multi-temporal reflectance data at a frequency timed with phenological variations. For example, conifer seedlings including Picea spp, for which I found relatively low accuracies (i.e., F-scores) of 0.60 and 0.54, respectively, are more distinct during dormant season (Chadwick et al. 2020). Improving model performance could involve increasing the spatial resolution of predictor variables (e.g., mean NIRv) to reduce the mixing of speciesâ spectral signals, while still capturing adequate spectral variation. Achieving this requires improving the spatial alignment between field 103 quadrats (i.e., training and validation data) and orthomosaics, which can be as large as 10 cm. This task is particularly challenging in remote areas, where distant differential GNSS stations and limited cellular service are common. However, the use of mobile differential stations for RTK corrections during both drone and ground-based GNSS data collection could facilitate this by improving positioning accuracy significantly (Assiri et al. 2023). The developed presence-absence models were based on sample data sets covering relatively small geographic extents. In such cases, models may become biased due to underrepresentation of important environmental determinants such as plant co-occurrences and microhabitat conditions (Besag 1974). Therefore, it will be necessary to acquire additional field samples when applying my methodology to proximate sites. Note that such bias may be less likely to occur if the predictor variables do not measure vegetation directly, but instead represent characteristics of habitat suitability including surface temperatures, soil parameters, and micro topography (Bradley et al. 2012). Such predictors may not be easily derived from drone imagery or contain sufficient spatial detail. For example, terrain characterized by DAP may not capture small topographic features, particularly under thick vegetation (Lovitt et al. 2017). To derive these additional ecosystem attributes, other drone-based sensing technologies are required. For instance, LiDAR can capture small changes in micro topography, whereas thermal imaging may be used to identify surface water and elevated soil moisture content (Khanal et al. 2017; Poirier et al. 2020; Watts et al. 2023). 4.4.4 Vegetation Structure as Predictor This research utilised a range of drone-acquired spectral and structural predictors. Canopy height models alone, which represent vegetation height, enable mapping of up to three plant functional 104 types, for example shrubs, trees and swale/hummock habitat (Cruzan et al. 2016; Cunliffe et al. 2016). Alternatively, a variety of point cloud metrics, such as height, canopy cover, and height variability, can be used to classify vegetation. Chapter 3 demonstrated that mapping structures representative of woody vegetation and tall grassâforb communities is possible, however, distinguishing lower-stature vegetation types like bryophyte-dominated communities, flowering plants, and short pasture-type vegetation using only structural data remains challenging. This chapter builds on their work by utilizing the six developed classes of vegetation structure, which were produced through unsupervised clustering as described in Chapter 3. Derived predictor variables included structure class occurrence (i.e., binary presence-absence variables) and class count. The overall importance score of structure class occurrence (i.e., sum of SHAP values of binary variables) was highest or second highest for all logistic regression models. Some models including bryophytes, Picea spp, as well as the plant functional types shrubs and conifer trees, however, were largely driven by low estimates of received solar illumination throughout the growing season, which is another predictor variable derived from modelled vegetation structure. 4.4.5 Operationality The methodologies and results produced in this research can be readily used to assess large restoration and wildfire sites (40â120 ha) due to recent advances in camera and drone technology (Chadwick et al. 2022b). Improved flight duration of drones and enhanced spatial image resolution and capture speeds have reduced data acquisition time significantly (Coops et al. 2019). Moreover, the use of very high spatial resolution multi-camera image acquisitions, where data sets from multiple cameras are fused, for mapping key plant groups at low taxonomic levels, requires near-centimeter alignment between data products. This is achievable with advancements in Global 105 Navigation Satellite System (GNSS) technology, including RTK GNSS, and improvements in computational power and software (Forlani et al. 2018). However, the effectiveness of these methodologies to capture spectral variation depends on image resolution (< 5 cm) and optimal illumination conditions such as cloud overcast and solar noon (Cruzan et al. 2016; Tu et al. 2020). Additionally, non-sunlit pixels may need to be removed (Shen and Cao 2017). 4.5 Conclusion This study utilized high-resolution RGB and multispectral drone imagery to derive fine-scale vegetation distribution information to aid restoration monitoring. Presence-absence models were produced for plant functional types and key plant groups at lower taxonomic levels using predictor variables derived from available drone-based maps of vegetation structure, spectral indices derived from multispectral reflectance data, and insolation surfaces. Performance metrics showed that the presence-absence of plant functional types can be more accurately predicted (F-score = 0.67) than the occurrences of associated key plant groups (F-score = 0.57). Generally, important predictor variables included the types of vegetation structure, spectral variation (i.e., standard deviation of various spectral indices), and the mean value of NIRv. Using a novel methodology, probabilistic outputs of the presence-absence models were used to produce discrete maps. The maps may allow the patchiness of the landscape to be assessed, revealing patterns of emerging desired and undesired vegetation and enabling comparisons with contemporary reference sites (e.g., post-wildfire analogues). Plant composition at the 10-year-old ROW site in the Lower Boreal Highlands ecoregion (Bray-Curtis = 0.28) was most similar to its paired post-wildfire analogy, followed by the nearby 5-year-old ROW site (BC = 0.49) and, lastly, the 14-year-old ROW site in the Upper Foothills ecoregion (BC = 0.69). Salix spp was the dominant woody component on the ROW sites, 106 while Asteraceae spp (a key native wildflower species) and Picea spp (a key conifer species) were generally lacking. Trifolium spp, which is undesirable as it characterizes non-native agronomic plant communities, covered relatively large areas at two of the three ROW sites (17 and 34.8%). My approach can be readily applied to similar restoration and post-wildfire sites and be used to inform restoration programs and assess restoration success. 107 Chapter 5: Informing Reclamation Success in Forested Landscapes using Drone-based Plant Community Assessments: Enhancing Vegetation Assessments and Reference Site Selection3 5.1 Background and Motivation The high-spatial resolution imagery and subsequent vegetation maps produced by drone data offer a unique perspective on the spatial patterns of these compositional and structural classes over the landscape, beyond what plot locations can provide. This has paved the way for innovative assessments, including the analysis of how plant communities are distributed in relation to habitat characteristics (Leslie and Kronenfeld 2011; Cromley et al. 2014). It also facilitates the study of the spatial distribution of individual plants (Ben-Said 2021; Rudge et al. 2022), the dimensions of vegetative patchesâsuch as size and shapeâand the degree to which these patches aggregate (Kelly et al. 2011; McGarigal et al. 2012). Through these applications, drones are revolutionizing the way ecologists study and understand plant community development. Botanical surveys are often employed to gather data on terrestrial ecosystem attributes, comparing them against predefined targets or historic conditions (Wortley et al. 2013; Gann et al. 2019; Mazón et al. 2019). Given the difficulties in establishing historical references and selecting targets, there is a growing interest in monitoring appropriate reference ecosystems over time to understand and compare vegetation patterns (Hiers et al. 2012). 3 Nuijten, R. J. G., Coops, N. C., Prescott, C. E. & Theberge, D. (2024) Informing reclamation success in forested landscapes using drone-based plant community assessments: Enhancing vegetation assessments and reference site selection. In review. 108 The establishment of a reference model for young reclamation and restoration sites requires intact and nearby native ecosystems that undergo natural disturbances, supporting native early successional species. However, selecting sites can be challenging due to the spatial variation in habitat conditions and plant communities, which are often unknown prior to field visits. This also affects their assessment as conventional sampling methods, such as line-point intercept or quadrat methods, may not accurately capture spatial variability and patterns and can produce skewed cover estimates (Hernandez-Stefanoni and Ponce-Hernandez 2006). Moreover, the concurrent assessment of reclamation and reference sites can significantly increase the time and costs of botanical surveys as the total coverage area increases (Shuman and Ambrose 2003; Braun and Reynolds 2012). Drones, however, may reduce the need for ecologists to physically traverse challenging terrains and place field plots, thereby saving time, and capture spatial variability. 5.1.1 Study Objectives In this study, I present a proof-of-concept examining the similarities, differences, and unique insights offered by drone-based mapping compared to conventional field-based sampling methods. My investigation seeks to determine whether ecologists can observe and infer the same findings regarding diversity, vegetation structure, and functional types from these two distinct sources of information. My research primarily focuses on site comparisons, specifically evaluating reclamation sites in Northwest Alberta, Canada, against those disturbed by wildfires to serve as references. Through this analysis, I explore the challenges of using naturally disturbed sites as benchmarks and discuss the potential of drone-based maps to refine the way these references are used. 109 5.2 Materials and Methods 5.2.1 Study Sites The proof-of-concept focuses on three reclamation sites in northwest Alberta, Canada, as described in Section 2.2 and Table 2.1. In addition, I chose proximate, similarly aged, wildfires-disturbed sites as references with which to compare the reclamation sites. For, my underlying assumptions and reference site characteristics I refer to Section 2.2.2. The selection of fire-affected reference sites for comparison with pipeline rights-of-way (ROW) sites was based on publicly available datasets and criteria related to topography, ecoregion, distance to the ROW site (with straight-line distances ranging from 5 to 45 km), and pre-disturbance ecosite type (i.e., Albertaâs Derived Ecosite Phase). Moreover, wildfires were selected based on an age similar to the pipeline reclamation sites, although the maximum age difference was set at 7 years to include a sufficient number of candidates. Lastly, only wildfire sections located within 2000 meters of roads and those that had not been subjected to harvesting or burning in the 50 years preceding the disturbance were considered. The selection process using geospatial layers is further outlined in Section 2.2.2.1 Fire severity varied across reference sites as described in Section 2.2.2.2. Moreover, certain sections showed minimal evidence of tree presence before the wildfire, characterized by a scarcity of mature trees, whether alive or dead. Note that I had limited data on other site characteristics influencing vegetation regeneration patterns following disturbance, including soil moisture, nutrients, and compaction. 110 5.2.2 Information Acquisition The proof-of-concept incorporated information acquisition using both field- and drone-based approaches. The first approach involved a typical botanical field survey with a limited number of plots (i.e., transects) per site. Plant species, height, and cover were recorded in 2x2-m2 quadrats, organized along 20-meter transects. Details on transect placement strategies and summaries of species richness can be found in Section 2.3.1 and Table 2.4. The second approach utilized drone data collection, where data from the botanical field surveys were used to develop models with the drone imagery on vegetation structure (from Chapter 3) and plant functional types (from Chapter 4). These datasets are highlighted in Figure 5.1. 111 Figure 5.1 Maps displaying drone-based imagery and analyzed maps for a section of a pipeline right-of-way (ROW), including: (A) red-green-blue (RGB) ortho imagery, (B) false color composite from multispectral ortho imagery, (C) maps of plant functional types, and (D) maps of vegetation structure. 112 5.2.3 Site Assessments 5.2.3.1 Statistical Comparisons of Diversity I compared plant composition between site pairs using both field data and drone-based maps, focusing on species, plant functional, and structure types. To do this, I first calculated Bray-Curtis distances based on species/plant type abundance (i.e., percentage cover) across field transects or, alternatively, for drone-based maps, across study site partitions (Bray and Curtis 1957). I applied a log transformation to the field-sample derived abundance data to mitigate disproportionate effects from rare species (Field et al. 1982). I then employed Non-parametric Analysis of Similarities (ANOSIM) to test whether there are significant differences in composition between reclamation and reference sites, as opposed to within them (Clarke 1993; Knight et al. 2020; Oksanen et al. 2022). A significant deviation of the R-statistic from zero (p < 0.05) indicates significant dissimilarity either between study sites (R value close to 1) or within the study sites (R value close to -1). In these site comparisons, field transects acted as units for field-based abundance metrics, while custom site partitions of approximately 2500 m², created through k-means spatial clustering, acted as units for map-derived abundance metrics. Lastly, I deployed Similarity Percentages Analysis (SIMPER) to quantify the contributions of species or plant types to site dissimilarity (Clarke 1993; Lemoine 2015; Oksanen et al. 2022). I calculated Simpsonâs evenness for each study site (Nagendra 2002). Simpson's evenness values measure the evenness of species/plant types within a community based on abundance data; values close to 0 indicate complete unevenness, while values approaching 1 represent evenness. 113 5.2.3.2 Spatial Patterning Analysis To evaluate the spatial arrangement of individual vegetation types from the drone-based maps, I calculated five Fragmentation Statistics (FRAGSTATS; Bosch, 2019). These metrics include average patch area, shape complexity of patches (shape index), average distance between patches of the same vegetation type (average Euclidean distance), size of the largest patch (largest patch index), and a metric of fragmentation that describes the amount of edge relative to the site area (edge density). 5.2.4 Refinement of the Reference Model Lastly, I utilized a tool to assess the compositional similarity among partitions of the study sites, aiming to identify those that significantly deviate. Initially, as described in Section 5.2.3.1, I partitioned the study sites, each approximately 2500 m². For each partition, I calculated a Bray-Curtis distance matrix using abundance data. This matrix facilitated the implementation of Non-Metric Multidimensional Scaling (NMDS), an ordination technique that graphically illustrates the compositional differences between partitioned reference sites and reclamation sites (Pedregosa et al. 2011; Oksanen et al. 2022). I established a 90% confidence interval in the NMDS ordination space, based on the partitions of the reference site (Chinchaga wildfires), similar to Hiers et al. (2012). Partitions lying outside this confidence interval were flagged as outliers, suggesting further review and potential removal to refine the reference model. Welchâs t-tests were used to identify plant functional types for which mean cover was statistically different between the outliers and the majority of the reference sites. 114 5.3 Results 5.3.1 Vegetation Composition I identified dissimilarities in species and plant type composition across the three reclamation (i.e., pipeline ROW) sites compared to their references, detailed in Table 5.1. All reclamation sites exhibited significant dissimilarity in field-derived species composition, with ANOSIM R-statistics ranging from 0.9â0.98. However, compositional dissimilarities varied greatly among paired sites in other datasets. For example, in both field- and drone-derived plant functional type composition, ROW Chinchaga A resembled its reference most closely (R-statistic ranging from -0.14â0.16), while ROW Little Smoky showed the greatest dissimilarity (0.8â0.96). I analyzed evenness across vegetation classes, listed in Table 5.1, revealing different patterns between datasets. Field-based species data showed relatively low evenness across all sites (Simpsonâs evenness 0.16â0.41). Notably, differences in evenness patterns emerged between field- and drone-based plant functional types. Field-based evenness values ranged from 0.5â0.75, with the highest evenness observed for the Chinchaga wildfire references (0.75), followed by ROW Little Smoky (0.66). In contrast, drone-based evenness values varied from 0.43â0.61, with the Chinchaga wildfire references (0.44) and ROW Little Smoky (0.47) demonstrating relatively low evenness. 115 Table 5.1 Summary of plant diversity and composition at three reclamation (pipeline rights-of-way) and reference (wildfire-disturbed) sites, based on field-based samples and drone-based maps. The Simpsonâs evenness scores range from 0â1, with 1 indicating highest evenness. The ANOSIM R-statistic ranges from -1â1, with 0 indicating perfect site similarity. Chinchaga A (Reclamation site) Chinchaga B (Reclamation site) Chinchaga A and B (Reference site) Little Smoky (Reclamation site) Little Smoky (Reference site) From field-based samples Species evenness2 0.3 0.16 0.24 0.41 0.29 Plant functional type Evenness 0.58 0.52 0.75 0.66 0.5 Species ANOSIM (R-statistic and p-value) 0.93 (0.04) 0.90 (0.01) n/a 0.98 (0.03) n/a Plant functional type ANOSIM (R-statistic and p-value) -0.14 (0.65) 0.64 (0.01) n/a 0.81 (0.03) n/a From drone-based maps Structure type Evenness 0.88 0.73 0.75 0.68 0.85 Plant functional type Evenness 0.61 0.46 0.44 0.47 0.43 Structure type ANOSIM (R-statistic and p-value) 0.03 (0.25) 0.44 (< 0.01) n/a 1 (< 0.01) n/a Plant functional type ANOSIM (R-statistic and p-value) 0.16 (< 0.01) 0.43 (< 0.01) n/a 0.96 (< 0.01) n/a 116 Figure 5.2 Comparisons of plant functional type cover across site pairs derived from drone-based maps and field-based samples, including drone-based Similarity Percentage (SIMPER) values within round brackets. *Wildfire Chinchaga A and B are displayed separately but were treated as a unified reference in further analyses. I compared the relative abundance, quantified as percentage cover, across various plant functional types using data obtained from both field transects and drone-derived maps (Figure 5.2). While there was agreement in shrub cover estimates between the two datasets, disparities were observed 117 for other plant functional types. For instance, at the ROW Little Smoky site, grass cover estimates from drone-based data were notably lower compared to field data (15% vs. 31%), whereas bryophyte cover was higher (19% vs. 49.5%). Additionally, drone-derived data suggested that the area covered by conifer seedlings at ROW Chinchaga A and its reference sites was potentially larger compared to what was indicated by the field data. SIMPER values (derived from drone maps) highlighted the vegetation types that contributed the most to compositional dissimilarity between paired reclamation and reference sites (Figure 5.2). For instance, at the ROW Chinchaga B and ROW Little Smoky sites, low-growing forbs (SIMPER of 31% and 11%, respectively) and shrubs (20% and 32%, respectively) were major contributors to site dissimilarity, with low-growing forbs being more abundant on reclamation sites and shrubs more prevalent on reference sites. Additionally, bryophytes (SIMPER of 27%) contributed most to dissimilarity at the ROW Little Smoky site. Lastly, Figure 5.3 similarly highlights relative cover and SIMPER values for mapped vegetation structure types. 118 Figure 5.3 Comparisons of vegetation structure type cover across site pairs derived from drone-based maps, including SIMPER values within round brackets. *Wildfire Chinchaga A and B are displayed separately but were treated as a unified reference in further analyses. 5.3.2 Spatial Patterning Figure 5.4 provides more nuanced insights into the spatial patterning and variability of plant types across sites. Disparities were noted between reclamation and wildfire reference sites. For example, low-growing forbs, which exhibited greater total cover on the reclamation sites, had relatively large average patch sizes (0.3â0.41 vs. 0.16â0.19), high shape index (0.17â0.31 vs. 0.09â0.12), and small Euclidian distances between patches (0.09â0.29 vs. 0.45â0.50) compared to the wildfire reference sites. Moreover, Figure 5.4 highlighted the spatial patterning of woody vegetation on reclamation sites. For instance, ROW Chinchaga B exhibited relatively limited cover of shrubs, 119 yet they demonstrated a large average patch size (0.34) and short average distances between patches (0.16). In contrast, patches with conifer tree seedlings at this site were relatively small in size (average patch size of 0.24) and spaced further apart (Euclidean nearest neighbor of 0.39). Lastly, high spatial variability was observed among some plant functional types at the wildfire references. For example, at the Chinchaga wildfires, tall-growing forbs and shrubs formed relatively large, continuous patches (largest patch index > 0.75) but demonstrated relatively high edge densities (0.47 and 0.96, respectively), indicative of fragmentation. Figure 5.5 similarly highlights fragmentation statistics for mapped vegetation structure types. 120 Figure 5.4 Five fragmentation statistics (FRAGSTATS) detailing spatial patterns and patch dimensions across mapped plant functional types. All values were normalized. 121 Figure 5.5 Five fragmentation statistics (FRAGSTATS) detailing spatial patterns and patch dimensions across mapped structure types. All values were normalized. 122 5.3.3 Reference Model Refinement In Figure 5.6, I present and analyze specific areas of the Chinchaga wildfire reference sites that show unique plant functional type compositions, following the spatial partitioning of the sites outlined in Section 5.2.3.1. The left panel demonstrates the compositional variability between partitioned references through NMDS, accompanied by their respective 90% confidence interval. Notably, eight sections, each approximately 2500 m2 in size, were found to fall outside this confidence interval. The right panel reveals significant compositional differences for bryophytes, shrubs, and bare soil, as determined by Welchâs t-tests, when comparing partitions within and beyond the confidence interval. Furthermore, I observed substantial variability in the cover of grasses and tall-growing forbs, where the interquartile ranges differ by 92% and 99%, respectively. Figure 5.6 Compositional similarity of mapped plant functional types within the Chinchaga wildfire references, following spatial partitioning as in Section 5.2.3.1, visualized with Non-metric Multidimensional Scaling (NMDS). In addition, their corresponding 90% confidence interval is shown. The right panel compares plant type cover within and outside the confidence interval using Welchâs t-tests, where * indicates a p-value < 0.05. 123 5.4 Discussion 5.4.1 Qualities of Drone and Field-based Datasets 5.4.1.1 Evenness Plot data and drone-based maps showed differences in measures of evenness of plant functional types. Plot data indicated that plant type abundance was most even at the reclamation site in the Little Smoky region (Simpsonâs evenness of 0.66) and at the wildfire references in the Chinchaga region (0.75). However, drone-based maps provided lower evenness measures of 0.47 and 0.44, respectively, with ROW Chinchaga A being the most even (0.61). The drone maps demonstrated, for instance, that the reduction in evenness at the Little Smoky reclamation site is due to high bryophyte cover (covering almost 50% of the site), which was not observable in field-based estimates (19% cover), likely due to the limited spatial coverage of field plots. 5.4.1.2 Composition The compositional similarities between reclamation sites and their references remained mostly consistent across datasets. Both drone-derived abundances of vegetation structure and plant functional types, as well as field-derived plant functional type abundance, demonstrated a consistent ranking in compositional dissimilarity across site pairs. Specifically, at the reclamation site, ROW Chinchaga A exhibited the highest similarity to its reference counterparts (ANOSIM R-statistic -0.14â0.16), while ROW Little Smoky showed most dissimilarity (R 0.81â0.96). However, some differences still exist, as field-based plant functional type composition was not significantly different between ROW Chinchaga A and its reference (R -0.14), whereas drone-derived composition data showed a relatively small but significant difference (R 0.16). Conversely, when considering field-based species abundances, all site pairs had comparable compositional 124 similarity and demonstrated strong, significant dissimilarity in species composition (R 0.9â0.98). However, at this higher taxonomic resolution, results may also have been affected more by the field samples potentially not fully capturing the sitesâ diversity, particularly considering the limited density of field plots in my study (Rochefort et al. 2013; Bried 2013; Rodgers et al. 2018; Viani et al. 2018; De Stefano et al. 2021). Our study was limited to assessments of maps of plant functional types. There is a consensus in the literature that drone-based remote sensing is currently limited to mapping dominant species and plant functional types (Knoth et al. 2013; van Iersel et al. 2018b; Cao et al. 2018; Qian et al. 2020; Villoslada et al. 2020; Filippi et al. 2022; Nuijten et al. 2024), as well as a number of indicator and invasive species (Qian et al. 2020; Chadwick et al. 2022a). The latter typically uses object-based image analysis (OBIA) approaches, which utilize plants' shapes and visible edges, or convolutional neural networks (CNNs) that learn from annotated imagery with a level of sophistication comparable to the human visual cortex. However, for the assessment of species composition, field sampling is necessitated. 5.4.1.3 Spatial Patterning Drone maps can offer ecologists insights into regeneration potential and challenges through spatial cues. For instance, the reclamation site ROW Little Smoky, despite being the oldest reclamation site, exhibited limited cover of shrubs and conifer seedlings, as well as relatively large average between-patch distances for both vegetation classes (both approximately 0.19, with values ranging from 0 to 1). In contrast, bryophytes covered nearly half of the site but also exhibited high values for the largest patch index (0.58; indicative of their dominance) and edge density (0.82; indicative 125 of their fragmentation). However, bryophytes may play a positive role in facilitating spruce establishment as highlighted in Gärtner et al. (2011). Another notable observation is the relatively high cover of low-growing forbs on the reclamation site ROW Chinchaga B, and high grass cover on all reclamation sites. Furthermore, at ROW Chinchaga B, grasses and low-growing forbs demonstrated relatively large average patch sizes (0.18 and 0.16, respectively), while patches were located relatively close together (Euclidean distances of 0.15 and 0.13, respectively). This underscores their dominance and connectivity, which could be an indicator of agronomic communitiesâ presence. Several decades ago, reclamation in the region used agronomic seed mixes, focussing on rapid revegetation (Hardy BBT Limited 1989; Azeria et al. 2020). Such communities are now undesirable but remain a challenge for newly reclaimed sites due to ingress from adjacent disturbance and competitive advantages (TC Energy 2014, 2020a). Beyond the assessment of spatial patterning of individual vegetation classes, drone-based maps offer more potential for spatial arrangement assessments, including capturing wildlife movement pathways and assessing co-location between plants (e.g., joint-count statistics; Cromley et al., 2014). For instance, vegetation structure maps, similar to those presented in my study (Chapter 3), can help identify potential movement pathways of predators such as wolves (which are important in the study region due to their influence on woodland caribou populations) and human access to the pipeline ROWs through least-cost path and viewshed analysis (Adriaensen et al. 2003; Lecigne et al. 2020). Lastly, with the increasing number of studies mapping individual plants (e.g., tree seedlings) via drone-based remote sensing (Qian et al. 2020; Chadwick et al. 2022a), spatial point pattern analysis, examining spatial randomness and clustering, becomes relevant for understanding pattern-process relationships in plant ecology (Ben-Said 2021; Rudge et al. 2022). 126 5.4.2 Reference Model Refinement The reference sites for my proof-of-concept, affected by wildfires, were chosen using open data as detailed in Section 5.2.1. This included a 25-m resolution digital elevation model and vegetation polygons, which were primarily derived from aerial photographs, photogrammetry, and manual digitization (Alberta Environment and Parks 2017; Alberta Agriculture and Forestry 2020). However, the quality of the selected references could potentially be improved by incorporating advanced remote sensing datasets. For example, open airborne Light Detection and Ranging (LiDAR) data can provide more accurate and detailed digital elevation models as well as information on ecosystem attributes like present forest types and canopy openings (Senf 2022) Additionally, satellite-based land cover classifications, like Canadaâs National Terrestrial Ecosystem Monitoring System, could provide additional maps of dominant tree species and non-treed habitats (Wulder et al. 2018; Hermosilla et al. 2022). The spatial patterning analysis revealed variability in certain plant functional types across the wildfire reference sites. Notably, the Chinchaga wildfires included the largest continuous grass patch, while tall-growing forbs and shrubs also formed sizeable, continuous patches (largest patch index > 0.75). This variability confirms the expected high spatial heterogeneity of plant communities in areas disturbed by wildfires and natural disturbances at large (Gärtner et al. 2011; Burkle et al. 2015; Baltzer et al. 2021). Together with the limited availability of spatially explicit information on pre-disturbance conditions and variations in disturbance severity, this may hinder their effectiveness as references in restoration models. In the proof-of-concept, I employed Non-metric Multidimensional Scaling (NMDS) to identify areas within the wildfire sites that had deviating plant compositions, earmarking them for further evaluation and potential exclusion from 127 the reference model, similar to the approach taken by Hiers et al. (2012). They conducted their analysis across approximately 30 plots to study species composition for a longleaf pine (Pinus palustris) restoration project at Eglin Air Force Base, Florida, USA. Alternatively, drone mapping, segmented into equal-sized areas, can achieve similar objectives more efficiently without necessitating extensive plot sampling. Looking ahead, future applications may benefit from integrating spatially explicit datasets that characterize habitat conditions into reference model refinements. For example, this could include soil type maps derived from interpolated soil samples (Zhang et al. 2020). Moreover, although only a few soil characteristics can be directly observed through drone-based remote sensing, the use of LiDAR sensors for micro topography models (Moudry et al. 2019; Poirier et al. 2020) and thermal cameras capturing surface temperature and water content is promising (Khanal et al. 2017; Ikkala et al. 2022). 5.5 Conclusions My study highlighted the qualities and limitations of field- and drone-based datasets for plant community assessments in the context of reclamation. I demonstrated the advantages of drone-based mapping in measuring evenness and composition of plant types. For example, they can provide deviating evenness measures compared to field-based data. The research also revealed the utility of drone maps in analyzing spatial patterns, offering critical insights into regeneration potential. A proof-of-concept proposed NMDS for refining reference models (i.e., wildfire-disturbed sites) in reclamation and restoration, earmarking sections in drone maps with deviating plant type composition. Moreover, I discussed the use of increasingly available advanced remote 128 sensing data, which can help improve the initial selection of naturally disturbed sites as references. Ultimately, this research underscores the complementarity of drone-based remote sensing with conventional field assessments, advocating for its expanded application to enhance the efficiency and depth of ecological evaluations across extensive landscapes. 129 Chapter 6: Advances in Drone-based Remote Sensing for Restoration Assessments: Information Uncertainty, Complexity, and Cost-Effectiveness4 6.1 Background and Motivation A new area for drone remote sensing applications is in the fields of ecological restoration, which aim to recover native biodiversity (i.e., species composition and structure) and ecological functioning (Gann et al. 2019). To date, however, the adoption of drones in restoration monitoring programs remains nascent, with their potential and limitations not fully examined compared to conventional field surveying (Robinson et al. 2022; McKenna et al. 2023). Moreover, despite drones being applicable in industrial settings where monitoring is critical for maintaining operational approval (e.g., surface mining and gas exploration), their integration into regulatory frameworks has not been realized (Government of Alberta 2013; Young et al. 2022). Their effectiveness may be constrained in ecosystems characterized by high species diversity and varying densities, such as early to mid-seral forests (Moudry et al. 2019; Durgan et al. 2020; Wilson et al. 2022), and in estimating certain ecosystem attributes, like species richness (Almeida et al. 2021). Additional hurdles may include upfront costs of drones and sensors, complex data processing, the need for collecting field samples for training and validation purposes, and regulatory concern (Sankey et al. 2017; Coops et al. 2019). 4 Nuijten, R.J.G., Coops, N.C., Zerriffi, H. & Theberge, D. (2024) Advances in drone remote sensing for restoration assessments: Information uncertainty, complexity and cost-effectiveness. In review. 130 6.1.1 Study Objectives In this chapter I compare information characteristics of a range of ecosystem attributes acquired from both drone and field-based approaches designed for restoration assessments. To do so, I undertake two primary sets of analyses. First, general perceptions on information uncertainty and complexity of drone applications are evaluated through a systematic literature review and sentiment analysis. Second, a cost-effectiveness analysis is conducted using a case study of plant community assessments in Alberta, Canada, focusing on important cost factors and the spatial coverage of information. My overall aim is to provide decision-makers concerned with completing ecosystem assessments, insights into the value, limitations, and opportunities of drone-based remote sensing acquired data. Therefore, I investigate how drone-based remote sensing approaches compare to conventional field surveying in terms of information uncertainty, complexity, and cost-effectiveness. I conclude with recommendations for future implementations and identify areas for further research, particularly in domains where costs, information uncertainty, and complexities can be potentially reduced. 6.2 Assessing Early Ecosystem Recovery using Drones: a Literature Review 6.2.1 Methods 6.2.1.1 Data Collection To find relevant publications on the use of drone and field-based approaches for ecosystem recovery assessments, with special focus on uncertainty and complexity, I conducted two systematic literature searches within the Web of Science core collection. I searched the titles, 131 abstracts and main texts of English journal articles published in the last 10 years (2013â2023; based on the first drone paper found using set search criteria). Search keywords were categorized as follows to retrieve papers using field-based approaches: â¢ Restoration-related keywords: "restoration", "regeneration", "rehabilitation" or "reclamation". â¢ Assessment-related keywords: "monitoring" or "assessment". â¢ Habitat-related keywords: "plants", "terrestrial" or "vegetation". â¢ Approach-related keywords: âsamplingâ, âsampleâ or âplotsâ. â¢ Exclusionary keywords: "UAV", "UAS", "drone", "RPAS" or "remote sensing". The inclusion of at least one keyword from each of the three specified categories was required, while journal articles could not include any of the exclusionary keywords. To locate publications using drones, exclusionary keywords were removed from the search, and the approach-related keywords were replaced by: â¢ Approach-related keywords: "UAV", "UAS", "drone", or "RPAS". The initial searches yielded 143 and 709 publications for field and drone approaches, respectively. The list of publications was further reduced following a review of the abstracts aided by a screening tool from Van De Schoot et al. (2021). Only studies conducting original surveys, with an emphasis on ecosystem attributes identified in Gann et al. (2019), were retained. Studies focused on non-terrestrial ecosystems were removed. Consequently, I retained 53 and 28 publications for field and drone approaches. 132 6.2.1.2 Organization and Analysis I organized the identified studies by several key parameters: ecosystem attributes, biomes, geographic regions, successional stages, and the types of sensors used. Additionally, with a specific focus on data acquisition characteristics, I further categorized the studies based on: season of data acquisition, coverage of the study, types of field data included (including plant data, environmental data, and aerial observations), number of field plots, use of multi-temporal data, and, in the case of drones, spatial resolution. The ecosystem attributes used included overall vegetation cover, aboveground biomass, vegetation structure, species richness, species composition, plant type composition, spatial patterning/mosaic, indicator species, and invasive species. I also included topography, a key habitat characteristic that can be captured using drone-based remote sensing (Moudry et al. 2019; Poirier et al. 2020). Biomes included temperate deciduous forests, temperate and boreal coniferous forests, tropical forests, grasslands, shrublands (including woodlands), wetlands/riparian, savannas/deserts, and heathlands. A list of the publications and identified ecosystem attributes can be found in Table 6.1. Table 6.1 A list of reviewed research articles and identified ecosystem attributes: (1) fractional vegetation cover, (2) plant functional type composition, (3) indicator species, (4) invasive species, (5) productivity (health/biomass), (6) spatial mosaic, (7) species composition, (8) species richness, (9) micro topography, and (10) vegetation structure. Field-based studies Ecosystem attributes Drone studies Ecosystem attributes Bourgeois et al. 2016 3, 7 Albuquerque et al. (2022) 1, 3, 7 Bried (2013) 1, 3 Almeida et al. (2021) 1, 5, 7, 8 Charles et al. (2022) 1, 4, 7, 8 Beyer et al. (2019) 6, 7 Curran et al. (2019) 1, 2, 4 Blackburn et al. (2021) 1, 5, 6 De Stefano et al. (2021) 1, 2, 5, 8 Brooks et al. (2021) 4 Evju et al. (2023) 1, 3, 7, 8 Buters et al. (2019) 3, 5 Fleisher and Hufford (2020) 1, 7, 8, 10 Cao et al. (2018) 7 Franson et al. (2017) 1, 2 Chadwick et al. (2022) 3, 7 133 Gross and Vary (2014) 7 Chen et al. (2023) 1 Hessenmöller et al. (2013) 7 Cohen and Lewis (2020) 4 Jellinek et al. (2021) 1, 2, 3 Curcio et al. (2023) 1, 6, 7 Laughlin et al. (2017) 2, 5, 7, 8 Dong et al. (2019) 1, 4, 6 Low et al. (2012) 1, 7 Durgan et al. (2020) 1, 4, 7, 10 Maginel et al. (2016) 7, 8 Fernández-Guisuraga et al. (2018) 6 Mangachena and Geerts (2019) 2, 10 Fernandez-Guisuraga et al. (2022) 1, 6, 7, 10 Mazurczyk and Brooks (2022) 4, 7, 8 Filippi et al. (2022) 2, 7 McIntosh et al. (2019) 1, 2, 4, 7, 9, 10 Furukawa et al. (2021) 2, 10 McIntyre et al. (2019) 2, 10 Garms et al. (2020) 3 Nuzzo et al. (2017) 1, 7 Gomez-Sapiens et al. (2021)) 1, 2, 7 Perles et al. (2014) 1, 2 Goodbody et al. (2018) 1, 2, 6 Rochefort et al. (2013) 1, 2, 3 Green and Burkhart (2020) 3, 6 Rodgers et al. (2018) 4, 6 Hamylton et al. (2020) 1, 2, 3 Santala et al. (2016) 1, 3, 4, 7, 8 Hird et al. (2017) 1, 10 Stern et al. (2021) 4, 8 Iizuka et al. 2018) 2, 5 Tangen et al. (2022) 4, 7, 8 Ikkala et al. (2022) 6, 9 Thomson et al. (2021) 3, 4 Johansen et al. (2019) 2, 3, 5, 9, 10 Viani et al. (2018) 1, 3, 6, 8, 10 Khan et al. (2021) 1, 5, 6 Wilson et al. (2013) 7, 8 Knoth et al. (2013) 7 Laslier et al. (2019) 1, 6 Lishawa et al. (2017) 1, 6, 10 Matese et al. (2021) 5 Moudry et al. (2019) 9, 10 Nardin et al. (2021) 3, 5, 6, 7 Neumann et al. (2020) 5 Norris et al. (2022) 7 Nuijten et al. (2021) 6, 10 Padro et al. (2019) 1, 2, 6 Pi et al. (2021) 1, 3, 7 Reis et al. (2019) 2 Samiappan et al. (2019) 1, 10 Sankey et al. (2017) 7, 9, 10 Thomson et al. (2021) 6, 7 van Iersel et al. (2016) 5, 10 van Iersel et al. (2018) 2, 9, 10 Villoslada et al. (2020) 2, 5, 8 Wang et al. (2022) 2, 6, 9 Whiteside and Bartolo (2018) 5 Wilson et al. (2022) 2, 7 Woellner and Wagner (2019) 1, 2, 10 Zahawi et al. (2015) 1, 5, 10 Zhang et al. (2023) 2, 6 Zhou et al. (2021) 2, 7 Zhou et al. (2023) 3, 5 134 6.2.2 General Trends In total I analyzed 81 records (53 using drones and 28 using field-based approaches). Generally, the most commonly evaluated ecosystem attributes were vegetation cover, species composition, and plant functional types, along with the characterization of indicator and invasive species (see Figure 6.1C). However, drone studies more frequently assessed vegetation structure, spatial patterning, and topography and less on species richness. Publications assessed vegetation at various stages of succession, where pioneer (included by 49â57% of the papers) and intermediate (86â74%) stages were most common (see Figure 6.2). The most common biome for drone studies was wetland/riparian (38% vs. 31.5% of the drone and field-based publications, respectively), whereas shrublands were more prevalent in field-based studies (43% vs. 13%). Other commonly included biomes are grassland (23% vs. 14% of the drone and field-based publications, respectively), coniferous forest (21% vs. 18%), and deciduous forest (7.5% vs. 14%). 135 Figure 6.1 General trends in investigated drone- and field-based studies, including (a) the total of publications per year, (b) proportion of publications by continent, (c) proportion of publications by ecosystem attribute, and (d) proportion of papers by remote sensing sensor. Remote sensing technologies and data specifications vary among drone-based studies. The most commonly used sensors include RGB and multispectral, with less frequent use of hyperspectral, LiDAR, and thermal sensors (see Figure 6.1D). The majority of these studies include image data 136 with a resolution < 5 cm, accounting for 51% of the studies, followed by 5â10 cm (23%) and those greater than 10 cm (21%). Data collection in drone-based studies predominantly occurs during the growing season (51%), while only 17% and 15% of studies collect data during the spring and dormant seasons, respectively. I found distinct differences in the study area and design of the surveys between drone-based and field-based studies (see Figure 6.2). Only 30% of the studies using drones involved two or more data acquisitions, slightly less than the 36% observed in field-based studies. A notable 59% of drone studies were conducted on areas less than 50 hectares, whereas the coverage area for most field-based studies was not reported. In terms of sample plots, 24.5% of drone studies and 29% of field studies utilized fewer than 50 plots. However, 32% of drone studies and 64% of field studies reported using more than 50 samples, indicating a tendency for field studies to employ larger sample sizes. It is important to note the variability in plot design across these studies, encompassing quadrats, transects, or image annotations, among others. 137 Figure 6.2 Additional trends in investigated 53 drone- and 28 field-based publications. 138 6.3 Information Uncertainty and Complexity 6.3.1 Methods To assess the quality of information acquired, I utilized the concepts of information complexity and uncertainty. Complexity relates to the reproducibility of original data collection and processing workflows when applied to new contexts, including different biomes and species compositions, and is influenced by the resources available to the analyst (Sorenson et al. 2007). Information uncertainty pertains to the completeness, reliability, and accuracy of information (Traub et al. 1983) and can be influenced by physical factors such as sensor noise, or procedural errors in data processing, summarization, or visualization (Schunn and Trafton 2012). Studies report on information uncertainty in various ways. Some utilize metrics of model performance, while others discuss aspects of information uncertainty based on comparisons of sampling designs, multi-temporal data collections, and other empirical observations (Bried 2013; Laughlin et al. 2017; Thomson et al. 2021; De Stefano et al. 2021). This variety can pose challenges due to differences in methodological workflows, contexts, and the compatibility of accuracy metrics across studies. To address this I undertook a sentiment assessment to capture how publications report on information uncertainty and complexity, a method borrowed from market/consumer behavior research (Zhao et al. 2015). This involved manual coding based on a qualitative scheme, which covered nine ecosystem attributes (as listed in Figure 6.1), three evaluation criteria related to information uncertainty (accuracy, bias, and precision), and six criteria related to complexity (transparency, qualifications, costs, duration, transferability, interpretation), based on the subjects used by Reinhart (2010). Sentiments for each subject were rated on a Likert scale: positive sentiments were assigned a score of 1, neutral sentiments a score of 0.5, and negative sentiments 139 a score of 0, as in Zhao et al. (2016). For instance, regarding vegetation structure, examples include a negative sentiment such as 'weak correlations were likely due to the lack of height variation among passive treatment plots, most of which ranged between 1.5 and 4.0 m. DTM error was also proportionally greater in low stature measures', a neutral sentiment like 'we have shown that low-cost UAV-derived DSMs can be used to obtain the height of nonwoody floodplain vegetation in leaf-on conditions with an accuracy which is acceptable for many applications', and a positive sentiment such as 'Ecosynth canopy height models (CHMs) were strong predictors of field height regardless of whether a DTM was created directly from the Ecosynth point cloud or using an alternative algorithm'. To facilitate easy comparison of sentiments across ecosystem attributes and information characteristics (i.e., uncertainty and complexity), I employed a sentiment index (SI) as proposed by Luo et al. (2021). The sentiment index was calculated for each unique combination of ecosystem attribute (i) and information characteristic (j) as follows: ðððððððððððððððððð ðððððððððððð,ðð = â(ðððððððððððððððððððð,ðð)Total number of sentiment recordingsðð,ðð Text coding was performed using NVivo software to annotate relevant statements in PDF files and assign codes from the qualitative scheme (Lumivero 2023). Each annotated statement received multiple codes. However, NVivo does not support a relational database for text strings with multiple codes. Consequently, the coded statements were exported, and a relational database was 140 established by matching text strings using the RapidFuzz Python tool (Bachmann and Cohen 2024). 6.3.2 Ecosystem Attributes Drone studies generally show a neutral to positive stance on information uncertainty for most ecosystem attributes, particularly in plant productivity and vegetation cover, which exhibit less uncertainty (Figure 6.3). In contrast, field-based methods often reveal greater information uncertainty, except for species richness. This is attributed to factors like sampling design, plot density, surveyor crew changes, and plant phenology, which can introduce bias and inaccuracies (Rochefort et al. 2013; Bried 2013; Rodgers et al. 2018; Viani et al. 2018; Stern et al. 2021; De Stefano et al. 2021). Below, I discuss the information uncertainty for drone approaches related to the nine ecosystem attributes in Figure 6.3. Figure 6.3 Sentiment index (SI) for various ecosystem attributes related to information uncertainty and complexity for drone- and field-based studies. The number of the used text annotations and publications are included in parenthesis. 141 6.3.2.1 Fractional Vegetation Cover Vegetation cover, specifically of green vegetation against not vegetated surfaces, can be retrieved from drone data with high accuracy, receiving an average sentiment index (SI) of 0.7 based on 13 publications. Conversely, field-based approaches demonstrate lower sentiment scores. These studies highlight bias introduced from sampling design, the number of plots, and changes in botanical surveyors (Rochefort et al. 2013; Bried 2013; Viani et al. 2018; Stern et al. 2021; De Stefano et al. 2021). Lastly, drone studies showed positive sentiment (SI=1) towards information complexity, as vegetation cover is often calculated using straightforward methods like greenness index thresholding, without training data (Chen et al. 2023). Field studies report comparably high sentiment (SI=0.8); however, some acknowledge the labor intensity (Rochefort et al. 2013; Stern et al. 2021). 6.3.2.2 Composition of Plant Functional Types and Species Deriving reliable cover estimates (i.e., composition) for species and plant functional types from drone data can be more challenging compared to overall vegetation cover. Information uncertainty in mapping of plant functional types (SI=0.6) is perceived slightly more positive compared to that of species composition (SI=0.5). Vegetation classes, either comprising plant functional types or dominant species, are generally mapped from ortho imagery from RGB or multispectral sensors and typically encompass 3â9 classes (Durgan et al. 2020). Limitations and uncertainties may stem from similar spectral signatures among vegetation classes, noisy signals caused by reflectance from nearby surfaces, and constraints in sensor technology, like limited bands in multispectral sensors (Gomez-Sapiens et al. 2021; Wilson et al. 2022; Filippi et al. 2022; Durgan et al. 2020; Beyer et al. 2019). Some studies suggest that combining species, such as grouping by plant 142 functional type, generally yield higher accuracy (Knoth et al. 2013; van Iersel et al. 2018b; Cao et al. 2018; Beyer et al. 2019; Villoslada et al. 2020). In contrast, field-based studies showed negative to neutral sentiment (SI=0.3â0.5), reflecting the same challenges reported for vegetation coverânamely, required labor and timeâand emphasizing difficulties in accurately representing composition with limited samples (Rochefort et al. 2013; Viani et al. 2018). Species and plant type composition mapping showed neutral to positive responses on information complexity (SI=0.6â0.7). Required image classification is generally seen as computationally simple, but collecting training samplesâthrough manual aerial image annotation or matching field samplesâcan be time-consuming (Goodbody et al. 2018; Reis et al. 2019; Hamylton et al. 2020; Zhang et al. 2023; Nuijten et al. 2024). Moreover, species classification may require multispectral and hyperspectral sensors, or timing data collection with phenological phases, adding operational complexity and costs (Cohen and Lewis 2020; Durgan et al. 2020; Chadwick et al. 2022a; Fernández-Guisuraga et al. 2022). 6.3.2.3 Species Richness Several drone studies discuss information uncertainty related to species richness (SI=0.2), typically utilizing hyperspectral data and operating under the assumption that species diversity correlates with spectral variability and, to a lesser extent, structural variability (Palmer et al. 2002; Torresani et al. 2020). The success of these methods varied across ecosystems and datasets. For example, Villoslada et al. (2020) observed a slight correlation (Spearmanâs rho = 0.23) in a coastal meadow ecosystem, whereas Zhao et al. (2021) demonstrated accurate predictions of species diversity (R2 = 0.73) in grasslands with up to 20 species. Information uncertainty is further 143 influenced by saturation effects with an increasing number of species, and associations of spectral variation with other environmental conditions such as aboveground biomass (Villoslada et al. 2020; Almeida et al. 2021). Conversely, field studies received a slightly higher average sentiment score (SI=0.4). Lastly, drone studies did not report on information complexity, while field-based studies received a neutral sentiment (SI=0.6). 6.3.2.4 Individual Invasive and Indicator Plants Drones are effective in detecting species at the (near-)plant level, a capability particularly relevant for identifying indicator and invasive species (SI=0.6). Focused primarily on grass-like and shrub species, these studies employ RGB imagery alongside advanced image segmentation techniques. Notable successes include the detection of wetland plants like Zizania latifolia and Lomandra longifolia, young tropical trees such as Vismia and Cecropia, and conifer seedlings like Picea, with accuracies exceeding 85% (Hamylton et al. 2020; Chadwick et al. 2022a; Zhou et al. 2023). Similarly, invasive species, including various grasses (Phragmites australis, Paspalum urvillei) and deciduous shrubs (Frangula alnus), have been identified with accuracy rates ranging from 68â95% (Dong et al. 2019; Cohen and Lewis 2020; Durgan et al. 2020; Brooks et al. 2021). The sentiment score related to information complexity for individual plant identification was 0.5, attributed to the labor-intensive digitization of individual plants in images (Brooks et al. 2021; Chadwick et al. 2022a; Zhang et al. 2023). For field studies, the sentiment score for complexity was also 0.5, whereas the score for information uncertainty was 0.2, for reasons similar to those for measures of species/plant functional type composition (Section 6.3.2.2). 144 6.3.2.5 Vegetation Structure and Topography Vegetation structure along with micro topography can be effectively mapped using drone data, though uncertainty levels vary (SI=0.4â0.6). Drone-based LiDAR excels particularly in characterizing vegetation structures and micro topography beneath the canopy (Moudry et al. 2019; Ikkala et al. 2022). In contrast, imaging sensors and photogrammetric methods are limited to the upper canopy layers, relying on accurate ground detection and interpolation techniques to generate topographic data. The accuracy, and the ease, of ground detection emerge as concerns, influenced by the remote sensing technique used (photogrammetry or LiDAR), vegetation density, the season (leaf-on or leaf-off conditions), topographic variability, and the spatial distribution of detected bare ground (Zahawi et al. 2015; Sankey et al. 2017; van Iersel et al. 2018a; Moudry et al. 2019; Ikkala et al. 2022; Wang et al. 2022b). For similar reasons, micro topography received low sentiment towards information complexity in drone studies (SI=0.5). Nonetheless, photogrammetric approaches have demonstrated success in monitoring vegetation growth over time and distinguishing between vegetation structures in early regenerative forests (Nuijten et al. 2021; Furukawa et al. 2021). 6.3.2.6 Plant Productivity Information uncertainty in assessing plant productivity (or aboveground biomass) is positively perceived, with an average SI of 0.8. Traditionally, plant productivity is measured through destructive biomass sampling or by tracking atmospheric CO2 levels, indicative of net photosynthesis (Laughlin et al. 2017; De Stefano et al. 2021). In drone studies, extrapolation from destructive aboveground biomass samples is common. For grass and herbaceous-dominated ecosystems, this often involves vegetation indices (e.g., normalized difference vegetation index; 145 NDVI) derived from multispectral imagery (Zhou et al. 2023; Blackburn et al. 2021; Villoslada et al. 2020). Conversely, in treed ecosystems, canopy height models are typically employed (Zahawi et al. 2015; Almeida et al. 2021; Matese et al. 2021). These methods achieve generally accurate results, having R2 values ranging between 0.63 and 0.82, with most accurate results observed for trees (Almeida et al. 2021; Matese et al. 2021; Zhou et al. 2023). However, in herbaceous and grass-dominated environments estimates can be biased as NDVI can peak before biomass, while in autumn NDVI may decrease even as biomass levels remain stable (van Iersel et al. 2016). When examining information complexity in drone studies, plant productivity (SI=0.9) received a high average score. This is because aboveground biomass typically involves metrics derived from drone data that are relatively easy to calculate in combination with simple linear modeling (Zhou et al. 2023). However, drone studies, despite not focusing on related complexities, necessitate destructive sampling like field-based studies (SI=0.6). 6.4 Cost-Effectiveness Examined Through a Case Study: Reclamation in Alberta Amid concerns about the ambiguity stemming from information uncertainty and complexity and the potential costs associated with using drones for monitoring ecosystem recovery, the analysis delves into the cost-effectiveness of drone versus field-based methods for assessing ecosystem attributes. I conducted a case study in Alberta, Western Canada, centred on three reclamation sites associated with operational natural gas pipelines. I compared data from these sites to data from similar, wildfire-disturbed areas (see Section 2.2). 146 6.4.1 Methods 6.4.1.1 Information Acquisition I undertook four different scenarios incorporating field- and drone data acquisition approaches, as depicted in Table 6.2. The first scenario incorporated drone data collection, with a small botanical field survey component for fitting models to drone data. The second scenario repeats the drone approach from Scenario 1 but employs a more advanced sensor combination which reduces drone data acquisition time by 60% due to improved image capture rates and image resolution. In the third scenario a typical botanical field survey is conducted relatively quickly with a minimal number of plots (i.e., transects). Lastly, the fourth scenario implements a more intensive field survey, using a higher plot density, increased to simulate a scenario where the decision-maker requires information with low information uncertainty. 147 Table 6.2 Characteristics of the four scenarios assessed in the case study, including the number of field plots per data acquisition. Each data acquisition covers a pipeline right-of-way (ROW) and wildfire-disturbed site, each encompassing 20 hectares. Scenario Scenario 1: Drone-based approach Scenario 2: Advanced drone-based approach Scenario 3: Field-based approach Scenario 4: Intensive field-based approach Description of methods Drone-based data acquisition with a small in situ botanical survey component Drone-based data acquisition, employing a more advanced sensor combination which reduces acquisition time by 60% compared to Scenario 1, and a small in situ botanical survey component. Botanical surveys using in situ plots. Botanical surveys using in situ plots at a higher density compared to Scenario 3. No. of field transects (t) 12 (i.e., 120 quadrats) 12 (i.e., 120 quadrats) 20 (i.e., 200 quadrats) 24 (i.e., 240 quadrats) No. of ecosystem attributes 5 5 3 3 All scenarios involved gathering data on a limited set of ecosystems attributes, specifically species composition, plant functional types, and species richness. Drone-based scenarios additionally provided vegetation structure maps and insights on spatial patterning. These attributes were acquired for paired sites: a 20-hectare pipeline ROW and a wildfire-disturbed site, totaling 40 hectares per acquisition (a). The field sampling design was consistent across scenarios, varying only in the number of field plots (Table 6.2), with drone scenarios expected to require fewer plots. In these scenarios, plot data helped train models for mapping plant type and species composition (Chapter 4), while vegetation structure and spatial patterning were analyzed using unsupervised clustering and simple map-derived metrics (Chapter 3). Species richness was directly derived from field data in all scenarios. 148 Sampling was conducted using 2x2-m2 quadrats in all scenarios, where a two-person team recorded species, cover, height and location. The quadrats were spread across dominant vegetation types and grouped along 20-meter transects (t) to facilitate the visual validation of spatial alignment with drone imagery, minimizing alignment errors. Drone flights covered each site twice: once for RGB imagery (1â1.5 cm resolution) and again for 5-band multispectral imagery (2.2â3 cm resolution). Both the field and drone-based methods include data processing and analysis tasks, including data management (e.g., organizing plot data, storing data), data sharing, and conducting statistical analyses for generating site comparisons. However, drone approaches require additional processing tasks, such as photogrammetric processing, height normalization in the 3D point clouds, and various modeling tasks like unsupervised and supervised data classification (see Chapter 3 and Chapter 4). Moreover, the handling of field data becomes more time-consuming due to the steps necessary to enhance the precision of geo-spatial positioning of field samples to align with drone composite images. Data acquisition and processing tasks are described in more detail in Section 2.3.3. Estimates of fixed and variable processing times, based on experiences from the thesis research, are detailed in Table 6.3. Note that variable times are proportional to the number of site pairs, while fixed times remain constant. 149 Table 6.3 Time estimates for processing and analysis steps for a site pair of 40 ha including 12 transects. 1Time goes up proportionally to the number of transects (e.g., to 20 or 24 transects). Drone-based scenario (1 site pair; 40 hectares; 12 transects total) Field-based (1 site pair; 40 hectares; 12 transects total) Fixed times (hrs) Variable times (hrs) Fixed times (hrs) Variable times (hrs) Check and organize plot samples 2 2 2 11 Data management 4 0.5 2 0.251 Site summaries / comparisons 10 2 5 11 Photogrammetric processing 4 1.5 0 0 Data pre-processing 4 1.5 0 0 Create structure maps 12 0 0 0 Create plant func. type maps 12 0 0 0 Contingency time 0 0.5 0 0.251 Total hours 46 8 8 2.5 6.4.1.2 Cost Calculations Cost calculations were based on costs related to data acquisition, data processing and analysis, as well as miscellaneous costs (accommodation, daily expense allowances, and transport), as listed in Table 6.4. These were calculated using current rates (in $CAD) from consultant companies in Alberta's oil and gas sector (Table 6.5), alongside estimates of personnel and logistical needs based on labor time measurements and empirical evidence, as outlined and described in Section 2.3.3. While most costs are variable, data processing and analysis costs include a fixed component, as time required for some tasks, like setting up project directories and software, remains constant (Table 6.3). This allows for cost calculations per scenario, incorporating the number of transects (t) and the number of data acquisitions (a; i.e., site pairs): ð¶ð¶ðððððð = ððð¹ð¹ + (ð´ð´ + ðð + ðð) â ðð â ð ð 150 Table 6.4 A breakdown of fixed and variable costs in $CAD associated with four cost factors by scenario, to be used with the equation above. Each data acquisition (a) covers a pipeline right-of-way (ROW) and wildfire-disturbed site, each encompassing 20 hectares, with a variable number of transects (t). Scenario 1: Drone-based approach Scenario 2: Advanced drone-based approach Scenario 3: Field-based approach Scenario 4: Intensive field-based approach Data Acquisition (A) 611.7 489.2 385 385 Processing and Analysis (P) 110 110 34.4 34.4 Processing and Analysis; Fixed (PF) 7950 7950 1320 1320 Miscellaneous (M) 236.7 251.2 176.2 176.2 Table 6.5 Rates for labour, equipment and accommodation in $CAD. Cost Factor Unit Rate per Unit Labour (included in Data Acquisition or Processing/Analysis Costs) Field Technician Day $ 1200 Drone Pilot Day $ 1250 Data Analyst Day $ 1320 Equipment (included in Data Acquisition Costs) Drone Equipment Day $ 110 or $ 250 Field Equipment (e.g., GNSS, Tablets) Day $ 110 Accommodation (included in Miscellaneous Costs) Vehicle Day $ 150 Operating Costs of Vehicle Km $ 1 Hotel Night per Room $ 150 Per diem (i.e., Daily Allowance) Day per Person $ 300 By incrementally increasing the number of data acquisitions, I identified potential break-even points between scenarios, where the cost per hectare of the drone scenario becomes lower than that of the field-based scenario. To evaluate the stability (i.e., shift) of break-even points, cost estimates from Table 6.4 were adjusted within a specified uncertainty range, resulting in high and low cost estimates. Data acquisition and miscellaneous costs were varied by ±10% collectively, while 151 analysis costs were simultaneously adjusted by a larger margin of ±20%, reflecting their higher perceived uncertainty. Additionally, I assessed the sensitivity of the break-even points to individual cost factors by iteratively modifying each cost factor individually by ±10%. 6.4.2 Results 6.4.2.1 The Standard Drone Scenario versus Field-Based Scenarios Relative cost allocation, expressed in $CAD per hectare, varies as the number of data acquisitions increases (as depicted in Figure 6.4). For instance, in the standard drone scenario (Scenario 1), with one data acquisition covering a total of 40 hectares, total expenses for data processing and analysis account for 50.6% of the total. However, this figure drops to below 15.7% when the number of data acquisitions is increased to 20. Meanwhile, both field-based scenarios (Scenario 3 and 4) demonstrate relatively consistent cost allocations for data processing and analysis, ranging from 6.2% to 15.2% of total costs. Costs allocated for data collection, however, remain constant across all scenarios. 152 Figure 6.4 Cost per hectare, broken down by cost factor, across four different acquisition scenarios. These scenarios include (1) a standard drone, (2) an advanced drone, (3) a standard field-based, and (4) an intensive field-based scenario (4). I identified break-even points in the analysis, which occur when the cost per hectare of the drone scenario becomes lower than that of the field-based scenario. Specifically, the cost-effectiveness of the standard drone scenario (Scenario 1) surpasses both the standard and intensive field-based scenarios (Scenarios 3 and 4) after 22.6 and 2.5 data acquisitions, respectively, as shown in Figure 6.5. This corresponds to covering areas of 900 and 100 hectares, respectively. However, the advanced drone scenario has a relatively low break-even point regardless of the number of 153 sampling transects in the field approach (between 65â130 hectares). Lastly, the stability of these break-even points was confirmed through cost uncertainty ranges. In most scenario comparisons, the shifts in break-even points were limited to a maximum of 7â14%, with the exception being the comparison between the standard drone and field-based scenario (Scenarios 1 and 3). Figure 6.5 Cost ranges per hectare with uncertainty ranges and break-even points across four different acquisition scenarios. These scenarios include (1) a standard drone, (2) an advanced drone, (3) a standard field-based, and (4) an intensive field-based scenario. 154 To identify which cost factors most impact the break-even points, I individually adjusted each cost factor by ±10% (as illustrated in Figure 6.6), often leading to changes in the break-even point of more than 10%. It was observed that these points are particularly sensitive to changes in data processing and analysis costs. For example, increasing analysis costs by 10% requires an additional 11â42.4% data acquisitions to achieve cost-effectiveness comparable to field-based scenarios, varying with the specific scenario combination. On the other hand, increasing data acquisition or miscellaneous costs by 10% leads to an opposite effect, where drone scenarios become more cost-effective than field-based scenarios with fewer data acquisitions (shift of -2.5 to -15.6%). Lastly, note that cost increases and decreases are not symmetrical in their impacts; for instance, decreasing data acquisition or miscellaneous costs by 10% leads to shifts of 3.1â22.5%. 155 Figure 6.6 The relative change of break-even points following cost changes, where cost factors were iteratively changed by ±10%. Note that a break-even point is the number of data acquisitions where a drone scenario becomes more cost-effective than a field-based scenario. The contrasted scenarios include: a standard drone (1), an advanced drone (2), a standard field-based (3), and an intensive field-based scenario (4). 156 6.4.2.2 The Standard and Advanced Drone Scenario Compared The relative cost allocations among the three cost factors (data analysis, data acquisition, and miscellaneous) differs between the standard drone scenario (Scenario 1) and the advanced drone scenario (Scenario 2). For instance, at 20 data acquisitions (i.e., site pairs), the data acquisition costs account for 56% of the total expenses in the advanced drone scenario (Scenario 2), as opposed to 60% in the standard drone scenario (Scenario 1). Moreover, I identified the break-even points of the standard and advanced drone scenario with the standard field-based (22.6 and 2.9 data acquisitions, respectively) as well as intensive field-based scenario (2.5 and 1.4 data acquisitions, respectively). This indicates that the advanced drone scenario, with its different sensor setup, achieves higher cost-effectiveness compared to the field-based scenarios with fewer data acquisitions. 6.5 Opportunities and Operational Considerations for Use of Drone Remote Sensing for Ecosystem Recovery Assessments The reviewed studies, along with a case study, underscored key successes in identifying ecosystem attributes of recovering ecosystems with drone approaches; however, they also presented considerations relevant for information acquisition planning, related to uncertainty of information, its complexity, and cost-effectiveness. Moreover, a number of opportunities were identified related to the choice of remote sensing technology and computational workflows. 6.5.1 Information Uncertainty Technological advancements are addressing the challenges of information uncertainty in ecosystem attributes. Currently, digital aerial photogrammetry, using RGB or multispectral 157 sensors, is the most commonly used remote sensing technique in ecosystem recovery assessments, used in 86.8% of the reviewed studies. However, emerging technologies like hyperspectral sensors and LiDAR are gaining traction. LiDAR enhances detail in topography reconstructions (i.e., digital terrain models), especially in densely vegetated areas (Moudry et al. 2019). For example, in archaeology, drone-based LiDAR has helped effectively detect earthworks in forested regions (Poirier et al. 2020). It has also been valuable in peatland restoration monitoring, simulating local water flow in flat, high water table areas, and overcoming terrain model inaccuracies seen in digital aerial photogrammetry (Ikkala et al. 2022). Moreover, this potentially improves the accuracy of reconstructions of vegetation structures and aboveground biomass estimates, which are sensitive to elevation. However, the full potential of drone-based LiDAR in characterizing short-stature vegetation has yet to be fully explored. Hyperspectral imagery, consisting of a much higher number of bands arranged in narrower bandwidths (5 â20 nm) compared to multispectral imagery, shows promise in improving the ability to accurately identify plant species from drones (Villoslada et al. 2020). For instance, scotch broom (Cytisus scoparius), an invasive species found in North America's west coast savannas, can be accurately detected from hyperspectral imagery due to its distinct leaf nitrogen content (Hacker and Coops 2022). Detection using multispectral imagery would have relied on their phenology (yellow flowers) and may have led to confusion with other flowering plants (Low et al. 2012; Charles et al. 2022). Additionally, sphagnum moss, an important indicator of peatland recovery, but hard to distinguish from vascular vegetation in the visible (VIS) and near-infrared (NIR) regions, shows notable differences in the short-wave infrared (SWIR) region (Rochefort et al. 158 2013; Salko et al. 2023). These differences can be captured with a limited number of drone-based hyperspectral sensors. Further gains in accuracy may be achieved through the combined use of LiDAR and hyperspectral-derived variables, referred to as data fusion. Sankey et al. (2017) demonstrated increased overall accuracy in classifying five species in a shrubland-grassland meadow following data fusion. Height information from LiDAR improved the separation between plant species with overlapping spectral signatures. Additionally, Almeida et al. (2021) found that both drone-based LiDAR and hyperspectral derived variables were correlated with species richness in 13-year-old restoration plots in the Brazilian Atlantic Forest, which included up to 60 native tree species. These variables include LiDAR-derived leaf area index (LAI)âindicating leaf coverageâalong with spectral vegetation indices related to vegetation structure, and measures of spectral variability. The combined use of these technologies holds promise for addressing previous challenges in mapping species and estimating species richness, though further research is needed, especially in younger and more complex ecosystems. 6.5.2 Information Complexity Information complexity can be mitigated by streamlining workflows, exploring alternative sensor options, and capitalizing on advancements in drone technology. Noteworthy innovations in drone technology include the availability of plug-and-play sensor systems that seamlessly integrate with drone flight software and triggering systems, increased sensor capture speeds, and extended battery capacities (Coops et al. 2019). Furthermore, recent years have witnessed substantial enhancements in sensor spatial resolution. For instance, these improvements enable detailed volumetric 159 reconstructions through digital photogrammetry using multispectral imagery, eliminating the need for a second RGB sensor traditionally employed for this purpose (Buunk et al. 2023). Additionally, LiDAR sensors now yield denser point clouds, providing datasets that simplify terrain and small feature identification (Poirier et al. 2020). Collectively, these enhancements streamline data collection and extend coverage, a welcome development given that most reviewed studies covered small geographic extents. The choice of sensor can considerably impact both data acquisition and processing times. For certain tasks, such as the detecting of conifer seedlings from ortho imagery, RGB sensors and digital photogrammetry may be the most straightforward and cost-effective option (Chadwick et al. 2022a). However, when detailed information on vegetation structure is required, particularly in areas with dense vegetation or complex topography, the complexity of the workflow can increase. In such cases, identifying bare ground in point clouds and generating terrain models often involves iterative refinement and manual visual validation (Anders et al. 2019; Nuijten et al. 2021). To mitigate some of these complexities, one can opt for LiDAR sensors, which eliminate the need for photogrammetric processing and simplify terrain modeling (Zeybek and ÅanlÄ±oÄlu 2019; Teppati Losè et al. 2022). Furthermore, reducing information complexity can be achieved through improvements in open-source software and protocols. While various digital photogrammetry software options are available, they often come with steep learning curves. Understanding the intricacies of their underlying algorithms and their interactions with image parameters like overlap, field-of-view, vegetation types, and atmospheric conditions can be challenging, partly due to the proprietary 160 nature of most software. Notable contributions include the development of open-source digital photogrammetry software like WebOpenDroneMap (Vacca 2019), improved image acquisition and processing protocols (Chadwick et al. 2022b; Cunliffe et al. 2022), and the use of digital image annotation tools for training image classification and plant detection models. Notably, DroneDB and Project Kiwi have been introduced as web-based annotation tools for drone data, designed to accommodate users with limited experience in geographic information systems, thereby making this task accessible for a wide range of plant specialists (Thoreau 2022; Toffanin and Di Leo 2024). 6.6 Conclusions The review has presented general trends in the applications of drone-based remote sensing for assessing ecosystem recovery, covering various ecosystem attributes relevant to ecological restoration projects. I have highlighted key considerations for decision-makers, concerned with completing ecosystem assessments, regarding information uncertainty. For instance, in estimating vegetation cover and aboveground biomass, uncertainties were found to be low, whereas uncertainty remains relatively high for species mapping and estimating species richness. The promise of hyperspectral and LiDAR technologies, as well as advanced machine learning techniques capable of recognizing intricate plant shapes and textures, calls for more research. My work also suggests that the impact of ecosystem characteristics, such as vegetation density and species diversity, on the ability of drone technology to characterize ecosystem attributes requires further investigation. Furthermore, simplifying data collection and processing workflows is a critical avenue for overcoming the hurdles in information complexity. This can be achieved 161 through the development of best practices guides and new open-source tools, for example, for streamlining image annotation (for model training and validation). Lastly, the cost-effectiveness analysis demonstrated the economic advantage of drone-based methodologies over conventional botanical surveys, especially for extensive areas beyond 100 hectares. Furthermore, my work showed that using cameras with faster data capture rates can further increase their cost-effectiveness, highlighting the importance of including such information in a projectâs cost calculations. In conclusion, I have discussed avenues for reducing information uncertainty and complexity, which includes opportunities related to advancements in sensor technology and the simplification of data collection and processing workflows. This investigation aims to promote broader adoption of drone-based remote sensing in ecosystem recovery assessments and to encourage interdisciplinary collaboration, especially in areas where costs, information uncertainty, and complexities can potentially be effectively managed and reduced. 162 Chapter 7: Conclusions 7.1 Dissertation Objectives The primary objective of this dissertation was to explore the role that drone-based digital aerial photogrammetry (DAP) can play in offering spatially explicit, valuable, and cost-effective insights into ecosystem recovery through the characterization of a comprehensive array of ecosystem attributes. This exploration was conducted within a reclamation context in forested areas in Alberta. This overall objective was addressed by answering the four research questions posed in Chapter 1, as detailed below. Q1: To what extent can the structure of early regenerative vegetation be characterized using DAP? Approach: In Chapter 3, I validated DAP heights for various plant functional types and developed a workflow for mapping structure types. Key steps included: (1) Extracting metrics representing vegetation cover, vegetation height, height variability, and canopy surface complexity; (2) Conducting unsupervised clustering analysis; and (3) Evaluating the results through clusterâ statistics and field data (obtained from 1x1 m2 quadrats), revealing the limitations of the data and workflow. Main findings: Chapter 3 demonstrated strong correlations (rs > 0.75) between DAP heights (all < 2 m) and field heights for shrubs, forbs, and conifers. However, the correlation was poor (rs = 0.26) for grass-like vegetation. DAP consistently underestimated heights, with RMSE ranging 163 from 38 cm for forbs to 58 cm for conifers. Despite this, DAP proved suitable for mapping various structure types and identifying patterns. Using unsupervised clustering, I mapped six (or seven, including vegetation > 2 m) structure types. Notably, three types could be well separated based on metrics of canopy surface complexity and maximum height: 1. Tall and complex structures (typically woody vegetation like willows). 2. Mid-tall vegetation structures with low to moderate height variability (tall forbs and grass-like vegetation). 3. Short vegetation structures with low to moderate height variability (short forbs and grass-like vegetation, including clovers). Obtaining classes for shortest vegetation structures, which included conifer seedlings, mosses, and short grass-like vegetation, proved more limited, in line with expectations. This is demonstrated by the overlap between other classes in terms of the plant functional types (i.e., life forms) observed within. Q2: To what extent can keystone plant communities be mapped using DAP derived spectral and structure data? Approach: Presence-absence models were developed in Chapter 4 for various plant species (or aggregated species, at the genus or family level) and plant functional types using information from field quadrats (2x2 m2) and various DAP-derived variables. This approach facilitated the investigation of the most crucial DAP variables on a model/species basis, as well as the limitations inherent in the data and workflows. Moreover, this facilitated comparisons between models for different species and plant functional types. Finally, discrete vegetation maps were generated by applying the models across sites and comparing estimated probabilities. 164 Main findings: Previous research typically focused on mapping a limited number of classes (ranging from 3 to 7) using DAP data, often choosing plant functional types over species. Chapter 4 demonstrates that the performance of presence-absence models tends to be more accurate for plant functional types (with an average F-score of 0.67) compared to models for individual species (0.57), aligning with my expectations. Notably, model performance was low for asters (average F-score = 0.48), spruces (0.54), willows (0.55), and grasses (0.46). The final maps of species and plant functional types included 9 and 7 classes, respectively. However, due to the low performances of some of species' models, further use of only the map of the plant functional types (7 classes) may be preferred. The most important predictor variables generally included vegetation structure types (from Chapter 3), spectral variation (e.g., standard deviation of various spectral indices), and the mean value of the NIRv spectral index. Observations and discussions from Chapter 4 suggested that the importance of spectral variation may be attributed to phenological events observed in certain species as well as habitat characteristics, such as exposed soil and co-occurring species. Lastly, the investigations underscored the potential of drone-data-derived variables that describe habitat conditions. Variables depicting solar illumination throughout the growing season were found to be crucial for certain models, including those for bryophytes, spruces, shrubs, and conifer seedlings. Q3: To what extent can conventional field sampling and drone maps be used to investigate how vegetation across pipeline ROWs mimic regeneration patterns following wildfire, and what are their intrinsic differences? 165 Approach: Previous research has underscored the potential of using proximate and appropriate reference sites in restoration assessments. In Section 2.2.2 I postulated that wildfires would be most appropriate in the context of reclamation in Alberta, as they enable comparisons with references at similar stages of succession, are naturally prevalent in the landscape, and have potential for rapid recovery of native plant species richness and cover under optimal circumstances. Chapter 5 first demonstrated how statistics established in ecology can be applied to drone maps to compare species composition. Second, the information differences between field sampling and drone approaches were highlighted, as well as the additional information drones provide. Lastly, Chapter 5 further discussed the appropriateness of using wildfires as a reference, and provided a proof-of-concept for identifying areas within wildfire sites that had deviating plant compositions (as determined from drone maps), with the aim of refining the reference model. Main findings: Chapter 5 revealed a significant disparity in evenness measures between plot data and drone maps, highlighting the limitations of relying solely on field plots. However, when ranking site pairs based on dissimilarity in plant functional type composition, the two datasets were in agreement. The Chapter illustrated how drone plant community maps offer nuanced insights into competitive and regeneration potentials through spatial patterning. For example, one ROW site (Chinchaga B) exhibited limited cover of shrubs but demonstrated regeneration potential through spatial cues, showing their patches were relatively large and positioned closely together. On the other hand, at ROW Chinchaga B, grasses and low-growing forbs demonstrated relatively large average patch sizes, while patches were located relatively close together (Euclidean distances. This underscores their dominance and connectivity, which could be an indicator of agronomic communitiesâ presence. Lastly, our analysis revealed spatial variability in plant 166 functional types across wildfire reference sites. Notably, the Chinchaga wildfires had the largest continuous grass patch, with tall forbs and shrubs also forming large, continuous patches. This confirmed my expectancy of high spatial heterogeneity of plant communities in wildfire-disturbed areas. Together with the limited availability of spatially explicit information on pre-disturbance conditions and variations in disturbance severity, this may hinder their effectiveness as references in restoration models. Q4: How do drone-based remote sensing approaches compare to conventional field surveying in terms of information uncertainty, complexity, and cost-effectiveness, and to what degree can these information characteristics be improved? Approach: Chapter 6 examined information characteristics relevant to decision-makers involved in the assessment of recovering ecosystems in reclamation/restoration contexts. The aim is to enhance the understanding of drone technology limitations and potential in comparison to field-based approaches, potentially stimulating the uptake of technology where appropriate. Chapter 6 reviewed general characteristics of relevant drone and field-based studies. Two analyses followed. First, a sentiment analysis on literature contents evaluated sentiments on information uncertainty and complexity by ecosystem attribute. Second, a cost-effectiveness analysis was performed in a case study from Alberta focusing on the spatial coverage of information. Main findings: The sentiment analysis conducted in Chapter 6 revealed uncertainties in ecosystem attribute information derived from drone and field-based approaches. Field plots were found to be more reliable for assessing species richness, given the high information uncertainty associated with drones. Conversely, drones demonstrated low information uncertainty for plant productivity 167 (biomass) and fractional vegetation cover. Certain ecosystem attributes can be captured with minimal uncertainty through drone remote sensing, but their variability was found to depend on ecosystem conditions, selected sensors, and processing/analysis workflows. These attributes include vegetation structure, micro topography, invasive species, indicator species, and species/plant type composition. The complexity of information acquisition is influenced by drone cameras, platforms, and chosen workflows, posing critical considerations and trade-offs for decision-makers. Furthermore, a cost-effectiveness analysis indicated potential savings at larger extents (> 100 hectares) with drone compared to field approaches. 7.2 Research Innovations and Significance This dissertation provided several innovations contributing to enhancing the knowledge on the potential of drone-based DAP data for assessing recovering ecosystems in the context of reclamation and restoration: â¢ Drone-based DAP's capability to characterize types of vegetation structure in early regenerating wetlands/forests, having high structural complexity, was not well known at the start of the PhD research. Chapter 3 showed that DAP height estimates correlate with field heights, but also exhibit strong bias. Additionally, Chapter 3 demonstrates the potential and limitations of such data sets for further mapping of vegetation structure types. This dissertation, along with other works published throughout the course of my research (Castilla et al. 2020; Chadwick et al. 2020; Fernández-Guisuraga et al. 2022; Evans et al. 2022), has contributed to the depth of knowledge on DAP capabilities. â¢ Prior to the dissertation, there was limited knowledge about the capabilities of DAP to map a wide range of species, particularly in complex early regenerating forests/wetlands. 168 Chapter 4 contributed to an improved understanding of the related limitations and challenges. My research showed that the classification of vegetation types may be more accurate than for species and provides insights on the number of classes to use (based on models using uniformly aggregated pixels). In addition, Bradter et al. (2020) suggested classifications of functional types yield higher accuracies than those of dominant species using 1-m resolution airborne imagery in grassland habitat, however, this assumption was, to my knowledge, not been tested using drone DAP data and in other environments. â¢ Chapter 4 contributed to knowledge on the sensitivities of metrics of spectral variation and their usage for mapping vegetation/habitat classes. Previously, spectral variation has mainly be used for estimating species richness and biomass (Villoslada et al. 2020; Almeida et al. 2021; Fassnacht et al. 2022). â¢ In Chapter 5, a pioneering framework is introduced to ecologists for evaluating ecosystem recovery with drones and reference sites (i.e., analogues). Moreover, the analysis, centered on ROW and wildfire-disturbed sites in Alberta, provided crucial insights into differences and similarities in information derived from conventional field-based methods and drone-based approaches. Finally, the chapter underscored the advantages of spatial patterning information in understanding plant community dynamics concerning competition and regeneration. â¢ The uptake of drones for ecosystem recovery assessments was found to be limited (Robinson et al. 2022; McKenna et al. 2023). This limitation may be attributed to a lack of familiarity with the complexities of data acquisition and processing, uncertainty of information, information comprehensiveness, and cost-effectiveness. Chapter 6 delves into these information characteristics, aiming to aid managers in the decision-making process 169 regarding information acquisition and encouraging wider adoption of drones. The first analysis in Chapter 6 examines information uncertainty and complexity, focussing on a range of ecosystem attributes derived from drone and field-based approaches, through a systematic literature review. The second analysis contributed to a better understanding of the costs associated with drone information acquisition, with a focus on economies of scale (i.e., spatial extent), through cost-effectiveness calculations. 7.3 Research Limitations and Future Directions Methods and products derived in this dissertation are associated with a range of limitations of technical, logistical, or financial nature. The main limitations that were encountered and possible future directions to address them are listed below. â¢ Limitation: In Chapter 3, technological limitations of DAP related to capturing vegetation structure were highlighted. The consistent underestimation of DAP heights and the observed weak correlation between DAP heights of grass-like vegetation and field heights were notable findings. Furthermore, limitations in 3D reconstructions were identified, especially with regard to conifer seedlings, which posed challenges in visually distinguishing them from other structure types in clustering analysis, contrary to expectations. Influential factors affecting the quality of reconstructions include image resolution (dependent on flight altitude and camera resolution), illumination conditions, and wind. Some of these factors, along with considerations related to vegetation type and density, may sometimes hinder accurate terrain modelingâa critical component in existing workflows. 170 Future directions: Chapter 3 discusses various papers proposing flight parameters potentially improving results. However, as most measures would considerably increase flight times, there is a need to find optimal flight parameters that strike a balance between data accuracy and cost-effectiveness. Furthermore, the advancement of drone LiDAR during the course of this dissertation, capable of capturing terrain beneath vegetation canopies, likely have made this technology suitable for characterizing the structures of short-stature vegetation. Note that prior to this research, LiDAR sensor costs were relatively high, and the point densities in the generated point clouds were generally lower than those obtained from DAP (which could have resulted in the omission of, for instance, the tops of conifer seedlings). â¢ Limitation: The performance of the presence-absence models developed in Chapter 4 (utilizing field quadrats as training data) as well as the representativeness of field-based species richness/evenness estimates presented in Chapter 5 (directly derived from the field quadrats) were both limited by my field sampling design. In an effort to mitigate spatial alignment errors between DAP and field data, quadrats of 2x2 m² were arranged along transects. However, this decision introduced a trade-off, as it limited the spatial distribution of quadrats and optimal capture of species richness. Lastly, inherent disparities between field observations and image data exist; for example, some species may only be visible to a limited extent in drone imagery due to occlusion. Future directions: The identified limitations underscored the need for future applications to adapt multiple specialized sampling designs. This includes one tailored to optimally 171 capture species richness, as in De Stefano, Fowers, and Mealor (2021) and Bried (2013), and one for mapping vegetation species/functional types. For mapping discrete classes, samples may be best collected digitally from very high-resolution drone orthomosaics. However, to ensure accessibility for ecologists/botanist without technical experience and prevent human error during image interpretation, the development of protocols and software specifically tailored to drone data is needed. These tools should take advantage of the multiple dimensions of drone data, incorporating point clouds, orthomosaics, and variables presented throughout the dissertation, in a user-friendly system. Such innovations may improve cost-effectiveness, mitigate alignment errors, and enable the improvement of spatial distribution of samples. â¢ Limitation: The assessment of individual ecosystem attributes in this dissertation provided limited insights into ecosystem resilience. Resilience is defined as an ecosystem's capacity to resist and recover from stresses while maintaining its functioning, structures, and identity (Jackson et al. 1995). This limitation stems from the data representing only a snapshot in time. To fully understand ecosystem resilience, it is crucial to consider the temporal variability of attributes, like species/plant type composition, at pipeline ROW and reference sites. Moreover, habitat conditions may have varied considerably within analogue sites, as suggested by vegetation patterns observed in Chapter 5. Future directions: Hiers et al. (2012) introduced the Dynamic Reference Concept, which captures temporal and spatial variations and quantifies changes in species composition at reference and restoration sites. Hiers et al. (2012) use multivariate statistical methods 172 (specifically NMDS) for comparative species composition assessments between restoration and reference sites. Sites staying within the reference conditions' 90% confidence interval over time are considered more resilient. Future drone-based studies may include multi-temporal data from reference sites to enable such analysis. In addition, this approach can help to refine references in restoration as demonstrated in a proof-of-concept in Chapter 6:. To accomplish this, reference site segments can be compared to the confidence interval of reference conditions, following a similar process as described earlier, and filtered iteratively. This aids in excluding sections of wildfires that significantly differ in terms of species/plant type composition from the majority of wildfire references. â¢ Limitation: The dissertation did not assess certain ecosystem attributes (e.g., species richness), individual plants (e.g., seedling density), and characteristic species/plant types relevant to reclamation assessments in Alberta. This omission primarily stemmed from the limitations of the data sets and the chosen sampling and classification approaches. Specifically, the classification faced challenges due to the selected unit of analysis. The resolution (2x2 m²) led to the inclusion of reflectance from other species and surfaces, and the regular grid used for classification failed to fully capitalize on the shape and edges of plant species/types identifiable in very high-resolution RGB image data. Regarding sensor characteristics, the spectral signatures of plants observed from multispectral cameras may have been too similar between plant species/types, related to the cameraâs spectral resolution as well as variance within species (including phenology) as highlighted by Salko et al. (2023). 173 Future directions: Chapter 6 shows that drone studies on ecosystem attributes not covered in this dissertation are ongoing, covering attributes like biomass, species richness, site characteristics (e.g., micro topography, surface moisture), and the detection of individual plants (e.g., invasive species, seedlings). Some studies involve more advanced drone sensors. For instance, the combined use of LiDAR and hyperspectral data may hold promise for estimating species richness and aboveground biomass in early regenerating wetlands/forests in Alberta based on findings from Almeida et al. (2021). Additionally, methods that delineate vegetation using spectral similarity, shape and texture are being developed and applied. This includes object-based image analysis (OBIA) approaches, which involves the generation of image segments (i.e., superpixels) followed by supervised classification (Knoth et al. 2013; Cao et al. 2018; Filippi et al. 2022), as well as more advanced instance segmentation approaches like convolutional neural networks (CNN) which learn from annotated imagery at similar sophistication as the human visual cortex (Qian et al. 2020; Chadwick et al. 2022a). However, CNNs require extensive image labeling and model training, which incurs upfront costs. Despite this, they may further increase cost-effectiveness at larger scales. 7.4 Closing Statement Decision-makers concerned with completing ecosystem assessments, following ecological restoration or reclamation, are increasingly involved in continuous monitoring and mitigating environmental concerns. However, spatially explicit, detailed, and accurate information on ecosystems is needed to characterize the status and recovery of ecosites. This dissertation improves the understanding of the capabilities and limitations of DAP in assessing ecosystem attributes of 174 recovering ecosystems in Northwest Alberta's forested landscapes, following pipeline installation and wildfires. It examined the extent to which DAP can characterize and map the structure and species diversity from technical and operational perspectives. Moreover, a literature review explored the broader potential of drones across various ecosystem attributes and drone remote sensing technologies. 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Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-ShareAlike 4.0 International
Graduate
Coops, Nicholas C.
Characterizing ecosystem attributes relevant for restoration assessment in early successional forests using drone-based remote sensing approaches
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