Science, Irving K. Barber Faculty of (Okanagan)
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
UBCO
Scinocca, Michael
Master of Science - MSc
University of British Columbia
Riparian forests are integral components of landscapes due to their ecological significance and the services they provide to both terrestrial and aquatic ecosystems. These unique ecosystems act as buffers, regulating water quality, stabilizing streambanks, providing habitat for diverse flora and fauna, and influencing local microclimates. Accurate assessment and classification of riparian forests are essential for effective land management and conservation efforts, especially in regions like the Okanagan Basin, where water resources are particularly vulnerable to anthropogenic impacts and climate change. Recent advancements in remote sensing (RS) technologies, particularly the integration and availability of multispectral and LiDAR data, offer opportunities to predict and delineate riparian forest composition at larger scales and over potentially inaccessible terrain, enabling comprehensive assessments of these ecosystems' spatial distribution, structural attributes, and species composition. This study integrated fine-scale RGB RS imagery with LiDAR data to classify riparian forest tree species in select fish-bearing streams within the Okanagan River system, to develop a reproducible and cost-effective process for predicting ten native tree species classes and one infrastructure class. To inform the random forest (RF) classification model, a total of 23 explanatory covariates created from LiDAR and RGB data were incorporated. The model was trained using 1,611 observations, collected through in-situ field visits and supplemented by open-access tree inventory data, sourced from various reaches of the study area, encompassing diverse locations, elevations, and ecosystem types. The RF model achieved an overall accuracy of 96.03%, evaluated through a confusion matrix. In the 100 m buffers used to describe riparian environments of the study area, (129,802 ha), 85,679 ha (66% of the area) were classified into 11 classes. The spruce class was the most predicted class (54.16% of classified cells), followed by western redcedar (15.01%), and lodgepole pine (7.46%). Of the 23 explanatory covariates used, LiDAR-derived covariates were found to be more influential than RGB-derived covariates. This research provides valuable insights for land management strategies and conservation efforts, especially in regions like the Okanagan Basin, where water resources are particularly vulnerable to anthropogenic impacts and climate change.
Classifying Riparian Forests of the Okanagan Basin, British Columbia, Canada, through Random Forest Modelling with LiDAR and Spectral Derived Parameters by Michael F. Scinocca B.Sc., University of Victoria, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Earth and Environmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2024 © Michael F. Scinocca, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Classifying Riparian Forests of the Okanagan Basin, British Columbia, Canada, through Random Forest Modelling with LiDAR and Spectral Derived Parameters submitted by Michael F. Scinocca in partial fulfillment of the requirements of the degree of Master of Science. Dr. Mathieu Bourbonnais, Department of Earth, Environmental and Geographic Sciences Supervisor Dr. Hillary Ward, BC Ministry of Water, Land and Resource Stewardship Supervisory Committee Member Dr. Lael Parrott, Department of Biology, Earth, Environmental and Geographic Sciences Supervisory Committee Member Dr. John Richardson, Department of Forest and Conservation Sciences University Examiner iii Abstract Riparian forests are integral components of landscapes due to their ecological significance and the services they provide to both terrestrial and aquatic ecosystems. These unique ecosystems act as buffers, regulating water quality, stabilizing streambanks, providing habitat for diverse flora and fauna, and influencing local microclimates. Accurate assessment and classification of riparian forests are essential for effective land management and conservation efforts, especially in regions like the Okanagan Basin, where water resources are particularly vulnerable to anthropogenic impacts and climate change. Recent advancements in remote sensing (RS) technologies, particularly the integration and availability of multispectral and LiDAR data, offer opportunities to predict and delineate riparian forest composition at larger scales and over potentially inaccessible terrain, enabling comprehensive assessments of these ecosystems' spatial distribution, structural attributes, and species composition. This study integrated fine-scale RGB RS imagery with LiDAR data to classify riparian forest tree species in select fish-bearing streams within the Okanagan River system, to develop a reproducible and cost-effective process for predicting ten native tree species classes and one infrastructure class. To inform the random forest (RF) classification model, a total of 23 explanatory covariates created from LiDAR and RGB data were incorporated. The model was trained using 1,611 observations, collected through in-situ field visits and supplemented by open-access tree inventory data, sourced from various reaches of the study area, encompassing diverse locations, elevations, and ecosystem types. The RF model achieved an overall accuracy of 96.03%, evaluated through a confusion matrix. In the 100 m buffers used to describe riparian environments of the study area, (129,802 ha), 85,679 ha (66% of the area) were classified into 11 classes. The spruce class was the most predicted class (54.16% of classified cells), followed by western redcedar (15.01%), and lodgepole pine (7.46%). Of the 23 explanatory covariates used, LiDAR-derived covariates were found to be more influential than RGB-derived covariates. This research provides valuable iv insights for land management strategies and conservation efforts, especially in regions like the Okanagan Basin, where water resources are particularly vulnerable to anthropogenic impacts and climate change. v Lay Summary Riparian forests, composed of streamside vegetation, play an essential role in maintaining water quality and supporting diverse wildlife. Using advanced remote sensing technology like LiDAR and multispectral data, this research accurately mapped tree species within select riparian forests of the Okanagan Basin. By identifying different tree species and assessing their distribution, the study exemplifies the use of remote sensing applications for effective land management and conservation efforts. Understanding the species distribution and abundance in riparian forests can guide future conservation strategies in protecting these valuable ecosystems from human impacts and climate change. vi Preface This thesis and research were conducted by Michael Scinocca under the supervision of Dr. Mathieu Bourbonnais at the University of British Columbia, Okanagan. The research, data analysis, and thesis writing were all carried out by Michael Scinocca, supervised by Dr. Mathieu Bourbonnais, and reviewed by Dr. Lael Parrott and Dr. Hillary Ward. Generative artificial intelligence was not used in the research, drafting, or preparation of the thesis. It is intended that chapter two will be submitted for peer-reviewed publication. All data analysis was completed using R. R. Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. vii Table of Contents Abstract .................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface.........................................................................................................................................vi Table of Contents ................................................................................................................... viii List of Tables ............................................................................................................................ ix List of Figures ........................................................................................................................... x Acknowledgements ................................................................................................................ xii Chapter 1: Introduction ............................................................................................................ 1 1.1 Riparian ecosystems .................................................................................................... 1 1.2 Riparian threats and losses .......................................................................................... 5 1.3 Riparian management .................................................................................................. 7 1.4 Remote sensing applications for riparian assessment .................................................. 9 1.4.1 Multispectral imagery ................................................................................. 11 1.4.2 Light detection and ranging ........................................................................ 13 1.5 Machine learning classification methods ..................................................................... 15 1.5.1 Random forest ........................................................................................... 16 1.6 Canadian and British Columbian riparian management .............................................. 17 1.7 Okanagan riparia ........................................................................................................ 21 1.8 Research statement ................................................................................................... 24 1.8.1 Problem and significance ........................................................................... 24 1.8.2 Goals and objectives .................................................................................. 24 Chapter 2: Random forest classification of riparian forests in select tributaries of the Okanagan, British Columbia, using remote sensing applications ...................................... 27 2.1 Background ................................................................................................................ 27 2.2 Study area .................................................................................................................. 29 2.3 Data............................................................................................................................ 34 viii 2.3.1 Training Data ............................................................................................. 34 2.3.2 LiDAR ........................................................................................................ 37 2.3.3 RGB Imagery ............................................................................................. 37 2.3.4 Ancillary data ............................................................................................. 38 2.4 Methods ..................................................................................................................... 38 2.4.1 LiDAR metrics ............................................................................................ 38 2.4.2 RGB imagery indices ................................................................................. 40 2.4.4 Random Forest .......................................................................................... 44 2.4.4.2 Random forest hyperparameter tuning ........................................ 46 2.4.4.3 Model evaluation ......................................................................... 47 2.4.4.4 Model prediction .......................................................................... 48 2.5 Results ....................................................................................................................... 48 2.5.1 Model performance .................................................................................... 48 2.5.2 RF model variable importance ................................................................... 49 2.5.3 Partial dependence plots ............................................................................ 50 2.5.4 Riparian vegetation classification output .................................................... 55 2.6 Discussion .................................................................................................................. 60 2.7 Conclusion .................................................................................................................. 66 Chapter 3: Conclusion ............................................................................................................ 67 3.1 Summary of research ................................................................................................. 67 3.2 Key Findings...............................................................................................................71 3.3 Future research ..........................................................................................................72 Bibliography ............................................................................................................................ 75 Appendix ................................................................................................................................. 90 ix List of Tables Table 1.1. Simplified stream class designation in British Columbia, based on channel width and stream presence, for the determination of riparian reserve and management zone buffer widths. This table was simplified by Tompalski et al. (2017), and originally sourced from Pike et al. (2010b). â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦ 20 Table 1.2. The size (in ha) of Okanagan watersheds considered, ordered from north to south based on their stream outlets, and separated based on which side of the Okanagan basin they occur on. â¦â¦â¦â¦â¦â¦...â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦. 26 Table 2.1. The number of trees included in the training database, as well as other relevant information. All tree coordinates (1467 features total) are located within the study area watersheds. Tree observations were collected from field work and existing geospatial databases. An additional 144 infrastructure class observations are not listed. â¦â¦â¦â¦.. 36 Table 2.2. Eighteen covariates, including seven LiDAR metrics and 11 RGB indices, that were included in the RF model (Pearson correlation coefficient < 0.7). LiDAR return attributes included metres above ground (z), intensity (i), and return number (rn). RGB Imagery bands included red (R), green (G), and blue (B), where each index was calculated over nine rasters each representing a month between March and November, allowing the mean (Mean), the minimum (Min), and the coefficient of variation (CoVar) to be calculated for each index. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.... 42 Table 2.3. Hyperparameter gird search ranges and selected values used to train the RF classification model. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦..... 47 Table 2.4. A confusion matrix visualizing the prediction ability of the RF model over each class. This confusion matrix finds the overall accuracy of the model, as well as the user accuracy (UA) and producerâs accuracy (PA) for each class1. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦... 49 Table A1. 57 covariates, (18 LiDAR metrics and 39 RGB imagery indices) considered for the RF classification model, were derived within raster cells of 3 m spatial resolution. 23 covariates were included within the RF classification model after correlation was assessed (Pearson's correlation coefficient >= 0.7), and are identified in bold within the covariate column. LiDAR return attributes included metres above ground (z), intensity (i), return number (rn), and classification (c), which includes âunclassifiedâ (c = 1), âgroundâ (c = 2), and ânoiseâ (c = 7). RGB imagery bands included red (R), green (G), and blue (B), where each index was calculated over nine rasters each representing a month between March and November, allowing the mean (Mean), the minimum (Min), and the coefficient of variation (CoVar) to be calculated for each index. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.. 90 x List of Figures Figure 2.1. The study area includes 24 tributaries of the south-flowing Okanagan River system within the Okanagan Basin, BC. These tributaries are fish-bearing (Ministry of Forests Lands Natural Resource Operations and Rural Development, 2018; Rae, 2005), which signals the importance of monitoring and managing the riparian vegetation within these watersheds (Okanagan Nation Alliance, 2017). â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.â¦..... 33 Figure 2.2. Workflow of the Random Forest classification model for Okanagan riparian forests, BC, Canada, showing how acceptability was assessed based on (a) adequacy of confusion matrix, variable importance, and partial dependence plots in demonstrating prediction ability for tree species, and (b) the ability of output classification to reflect expected tree species distribution. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦. 45 Figure 2.3. Variable importance (VI) plot displaying each of the 23 covariates used in the model on the x-axis, and the associated score calculated by weighting the sums of Gini impurity scores from the nodes of the decision trees in the RF model. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.. 50 Figure 2.4. Partial dependence plots (PDPs) that display each of the eleven classes of black cottonwood (Act), trembling aspen (At), subalpine fir (Bl), western redcedar (Cw), interior Douglas-fir (Fdi), western larch (Lw), lodgepole pine (Pl), ponderosa pine (Py), spruce (S), willow (W), and infrastructure (IFST), for each of the eight most important covariates based on the Gini impurity scores found through the RF model, including z_SD (A), VNDVI_CoVar (B), i_Mean (C), NGBDI_CoVar (D), z_CC (E), i_SD (F), VNDVI_Min (G), and RGBVI_Mean (H). The range of training samplesâ covariate values is given as small vertical black lines along the x-axis of each PDP to contextualize the distribution of data across a covariateâs range of values. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.â¦... 54 Figure 2.5. Area, in hectares, of classified and unclassified 3 m cells within 100 m of streams of the study area watersheds. Cells which were within riparian environments (100m of streams), and also had at least one LiDAR return within the cell that were greater than 2 m in height, are summarized here. Some cells were not classified due to not having a class probability that was â¥ 25% (bottom column). â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦... 56 Figure 2.6. Predicted class for 100m stream buffers in the study area watersheds. The Hardy Falls inset shows an example of the prediction at a low elevation next to Okanagan Lake, the Mill Creek Falls inset shows an example of the prediction at a low elevation further inland from major lakes of the region, and the Greyback Reservoir inset shows a higher elevation prediction. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦. 57 Figure 2.7. A line graph that shows the proportion of classified cells for each BEC zone. BEC Zones are ordered along the x axis from lowest elevation on the left to highest elevation on the right to demonstrate how the model predicts at different elevations/regions. The amount of area of considered cells within each BEC zone is displayed in hectares beside each BEC zone name, to allow for an understanding of the difference in size between BEC zones of the study area. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦.. 59 Figure A1. Elevation map of the Okanagan Basin displaying larger population centers, lakes, and highways, as well as the different channels of the Okanagan River. â¦â¦â¦â¦â¦â¦... 96 xi Figure A2. Study area watersheds displayed with intersecting BEC zones. Classified cells were cells in which were 100m from streams (riparian environments), and where the cell contained at least one LiDAR return greater than or equal to 2 m from ground height. the Leon Creek watershed was the only watershed in which riparian environments did not intersect each BEC zone within the watershed: Leon Creekâs ICH BEC zone (A), and Leon Creekâs MS BEC zone (B). â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦..... 97 Figure A3. Covariate correlation plot displaying the output Pearsonâs correlation coefficients for each pairwise relationship. Coefficients which are bounded by a black border are where the coefficients are greater than or equal to 0.7. Bolded covariate names, as well as shaded cells for that covariate, highlight covariates in which were selected to avoid high correlations between covariates inputted into the random forest (RF) classification model. â¦â¦â¦â¦â¦..â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦ 98 Figure A4. Imagery that gives context to why the months between March and November were specifically selected for indices to be derived over, based on the snow accumulation seen in the study area within the year of the considered imagery (2019) as well as the knowledge of tree defoliation; snow was very close to being melted in most of the region by March with much of the region regaining snow accumulation until after November. Also, black cottonwood and trembling aspen each drop their leaves in October and November while higher elevation western larch commonly drop their needles in November. â¦â¦.... 99 Figure A5. Vegetation Resources Inventory (VRI) inventory units symbolized by reference year, which explains when the BC Provincial Governmentâs VRI inventory unitsâ assessed imagery were collected, for the Okanagan basin, BC. â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦â¦..100 xii Acknowledgements I respectfully acknowledged that the land this research was conducted on and about is the traditional territory of the Syilx/Okanagan People. This research would not have been possible without the collaboration, guidance, and enthusiasm received from Dr. Mathieu Bourbonnais, as well as the Okanagan Basin Water Boardâs generosity. I am grateful for the experience that this project has brought me, and the doors that Dr. Bourbonnais has helped me open to future opportunities in the field of geocomputation and remote sensing. I would also like to thank my committee members Dr. Hillary Ward and Dr. Lael Parrott for their support, direction, and feedback. I would also like to thank the members of Dr. Bourbonnaisâ EOSEL lab for their camaraderie and willingness to listen as I worked though my syntax issues and troubleshooting, as well as for involving me in various once-in-a-lifetime field-work experiences that were unrelated to my thesis. Specifically, I would like to recognize my friends Ilithia Morely and Renee Larsen for the friendship that helped me get through the toughest moments of graduate studies. I must thank both my mom, Bobbie Chambers, and my dad, John Scinocca, for their unwavering encouragement and emotional support through my graduate studies, and for helping me develop the skills necessary to accomplish my goals. I would also like to recognize mentors Dr Mathieu Bourbonnais, Craig Hartel, Erik Val, Dr. Jessica Neal, Dr. Ian OâConnell and Dan Austin for encouraging my growth, learning, and obsession within the realm of spatial science. I would like to thank the many wonderful folks who assisted me with my field work, including Echo Sulin, Renee Larsen, Tina Deenik, John Scinocca, Mathieu Bourbonnais, Eric Love, Minh Luc, Levi Oostenbrink, and Hannah Treleaven, as well as canine field-workers Oscar Larsen, Beast Luc, and Maisie Treleaven. Lastly, I would like to acknowledge my partner Hannah Treleaven for her constant reassurance, support, and encouragement. This project brought me xiii to BCâs Interior, and for that I am grateful, as I have fallen in love with the people and geography of the region. 1 Chapter 1: Introduction 1.1 Riparian ecosystems In their natural state, riparian environments are among the most intricate, dynamic, and heterogeneous biophysical habitats on terrestrial earth (Naiman et al., 1993). They serve as crucial movement corridors for wildlife, providing essential pathways for their mobility across the greater landscape (Klemas, 2014). Riparian environments harbor unique flora and fauna that exhibit taxonomic or functional distinctions when compared to the neighbouring upland regions (Bennett et al., 2014; Naiman et al., 1993; Sabo et al., 2005). Furthermore, riparian zones often support a higher abundance and diversity of wildlife populations (Capon et al., 2013). Riparian environments contribute many different services to riverine environments, including mitigating downstream flooding (Dixon et al., 2016), managing water quality (Honey-Rosés et al., 2013; Klemas, 2014), controlling stream sediment loads (Zaimes et al., 2004), moderating stream temperature and light (Klemas, 2014; Tompalski et al., 2017), and providing cultural value (Vollmer et al., 2015). Riparian environments are transitional zones that exist between freshwater features and upland terrain (Naiman, Décamps, et al., 2005). These areas are gradually modified by various factors related to the climate, hydrology, geology, geomorphology, biology, and ecology of a riparian setting (Rodriguez-Iturbe et al., 2009). Various terms are used to describe riparian environments, including riparian areas, riparian zones, and riparian buffers. Variations in terminology used to describe riparian environments can be attributed to several factors including divergent legal frameworks and governance structures across countries and administrative divisions, as well as regional dissimilarities between riparian ecosystems such as width, depth, flooding frequency, hydrogeomorphology, and vegetation (Fischer et al., 2001; Fischer & Fischenich, 2000). Riparian environments can also be further categorized based on the specific 2 ecosystem services they provide. For instance, riparian vegetation specifically encompasses the plant life in the semiterrestrial zone (Naiman et al., 1993), while riparian buffers generally pertain to the established rules and regulations governing activities in proximity to watercourses (Larsen-Gray & Loehle, 2022). Many faunae have become uniquely adapted to benefit from riparian environments. Diadromous fishes, which include pacific salmon and trout (Oncorhynchus spp.), and lampreys such as the Pacific Lamprey (Entosphenus tridentatus) and the Western Brook Lamprey (Lampetra richardsoni), depend on freshwater, estuary, and marine environments to survive and reproduce (Flitcroft et al., 2019; Slaney et al., 1996). For anadromous fishes, such as Chinook (Oncorhynchus tshawytscha), thermal variability within freshwater habitats also heavily influences habitat use (Torgersen et al., 1999). The abundant feeding and breeding habitat that riparian environments foster are attractive to many other faunae as well. For example, amphibians rely on aquatic features for breeding and feeding, and the increased air-moisture usually found in riparian ecosystems eases their cutaneous respiration (Marczak et al., 2010). Predators â such as cougars and wolves â commonly hunt larger fauna, including fishes, rodents, and ungulates, within riparian environments (Stella & Bendix, 2018). Similar to their predators, these species are attracted to riparian environments for feeding, shelter, breeding, and landscape-movement (Marczak et al., 2010). Fishes, along with other riparian insectivores such as birds, bats, and spiders, greatly benefit from the insects which utilize aquatic riparian features to breed, influencing the gathering of these insectivores within riparian areas (Nakano & Murakami, 2001). These insects, as well as leaf-litter, are important inputs into streams which enhance the production and biodiversity of both aquatic and terrestrial riparian environments (Marczak & Richardson, 2007). Flora also contribute to and require riparian functions; the woody vegetation within riparian forests and woodlands are the fundamental basis of the ecosystem (Naiman et al., 1993). The composition and structure of riparian vegetation can determine how much illuminance the 3 understory or stream receives, trap sediments and nutrients in surface flows, stabilize stream banks, regulate microclimate, and provide food resources and habitat to fauna (Barling & Moore, 1994; Hood & Naiman, 2000). Riparian forestsâ root depth and density can influence bank stability, promote denitrification, filter groundwaters, and control stream water chemical properties (Capon & Pettit, 2018; Naiman, Décamps, et al., 2005; Zaimes et al., 2004). Riparian vegetationâs ability to slow the movement of both overland and groundwater flow can suppress downriver flooding, and this delay in flow also increases the amount of time evapotranspiration can occur (Naiman, Décamps, et al., 2005). This deceleration of water infiltration to streams also serves to decrease stream sedimentation levels, which influences water quality and downstream geomorphology (Naiman, Décamps, et al., 2005; Leigh et al., 2013). Mature riparian forests that contain vegetation which is wider, denser, and more contiguous can more efficiently trap and filter nutrients and sediments (Dixon et al., 2016; Mayer et al., 2007). Riparian vegetation, among factors such as hydrology, topography, geomorphology, and soil characteristics, has the greatest influence on sediment and nutrient trapping in riparian forests (Mayer et al., 2007). When compared to surrounding upland areas, these riparian forests often contain higher numbers of plant species with greater structural complexity (Sabo et al., 2005). The positive effects that riparian vegetation has on a riparian system are also governed by spatial and temporal factors (Capon & Pettit, 2018). Larger streamside vegetation can regulate stream temperature and stream light levels through evaporative cooling and shading (Naiman et al., 1993), which is an essential habitat characteristic for the wellbeing of aquatic species such as anadromous fish (Larson & Larson, 1997). If nutrient levels are high within a stream, shade provided by streamside vegetation can also deter large algal blooms (Chauvet & Décamps, 1989). As temperatures increase due to climate change, cooler in-stream temperatures, which can be provided through riparian shading, will become more important as refugia for biota (Capon & Pettit, 2018). In-stream biota are some of the most susceptible to a warming climate, and they 4 commonly seek thermal refuge during heatwaves and droughts (Davies, 2010). One study conducted in the coastal British Columbia (BC) headwaters of the Malcolm Knapp Research Forest found that daily maximum temperature in streams that did not have streamside vegetation increased by 2° to 8° C, in comparison to streams that had streamside vegetation (Gomi et al., 2006). While riparian forests contribute many benefits to other riparian species, they also benefit from riparian settings themselves. For example, in North America, cottonwood (Populus spp.) seeds deposited along alluvial sections of streams require suitable soil moisture content with fine sediments for germination, conditions which are generally found when floodwaters are receding (Naiman, Décamps, et al., 2005; Stella & Bendix, 2018). Proximity to the stream also provides riparian vegetation greater water availability compared to upland forests. When compared to upland forests, greater soil nutrient availability in riparian settings resulting from the regular deposition of organic matter and stream sediments promotes the growth and vitality of riparian vegetation (Lovett & Price, 1999). Many of these benefits, such as water availability, have been exploited for human sustenance as well. Throughout history, human societies have chosen to settle in riverine environments, primarily due to the ease of access to vital water resources (Rodriguez-Iturbe et al., 2009). Riparian environments also hold value to humans for recreational activities, hunting and foraging, and cultural significance (Vollmer et al., 2015). Globally, approximately one-quarter of the worldâs human population lives in a floodplain environment (Devitt et al., 2023). Continental aquatic systems are no longer governed solely by climate, topography, vegetation, and geology, but now are subject to the influence of human-driven factors related to population growth, urban and industrial development, water management initiatives, and international environmental policies (Meybeck, 2003). Through the Anthropocene, global degradation of riparian environments has created the need for management and policy which transcend local scales and should involve 5 many governing entities and different disciplinary perspectives (González et al., 2015). To understand riparian ecosystems, it's essential to examine interactions between aquatic and terrestrial elements and to assess the entire system from upstream tributaries to downstream outlets (Naiman, Décamps, et al., 2005). 1.2 Riparian threats and losses Humans have been shaping riparian landscapes since the beginnings of human settlement within riverine valleys, and the majority of the worldâs largest rivers and their riparian environments have been continually altered from centuries of flow regulation by dams, revetments, and abstractions (Dynesius & Nilsso, 1994; Meybeck, 2003; Naiman, Décamps, et al., 2005). Altering watercourses results in simplified flow regimes, biodiversity degradation, and abrupt transitions between different types of riparian landscapes (Naiman, Décamps, et al., 2005; Naiman & Décamps, 1997; Rodriguez-Iturbe et al., 2009). Today, global riparian ecosystems face pressures related to land use, water diversion, modified flood regimes, and biodiversity loss that can severely alter ecosystem services used by plants, animals, and humans (Capon & Pettit, 2018; Stella & Bendix, 2018). Approximately 90% of riparian environments within Europe and North America have reached a state of functional extinction from cultivation and hydrologic management, and the majority of riparian environments in developing countries are following similar trends (Tockner & Stanford, 2002). Approximately 86% of the monitored mammals, birds, amphibians, reptiles and fishes found in freshwater environments have been lost between 1970 and 2016 (Arora & Mishra, 2019; World Wildlife Fund, 2020). In Canada, the main threats to freshwater fishes that rely on riparian environments include the degradation and loss of habitat, introduced species, siltation, dams and impoundments, and development related to residential, agricultural, or industrial infrastructure (Monk & Baird, 2014). Additional threats to riparian habitats utilized by fauna within Canada include water extraction, overexploitation, acid rain effects, channel modification, and introduced contaminants (Monk & Baird, 2014). 6 While some threats to riparian ecosystem functions are from abiotic sources, such as high levels of in-stream nutrients or sediment load, threats to freshwater ecosystem function more commonly stem from the removal of riparian vegetation (Capon & Pettit, 2018). The deterioration of long-lived larger woody riparian vegetation is common in riverine environments, as changes to hydrology can cause drowning and underwatering of riparian trees (Stromberg et al., 2007). Grazing activities in riparian environments can also impede the growth of riparian trees, such as cottonwood, particularly as cattle often graze on the seedlings before they can reach maturity (Auble & Scott, 1998). Other deforestation pressures include wildfires, forest harvest, and roadway construction, which can influence downstream flooding and decrease slope stability and water quality (Pike et al., 2010a). Landscapes with high levels of anthropogenic influence generally have narrower riparian zones, as well as reduced riparian forest connectivity (James et al., 2016). Non-point source pollution associated with forest harvest activities, including vegetation removal, road construction, and soil compaction, is another common threat to riparian environments (Larsen-Gray & Loehle, 2022). These types of pollution degrade water quality in freshwater environments (Larsen-Gray & Loehle, 2022), and changes to water quality combined with fragmentation of waterways due to damming and other water-course changes can severely limit the survival of freshwater species (Flitcroft et al., 2019; Pringle et al., 2000). Climate change will present increased stresses on riparian environments, resulting in changes to streamflow, water temperatures, riparian forest microclimates, and vegetation composition (Flitcroft et al., 2019; Palmer et al., 2008). These riparian changes will likely be amplified in areas that are projected to see shifts from snow-dominated to rain-dominated hydrology, creating more variable streamflow events (Goode et al., 2013). Increasingly variable streamflow and riverine water temperatures driven by climate change negatively affect survival and reproduction rates of resident fishes (Davies, 2010; Mantua et al., 2010; Tompalski et al., 7 2017). Canada is forecasted to have more extreme precipitation events, as well as a 20% increase in annual precipitation and 30% increase in winter precipitation (Bates et al., 2008). These changes in the timing and amounts of precipitation can result in drastic changes in streamflow over short periods of time (Mantua et al., 2010), and riverine environments will be more susceptible to downstream geomorphologic modifications from increases in high discharge events (Rodriguez-Iturbe et al., 2009). 1.3 Riparian management The degradation of riverine and riparian ecosystems is a global concern, and the monitoring and management of these landscapes has increased since the 1990s (González et al., 2015). For example, retaining vegetated buffers around riparian areas to conserve riparian ecosystem services is increasingly used as a management approach during forest harvest activities (Larsen-Gray & Loehle, 2022). Generally, issues relating to drinking water quality and flood control are the greatest focus of riparian ecosystem management (Honey-Rosés et al., 2013; Huylenbroeck et al., 2020). However, there is an increasing need to understand how anthropogenic processes, including pressures relating to land use, water diversion, and watercourse modification for flood control (Naiman & Décamps, 1997), influence the quality of flora and fauna habitat (Klemas, 2014; Rae, 2005). Riparian management often focuses on woody vegetation due to its moderating influence on riparian pressures (Naiman, Décamps, et al., 2005). Generally, the management of riparian areas focuses on protection, restoration, or replacement of riparian vegetation (Capon & Pettit, 2018). For example, a project to improve diking on a section of Mill Creek in Kelowna, BC resulted in the loss of 2,901 m2 of treed riparian area, and in response 8,087 m2 of riparian vegetation area was restored or enhanced through the manual introduction of riparian-specific species, including 1,586 trees and 4,204 shrubs (Redden, 2022). Increasingly, riparian vegetation enhancement 8 may require land managers to use fast-growing, non-native species to provide stream shading rather than native species (Davies, 2010). As future climate change presents new problems for riparian environments, engineering solutions may become increasingly used by managers to physically alter environments to retain or influence ecosystem services (Capon & Pettit, 2018). Riparian restoration commonly addresses social or political concerns such as weed invasion, riparian tree mortality, and riverbank erosion (Pike et al., 2010a). Revegetation efforts are commonly used to mitigate anthropogenic pressures such as grazing, hydrologic alterations, stream channelization, and invasive species mitigation (Capon & Pettit, 2018). To promote the growth of vegetation in riparian ecosystems influenced by anthropogenic pressures, restoration can range from passive strategies such as erecting fencing to allow natural restoration, to more active strategies including the physical planting of species that can promote targeted ecosystem functions (Capon & Pettit, 2018). For example, in the southern Okanagan region of BC, increasing the availability of shrubbery within riparian settings was key to increasing the breeding capabilities of the endangered yellow-breasted chat (Icteria virens auricollis), even within heavily populated urban environments (Forrester et al., 2020). In human-dominated landscapes, protection and management of riparian areas is often focused on conservation of the last remnants of native vegetation due to land-use pressures (James et al., 2016). When considering climate change, riparian restoration is recognized as a strategy to conserve irreplaceable ecological functions and services (Capon et al., 2013). There is also an increasing need for riparian management to consider the restoration of vegetation to reflect historical compositions as well as how non-native species may be used to maintain riparian ecosystem function (Capon & Pettit, 2018). A common approach used to determine the health of a riparian ecosystem is to quantify how much of a streamâs floodplain is still vegetated (Pace et al., 2022). Since the 1980s, there has been an increasing focus on the restoration and monitoring of riparian environments (Walker 9 & Hobbs, 2007). In a review of studies on the restoration of riparian vegetation, González et al. (2015) found that restoration of riparian vegetation globally has centered around a few main approaches, including altering the hydro-geomorphic schemes of riparian watercourses, introducing vegetation, controlling non-native species, converting floodplains to deter natural flooding, and controlling grazing and herbivory practices. They also noted that the impact of riparian restoration efforts is generally only evaluated over the short term (< six years), the efficacy of restoration efforts was most often assessed within studies considering riparian vegetation restoration, and most studies considered vegetation structure rather than vegetation diversity and processes, which include biomass accumulation and survival (González et al., 2015). Describing riparian vegetation attributes and quantifying the area that contains vegetation has become easier and more cost effective over the past few decades with the continued development of remote sensing (Wulder et al., 2012). 1.4 Remote sensing applications for riparian assessment The accurate measurement of riparian vegetation properties is needed to support sustainable management practices (Dufour et al., 2013). The benefit and need for the assessment of riparian environments, and specifically riparian vegetation, has been demonstrated globally and is the focus of considerable methodological innovation (Huylenbroeck et al., 2020). Early efforts to map and monitor riparian vegetation relied heavily on measurements from field-based transects conducted perpendicular to streams, which recorded the distribution of plant communities (Dilts et al., 2010). Since the 1990s, riparian studies have commonly characterized forests using vegetation structure metrics relating to abundance, height, density, and complexity (González et al., 2015). Because of the spatial composition, temporal dynamics, dense vegetation and remote locations of many riparian areas, it is expensive and labor-intensive to collect extensive field data, especially for expansive riparian networks (Akay et al., 2012; Pace et al., 2022; Wasser et al., 2015). It is also difficult to map large and narrow swaths of riparian vegetation 10 through field-based approaches, which can require interpolation between observations (Dilts et al., 2010). Early riparian assessments used air-photo interpretation to assess vegetation (Owers et al., 2012); however, air-photo interpretations can be prone to error or bias making it difficult to assess riparian condition. Satellite remote sensing (RS) technologies have been commonly used to assess landscapes since the launch of Landsat 1 in 1972 (Goetz, 2006), and allow for the repeated collection of standardized data at broad spatial scales (Dufour et al., 2013; Lattin et al., 2004). With advancements in sensors paired with computational power, RS applications have been increasingly employed in studies related to riparian processes and management (Wulder et al., 2012). A recent review of the use of RS in modelling riparian vegetation conducted by Huylenbroeck et al. (2020) found that few riparian vegetation studies before 1990 employed the use of RS technologies. Even with extensive developments in RS technologies and access to data, they found that riparian studies continue to rely heavily on field-based approaches. Additionally, they noted that only 8% of riparian studies between 1980 and 2018 used high resolution satellite sensors (â¤ 10 m resolution), and these data were generally used to enhance lower resolution satellite imagery (Huylenbroeck et al., 2020). As RS technologies and imagery continue to improve, there are opportunities to leverage these technologies to develop new methods for monitoring riparian ecosystems. Predictions of riparian vegetation species should include parameters that can accurately describe ecological processes (Klemas, 2014). RS approaches for classifying vegetation types and species often rely on the spectral differences among species (Doody et al., 2014; Dutta et al., 2017; Michez et al., 2016; Richter et al., 2016). Within riparian environments, vegetation types are also heavily dependent on topographic characteristics, including height above the river, soil 11 texture, and soil moisture (Dilts et al., 2010), which can be quantified using RS. Riparian vegetation can also be characterized based on vertical and horizontal structure of the vegetation, using attributes such as canopy closure and height (Forzieri et al., 2013; Goetz, 2006; Michez et al., 2013; Stackhouse et al., 2023; Wasser et al., 2015). Increasingly, multiple types of RS data are being combined to improve forest inventories (Coops et al., 2021) and vegetation mapping in riparian areas (Forzieri et al., 2013). 1.4.1 Multispectral imagery The use of multispectral RS sensors, which describe the absorption and reflectance of objects based on reflected and emitted solar radiation (Tucker, 1980), can be used to quantify riparian characteristics. For example, spectral signatures collected by sensors can explain differences in leaf chlorophyll content and soil moisture; however, they are susceptible to error due to atmospheric attenuation and noise (e.g., cloud, smoke; Tucker, 1980). Recently, multispectral imagery has been increasingly used to characterize riparian vegetation based on spectral signatures (Huylenbroeck et al., 2020). For example, multispectral imagery has been used to quantify anadromous fish habitat characteristics (Harrison et al., 2020), assess the health and structure of riparian vegetation (Hall et al., 2009; Michez et al., 2016), and to classify riparian vegetation and landcover types (Dutta et al., 2017; Forzieri et al., 2013; Jeong et al., 2016; Martin et al., 2020; Michez et al., 2016; Richter et al., 2016). Forest species classification studies which employ comprehensive multispectral or hyperspectral datasets tend to have higher classification accuracies (Huylenbroeck et al., 2020). However, hyperspectral imagery is costly to acquire (Michez et al., 2016), especially across large areas (Luther et al., 2019). Instead, freely available multispectral imagery, including Landsat Thematic Mapper or Sentinel-2, are widely used (Luther et al., 2019). For example, Pace et al. 12 (2022) used open-access 10 m resolution Sentinel-2 imagery to assess riparian vegetation and ecosystem health in multiple watersheds of Portugalâs Northwest region. Multispectral RS imagery are commonly used to develop vegetation indices, including the normalized difference vegetation index (NDVI), which can be used to infer about canopy traits such as health and density (Xue & Su, 2017). NDVI is not indicative of any specific physical attribute of vegetation, and can be correlated with other indices such as leaf area index, proportion of vegetation cover, vegetation condition, and amount of biomass (Carlson & Ripley, 1997). Regardless, NDVI is one of the most widely employed indices used to quantify the presence of green photosynthetic vegetation. NDVI is a ratio that uses the red (R) and near-infrared (NIR) sensor bands ðð·ðð¼ =(ðð¼ð â ð )(ðð¼ð + ð ) NDVI values range between 1 and -1, where a higher positive value indicates green vegetation with high productivity (Xue & Su, 2017). Many studies rely on expensive and/or novel multispectral datasets or acquisition methods to classify vegetation. Doody et al. (2014) used âcoastal blueâ (400â450 nm), âredâ (630â690 nm), âred edgeâ (705â745 nm) and ânear infrared2â (860â1040 nm) spectral bands from the multispectral WorldView2 sensor to generate indices at a 2 m spatial resolution to identify invasive willow (Salix spp.) in an Australian riparian environment at a 97% accuracy. High-resolution WorldView2 RS data is not open-access but rather proprietary, requiring licensing agreements for access due to its commercial nature. Michez et al. (2016) classified five tree species using spectral indices generated from an unmanned aerial vehicle (UAV) sensor, garnered overall accuracies of 79.5% and 84.1% for their two different study areas, respectively, and exemplified a cheaper manner of assessing riparian vegetation at small spatial resolutions. They argued that the multitemporal aspect of their index generation aided in classifying between multiple different (Eq. 1) 13 deciduous species, and also in a cost-effective manner. They were also able to use many vegetation indices that only required red, green (G), and blue (B) bands, which are easily acquired through freely available datasets. While using UAV-based RS data acquisition may be cost-effective in comparison to plane-based RS data acquisition, not every study has UAVs and sensors readily available for use. Open-access datasets, or datasets collected and supplied by other researchers at no cost (e.g., visible spectrum imagery collected in tandem with aerial LiDAR) are a more cost-effective means of data acquisition. While spectral imagery and indices are useful to help understand the condition and type of vegetation in riparian areas they do not provide information regarding the structural composition of vegetation and topography (Congalton et al., 2003; Wasser et al., 2015). Martin et al. (2020) used multitemporal multispectral data to generate vegetation indices to classify two different deciduous species at a 90% accuracy, and found that the addition of light detection and ranging (LiDAR) generated metrics improved the classificationâs accuracy. 1.4.2 Light detection and ranging LiDAR is increasingly used to quantify vegetation structure and to develop high spatial resolution topographic models. For various forest harvest tasks, such as tree canopy analysis, terrain mapping, biomass estimation, and vegetation monitoring, the cost and required time of LiDAR data collection and processing is more efficient than other techniques, such as the interpolation of in-situ sampling data (Akay et al., 2009). LiDAR is an active RS technology and uses a laser-based measurement system, in conjunction with global positioning systems (GPS), to produce a point cloud. A point cloud is a collection of returns in a three-dimensional coordinate system, where points are attributed with x, y, and z (elevation) measurements to determine the three-dimensional locations of each point (Dong & Chen, 2018). Other attributes associated with each point include the intensity of the return, which is the strength of a return; classification, which 14 can explain if a return is ground or non-ground; and return number, which can explain the partial returns from a single emitted pulse (Lefsky et al., 2002). LiDAR data are commonly used to produce a digital elevation model (DEM) with fine spatial resolution (e.g., < 1 m) and topographic derivatives which explain attributes of the landscape (Cavalli et al., 2008; De Reu et al., 2013; Stefankiv et al., 2019; Wilkins & Snyder, 2014). Common topographic indices derived from LiDAR DEMs include slope, aspect, terrain ruggedness, and topographic position index (De Reu et al., 2013; Dong & Chen, 2018). Riparian-specific descriptors that are derived using DEM data include the height above river and the topographic wetness index. Height above river, which is based on perpendicular transects from streams, as well as flow direction and accumulation (Grijseels et al., 2020; Stackhouse et al., 2023), is used to inform flood models and the extent of riparian zones (Dilts et al., 2010). DEM data derived from LiDAR are also used to define the boundaries of waterways, which can help identify flow barriers for stream fishes when used in conjunction with hydrologic monitoring (Grantham, 2013). LiDAR point-clouds are also used to derive information that explains the vertical and horizontal structure of vegetation, including vegetation heights, crown widths, density, and canopy cover (Coops et al., 2021). Akay et al. (2012) investigated the ability of LiDAR-derived metrics, including average tree height, total crown width, canopy closure, and vegetation density, to explain field-based measures of the same metrics for an Oregon riparian forest, and found strong relationships for tree height (R2 = 0.88) and crown width (R2 = 0.92). The generation of LiDAR-derived metrics to explain riparian vegetation is also an important step in the classification of riparian vegetation. Dufour et al. (2013) explored the use of a supervised oriented-object machine learning classification model on a French riparian site, and classified the landscape using a LiDAR-derived canopy height model and intensity surface into vegetation types, including hardwoods, shrubs, juvenile shrubs, herbs, and meadows, as well as other landcover types 15 including streams and roads at an accuracy of a 93% global kappa index. They noted that the inclusion of other independent covariates, such as spectral, texture, context, and shape information could improve results. 1.5 Machine learning classification methods Since the Landsat program was launched in 1972 there have been substantial advancements in methods used to classify vegetation at landscape scales with RS data (Grossmann et al., 2010). These methods can include supervised and unsupervised classifications of single images or multitemporal imagery, and can incorporate non-RS ancillary data (e.g., topography, climate, social data; Holmgren & Thuresson, 1998). Unsupervised classifications, which do not require training data (Norman et al., 2018), group cells into undetermined classes which are then assigned to classes using a proximity matrix (Breiman, 2001). While unsupervised classification approaches do not require training data, they may not identify meaningful classes or the species of interest. Supervised classifications require training data to classify the landscape into pre-determined classes (Holmgren & Thuresson, 1998). This training data should be plentiful and widespread, especially so over large areas, in order to properly capture the trends between different classes during classification (Peerbhay et al., 2021). Supervised statistical techniques, such as classification and regression trees, are widely used for both the analysis and classification of ecological data (Deâath & Fabricius, 2000), though they are prone to over-fitting (Grossmann et al., 2010). Random Forest (RF) classification models can overcome the issue of overfitting (Breiman, 2001), and can also accommodate non-parametric relationships (Hayes et al., 2014), which are common with complex and unbalanced ecological datasets (Grossmann et al., 2010). 16 1.5.1 Random forest RF classification models, hereon referred to as RF models, use bagging, a technique that combines the predictions of many decision trees trained on random subsets of the training data, to improve classification accuracy and reduce overfitting (Breiman, 2001). RF models, while generally achieving higher classification accuracies than other landscape classification methods (Belgiu & DrÄgu, 2016), assume the training data represents an exhaustive sample of all classes that are present on the landscape (Foody, 2021). Vegetation samples collected from the field offer the most reliable inputs for RF models; however ancillary data (e.g., air-photo interpretation) and other datasets created by qualified professionals (e.g., open source government datasets) can also be used to train classification models (Matasci et al., 2018). While some studies have used RF modelling approaches coupled with RS data to detect presence of riparian vegetation (Pu et al., 2021; Stackhouse et al., 2023), RF models are more commonly used to classify riparian vegetation, often based on specific species or vegetation heights, such as herbaceous plants, shrubs, and trees (Hayes et al., 2014). The differentiation of coniferous or deciduous classes is a main focus of most riparian vegetation classification studies. For example, object- and cell-based metrics derived from LiDAR data are commonly used in RF models to classify coniferous and deciduous tree species (e.g., Hayes et al., 2014; Matasci et al., 2018). The decision to use either cell-based or object-based (i.e., tree-crown delineation models) data for RF classifications depends on the study objectives. In landscapes where large-canopy trees such as black cottonwood (Populus balsamifera ssp. trichocarpa) and narrow-canopy trees such as subalpine fir (Abies lasiocarpa) occur, the scale at which object-oriented delineation of tree crowns is conducted could favour one species, which can be avoided using cell-based approaches. Many species of deciduous trees also do not have the straight stems that object-17 based tree crown delineations often assume, creating issues in accurately quantifying deciduous trees with automated vector crown polygons or stem points (Laslier et al., 2019). Indicators derived solely from one type of sensorâs data (e.g., LiDAR or multispectral) are commonly used as explanatory inputs to multivariate riparian vegetation RF models (Hayes et al., 2014; Matasci et al., 2018), but the combination of more than one type of RS data to create these explanatory covariates tends to result in higher overall classification accuracies and better predictions. For example, Liu et al. (2017) found the combination of LiDAR and hyperspectral metrics in RF models outperformed models which included metrics derived solely from either sensor. Numerous studies have explored the use of RS data with RF modelling techniques for the mapping of trees within forested or urban environments (e.g., Alonzo et al., 2014; Dian et al., 2016; Huang et al., 2017; Liu et al., 2017; Matasci et al., 2018; Nevalainen et al., 2017; Qin et al., 2022; Richter et al., 2016; Shen & Cao, 2017; Shi et al., 2018; Sothe et al., 2019; Zhong Xu et al., 2020). However, there is a lack of riparian forest classifications using RS data, with most focusing on riparian environments directly adjacent to urban or agricultural landscapes (Laslier et al., 2019; Michez et al., 2016; Pu et al., 2021). Fewer have considered more rural riparian environments with less adjacent anthropogenic influence (Hayes et al., 2014; H. T. Nguyen et al., 2020; Stackhouse et al., 2023). 1.6 Canadian and British Columbian riparian management In Canada, there are more than 8,500 rivers and 2 million lakes, which equal approximately 9% of Canadaâs area (Monk & Baird, 2014). The riparian environments of these rivers and lakes have been the most commonly settled environments in Canada since the onset of European colonization, due to floodplainsâ abundance of ecosystems services. Unfortunately, development has led to considerable losses of floodplain vegetation due to various practices 18 relating to land-clearing, roadway construction, and forest harvest operations. As a result of documented declines in North American anadromous species across North Americaâs Pacific Northwest region since the late 1700s (Rand et al., 2012), most management and conservation of riparian environments relates to the protection of fish habitat, though the availability of water for anthropogenic uses and the protection of other floral and faunal speciesâ habitats also are main drivers of these practices. In order to inform decisions relating to riparian management, detailed mapping of riparian environments is needed within BC. For the purposes of delineating riparian areas in BC, the Riparian Management Area (RMA) width classification system defines specific stream buffers based on stream width and fish presence, and these buffers inform forest management decisions near or within riparian environments (Pike et al., 2010b). The RMA width classification system labels streams from S1, which are larger streams with fish occurrence, to S6, which are smaller streams without fish occurrence (Table 1.1). The RMA of streams are further delineated into two distinct regions, known as the Riparian Management Zone (RMZ) and the Riparian Reserve Zone (RRZ). This classification provides a comprehensive breakdown of the RMA, enabling more focused and targeted management approaches within each designated zone. RMZs occur directly adjacent to streams, and have very strict development regulations to protect ecologically sensitive riparian functions, whereas the RRZs serve as buffer zone between the RMZ and upland areas, and relax development regulations. The presence of vegetation within RRZs enhance the protection of riparian watercourses from increased sedimentation from run-off as well as providing increased habitat connectivity (Stevens et al., 1995). Many other governing administrative bodies within the Pacific Northwest use similar systems to delineate riparian area widths (Pike et al., 2010b). The main concerns regarding riparian environments in BC involve drinking water quality, flooding, landslides, and fish and other faunal habitat (Pike et al., 2010b). These concerns are 19 particularly pressing in the Okanagan, which has the highest population-to-surface water yield ratio of all Canadian basins, highlighting significant regional water demands (Statistics Canada, 2016). Investigating bird density and diversity within the Invermere Forest District, Kinley and Newhouse (1997) found that provincial recommended riparian widths decreased the density and species heterogeneity of riparian bird species when compared to wider riparian zones (~ 70 m). However, increasing the widths of RMAs in BC to include more riparian vegetation could be difficult as many crown forests adjacent to RMAs have already been lost or altered due to anthropogenic practices related to either forest harvest, agriculture, or infrastructure development. Also, in many areas private land and crown land intermingle, particularly in the Okanagan, the Kootenays, and Vancouver Island, and in these areas conflicting interests between timber harvesting managers and drinking water supply managers are exacerbated (Pike et al., 2010b). In BC, the provincial governmentâs Terrestrial Ecosystem Mapping (TEM) initiative systematically classifies and maps landscape ecosystems as polygons based on air photo interpretation, taking into account factors like vegetation structure, climate, soil, and topography. TEM is used to quantify the distribution and characteristics of various terrestrial ecosystems across the province, aiding in land management, conservation, and natural resource planning (Thompson et al., 2016). Unfortunately, TEM only covers specific areas of the province; in the Okanagan for example, most TEM-mapped areas are concentrated in the valley bottom, near urban environments. Several studies have used RS data to predict forest classes using TEM guidelines in BC (Johansen et al. 2007; Thompson et al. 2016); however, they have not considered riparian species specifically. Recent studies have delineated streams and woody vegetation (Tompalski et al. 2017; Dakin Kuiper et al. 2023), as well as riparian vegetation (Stackhouse et al. 2023) using LiDAR data. No studies in BC have attempted to classify riparian vegetation into different species-20 based classes in relatively large watersheds, such as those found within the Okanagan basin. Riparian classifications that have multiple output classes based on species identification can allow for more informed decision making for riparian management goals and projects. Table 1.1. Simplified stream class designation in British Columbia, based on channel width and stream presence, for the determination of riparian reserve and management zone buffer widths. This table was simplified by Tompalski et al. (2017), and originally sourced from Pike et al. (2010b). Stream class Fish occurrence Average channel width (m) Riparian reserve zone (RRZ) width (m) Riparian management zone (RMZ) width (m) Total width of riparian management area (RMA) (m) S1a Yes >20 50 20 70 S2 Yes >5; <=20 30 20 50 S3 Yes >1.5; <=5 20 20 40 S4 Yes <1.5 0 30 30 S5 No >3 0 30 30 S6 No <=3 0 20 20 a Class S1 can be divided into two subclasses: S1-A and S1-B. S1-A streams have an average width greater than 100 m over at least a 1-kilometre course. 21 1.7 Okanagan riparia Water within the Okanagan basin, hereon referred to as the Okanagan, originates in the upper elevation areas that surround the valley bottom before flowing through small tributaries and creeks, which feed into many of the Okanaganâs main-stem lakes and rivers. Some of these large lakes include Wood Lake, Kalamalka Lake, Swan Lake, Okanagan Lake, Skaha Lake, Vaseux Lake, and Osoyoos Lake, many of which are connected by the Okanagan River (Appendix A, Figure A1; Cannings, 2000; Rae, 2005). The large lakes within the Okanagan moderate winter temperatures, allowing biotic species of the Columbia basin to extend north into Canada (Cannings, 2000). Many other larger lakes that occur in the headwater areas of the Okanaganâs numerous tributaries have been dammed for regulation of domestic and irrigation water uses, and many dams regulate discharge along the Okanagan and Columbia rivers as well. (Rae, 2005). Lakes and rivers of the Okanagan have been heavily influenced by glacial processes. Glacial Lake Penticton, which reached heights of 60 m higher than the present day shore-height of Okanagan Lake, heavily influenced the networks and flow patterns of the present day streams (Fulton, 1969; Nasmith, 1962). Riparian environments which occur in the lower-elevations of the Okanagan valley are characterized by deep and narrow canyons, carved out after multiple gradual draining events of dams associated with glacial lakes and residual isostatic rebalance (Roed & Greenough, 2014). Provincial datasets such as TEM and the Vegetation Resources Inventory (VRI) serve as the only current landscape-level inventory within the Okanagan. For example, VRI can be used to determine information on tree species composition within inventory units. It is common, however, for inventory units to have not been updated for decades and reference imagery used to classify tree species composition can date back as far as 1955 in the Okanagan. While datasets such as VRI and TEM can be useful tools in understanding the potential occurrence of tree 22 species distribution across the Okanagan, they cannot accurately describe the specific locations of trees due to the relatively large size of polygons (mean of 11.96 ha). Conifer tree species are more abundant than deciduous species within the Okanagan basin, and are generally used as the defining vegetative species for provincial classifications such as TEM or the Biogeoclimatic (BEC) classification system. BEC zones within the Okanagan include Bunchgrass (BG), Ponderosa Pine (PP), Interior Douglas-fir (IDF) Interior Cedar â Hemlock (ICH), Montane Spruce (MS), and Englemann Spruce â Subalpine Fir (ESSF) (Appendix A, Figure A2; Ministry of Forests, 2021). Of native Okanagan deciduous tree species, black cottonwood and birch (Betula spp.) species, unlike trembling aspen (Populus tremuloides), prefer the wet soils and moist conditions of riparian environments and therefor are riparian indicator species (Okanagan Nation Alliance et al., 2019). Large, non-native, tree-sized weeping willow (Salix babylonica) also occur within urban riparian environments of the Okanagan. Native conifer species commonly found within the Okanagan include: ponderosa pine (Pinus ponderosa), interior Douglas-fir (Pseudotsuga menziesii var. glauca), western larch (Larix occidentalis), western redcedar (Thuja plicata), lodgepole pine (Pinus contorta), subalpine fir (Abies lasiocarpa), Englemann spruce (Picea engelmannii) and white spruce (Picea glauca). While many tree species of the Okanagan are not riparian specific, several upland tree species (e.g., trembling aspen) will sparsely occur within Okanagan riparian environments. Elevational gradients (270 m â 2300 m) influence the distribution and abundance of riparian tree species in the Okanagan (Allen & Nott, 2021). Within riparian environments, black cottonwood is found at most elevations of the Okanagan, though it is most abundant in lower elevations and decreases in abundance with increasing elevation. Paper birch (Betula papyrifera) only occur within lower elevation riparian environments, and are not as common as black cottonwood because they cannot tolerate dry soils and soils with relatively poor nutrient regimes. Non-native weeping willow, sometimes growing to heights of greater than 20 m, are common in urban areas 23 of the Okanaganâs valley bottom and many different native shrubby willow species occur through the majority of elevations of riparian environments. BC is rich with numerous different species of native willow in comparison to other regions of Canada and the rest of the world, as there are at least 42 different species that occur within the province (Breyshaw, 1976), and some of these shrubby willow species can reach tree heights (> 10 m). Many conifers that are more common in upland areas still sparsely occur within riparian environments, such as ponderosa pine, and interior Douglas-fir within lower elevations of the Okanagan. Within riparian environments that occur within the ICH BEC zone, western redcedar become more common when compared to lower and drier elevations. At some of the highest elevations of the Okanagan (e.g., within the MS and ESSF BEC zones) species such as spruce (Picea spp.) and subalpine fir are common within riparian environments and upland ecosystems, partly because smaller headwater streams in these areas have less influence on soil moisture and vegetation composition compared to larger streams at lower elevations. There has been considerable loss of riparian vegetation since the arrival of European settlers in the Okanagan in the mid-1800s due to urban and agricultural activities, road building, and livestock grazing (Lea, 2008), particularly at lower elevations. For example, black cottonwood â red-osier dogwood floodplain ecosystem types, which are riparian forests characterized by numerous black cottonwood above a dense understory of shrubby species including red-osier dogwood (Cornus stolonifera), water birch (Betula occidentalis), and numerous species of rose (Rosa spp.) and willow have declined by 63% since 1800, while water birch â red-osier dogwood floodplain, fan, and riparian ecosystem types have declined by 92% (Lea, 2008). Lea (2008) also noted the loss of 93% of aquatic area of the Okanagan Riverâs three separate Canadian segments, attributed to the channelization and diking of the river in 1948 (Appendix A Figure A1). In higher-elevation areas of the Okanagan, the main threats to riparian ecosystems include forest harvest, grazing, and mining activities (Okanagan Nation Alliance et al., 2019). 24 1.8 Research statement 1.8.1 Problem and significance Riparian environments of BCâs Okanagan region are ecologically significant landscapes that play a vital role in maintaining water quality, preventing erosion and flash-flood events, providing recreation and cultural significance, and supporting diverse species of flora and fauna. Given the changing climate, understanding how riparian areas may be affected by alterations in hydrologic, temperature, and precipitation patterns is essential. Investigating the composition of riparian vegetation is crucial for assessing ecosystem resilience and guiding conservation efforts, as shifts in dominant plant species can impact water retention, habitat suitability, and overall ecosystem stability in consideration of climate-related changes (Okanagan Nation Alliance et al., 2019). The Okanagan has experienced drastic losses to its riparian environments, and other ecosystems such as shrub/steppe ecosystems, over the last century and increased anthropogenic pressures, climate change, and natural disturbances (e.g., wildfires, floods, and drought) will further threaten remaining riparian ecosystems (Lea, 2008). Riparian vegetation maps in the Okanagan, developed using reproducible methodologies and remotely sensed data, can be used to help inform land use planning and conservation of riparian ecosystems and habitat. 1.8.2 Goals and objectives This study classifies riparian forest tree species, through the use of RS applications, for select fish-bear streams (n = 24; Table 1.2) in tributaries of the Okanagan River system (Ministry of Forests Lands Natural Resource Operations and Rural Development, 2018; Rae, 2005). Fish-bearing streams hold significance due to the importance of monitoring and managing riparian vegetation to increase or retain favourable conditions for use by anadromous species within the 25 region (Okanagan Nation Alliance, 2017), as well as other fauna that rely on riparian habitat (Forrester et al., 2020). This study aims to develop a reproducible process for classifying and mapping riparian forests. The research incorporates high spatial resolution RGB RS imagery with LiDAR data to develop metrics and indices which explain riparian vegetation and landscape characteristics, at a 3 m spatial resolution. These metrics and indices are used as covariates to inform a RF machine-learning model to classify and predict ten classes of native tree species of the Okanagan: black cottonwood (Populus balsamifera ssp. trichocarpa), interior Douglas-fir (Pseudotsuga menziesii ssp. glauca), lodgepole pine (Pinus contorta), ponderosa pine (Pinus ponderosa), spruce (Picea spp.), subalpine fir (Abies lasiocarpa), trembling aspen (Populus tremuloides), western larch (Larix occidentalis), western redcedar (Thuja plicata), and willow (Salix spp.). Some species classes contain multiple species due to hybridization or structural similarities. For example, the spruce class encompasses Engelmann spruce (Picea engelmannii) and white spruce (Picea glauca) due to their common hybridization with the Okanagan. In order to train the RF model, point locations of native tree observations that represented the ten vegetative output classes from across the Okanagan were collected. The methodology developed in this study provides a reproducible and cost-effective approach to classify and assess riparian environments at fine spatial scales over large areas. The output classification maps, along with output probability maps for each class, can also be used to monitor Okanagan riparian environments. These classification surfaces can help inform management decisions on riparian fish habitat, infrastructure development, delineation and protection of riparian vegetation, and streamside restoration projects. 26 Table 1.2. The size (in ha) of Okanagan watersheds considered, ordered from north to south based on their stream outlets, and separated based on which side of the Okanagan basin they occur on. West side of Okanagan East side of Okanagan Watershed name Area Watershed name Area Shorts 18,557 ha B.X.* 13,199 ha Lambly 24,396 ha Coldstream* 20,605 ha McDougall 5,347 ha Upper Vernon* 12,799 ha Powers 14,531 ha Middle Vernon* 2,985 ha Trepanier 26,004 ha Lower Vernon* 25,658 ha Peachland 14,837 ha Mill 22,369 ha Eneas 9,235 ha Mission 84,510 ha Prairie Valley 1,392 ha Bellevue 8,777 ha Trout 74,644 ha Leon 825 ha Shingle 29,859 ha Naramata 4,180 ha Penticton 18,003 ha Shuttleworth 8,995 ha Vaseux 29,431 ha Inkaneep 17,918 ha * Minor watershed of the Vernon watershed. 27 Chapter 2: Random forest classification of riparian forests in select tributaries of the Okanagan, British Columbia, using remote sensing applications 2.1 Background Riparian environments are highly complex and diverse terrestrial habitats (Naiman et al., 1993), serving as essential wildlife corridors (Klemas, 2014) and harboring unique flora and fauna (Sabo et al., 2005). They support abundant biodiversity (Naiman & Décamps, 1997) and provide various ecosystem services, including flood mitigation (Dixon et al., 2016), water quality management (Honey-Rosés et al., 2013), and temperature regulation (Klemas, 2014). Riparian ecosystems benefit diadromous and anadromous fishes (Slaney et al., 1996), amphibians (Marczak et al., 2010), predators (Stella & Bendix, 2018), and insectivores (Nakano & Murakami, 2001), shaping ecosystem dynamics. Riparian vegetation plays a critical role (Naiman et al., 1993), influencing stream stability (Barling & Moore, 1994), water quality (Naiman, Décamps, et al., 2005), and habitat availability (Larson & Larson, 1997). Additionally, riparian ecosystems offer cultural value and have historical importance for human settlements, recreational activities, and sustenance (Vollmer et al., 2015). The management of riparian environments is increasingly complex due to human-driven factors (Meybeck, 2003) and requires a holistic approach that considers both aquatic and terrestrial interactions along river systems. Riparian management approaches include retaining vegetated buffers around riparian areas during forest harvest activities (Larsen-Gray & Loehle, 2022), with a primary focus on drinking water quality and flood control (Honey-Rosés et al., 2013). Understanding how anthropogenic processes, including land use, water diversion, and flood control modifications, impact flora and fauna habitat quality is needed (Klemas, 2014; Naiman & Décamps, 1997; Rae, 2005). Riparian management often emphasizes protection, restoration, or replacement efforts of woody vegetation (Capon & Pettit, 2018) as a result of its moderating influence on riparian 28 pressures (Naiman, Décamps, et al., 2005). Climate change introduces new challenges, potentially leading to increased use of engineering solutions to influence ecosystem services (Capon & Pettit, 2018), or increased use of fast-growing non-native species for stream shading (Davies, 2010). Monitoring riparian ecosystems includes assessing floodplain vegetation (Pace et al., 2022), which has become more cost-effective with remote sensing (RS) technology (Wulder et al., 2012). Since the 1990s, there has been a shift towards characterizing riparian forests using RS-derived vegetation structure metrics (González et al., 2015; Huylenbroeck et al., 2020), but extensive field data collection remains costly and challenging, especially in remote or densely vegetated riparian areas (Akay et al., 2012; Pace et al., 2022; Wasser et al., 2015). Riparian RS approaches frequently utilize spectral differences between vegetation species, aiding species classifications (Doody et al., 2014; Dutta et al., 2017; Forzieri et al., 2013; Jeong et al., 2016; Martin et al., 2020; Michez et al., 2016; Richter et al., 2016). Light detection and ranging (LiDAR) can enhance accuracies of riparian classification models (Liu et al., 2017) through its ability to efficiently quantify vegetation structure, including canopy height and density (Akay et al., 2012; Coops et al., 2021; Huylenbroeck et al., 2020; Liu et al., 2017; Matasci et al., 2018; Stackhouse et al., 2023; Tompalski et al., 2017). Combining different RS data types, like LiDAR and multispectral imagery, enhances vegetation classification accuracy (Dufour et al., 2013). Machine learning classification methods, such as random forest (RF), have become prominent in RS-based vegetation mapping (Alonzo et al., 2014; Dian et al., 2016; Huang et al., 2017; Liu et al., 2017; Matasci et al., 2018; Nevalainen et al., 2017; Qin et al., 2022; Richter et al., 2016; Shen & Cao, 2017; Shi et al., 2018; Sothe et al., 2019; Zhong Xu et al., 2020). The choice between cell-based and object-based approaches for tree-level RF classifications varies depending on study factors like computational power and tree species composition; in landscapes with diverse tree 29 species, using a cell-based approach can mitigate potential scale-related issues in object-oriented crown delineation (Laslier et al., 2019). This study aims to classify common tree species within the riparian environments of 24 selected tributaries of the Okanagan River system within the Okanagan Basin, British Columbia (BC). The goal is to develop a reproducible methodology using RS applications, including high-resolution RGB imagery and LiDAR data, to classify native riparian tree species. The methodology offers a cost-effective approach for fine-scale riparian assessment, with the potential for broader application in other global riparian environments. The resulting classification and probability maps can support informed decision-making for riparian fish habitat conservation, infrastructure development, protection, and restoration projects, addressing critical needs in the Okanagan to address riparian vegetation management through an improved understanding of riparian forest composition. 2.2 Study area The Okanagan basin is approximately 863,500 ha and 200 km in length. It includes the regional districts of North Okanagan, Central Okanagan, and Okanagan Similkameen, which have a 2023 estimated population of 422,124 (Statistics Canada, 2023). Agriculture plays a vital role in the regionâs economy, as vineyards, orchards, and wineries thrive due to the beneficial climate with favourable glacially and fluvially influenced soil conditions (Roed & Greenough, 2014). Tourism is also a major economic driver, attracting visitors with ample opportunities for winery tourism and both summer and winter outdoor recreation activities, among other attractions. In higher elevation areas of the basin, forest harvest operations are common, and are serviced by many unpaved service roads. Elevation in the Okanagan ranges from 270 m to 2300 m. Okanagan Lake occupies the majority of the basinâs bottom, and other large lakes include Wood Lake, Kalamalka Lake, Swan 30 Lake, Skaha Lake, Vaseux Lake, and Osoyoos Lake (Appendix A Figure A1). Of the tributaries that drain into the Okanagan River system, which itself encompasses any creeks that either enter the Okanagan River directly or one of Okanagan Lake, Skaha Lake, Vaseux Lake, or Osoyoos lake, some of the largest include the Mission Creek Watershed (84,510 ha), the Trout Creek watershed (74,645 ha), and the Vernon Creek Watershed (75,245 ha), which includes B.X, Coldstream, Upper Vernon, Middle Vernon, and Lower Vernon watersheds. Many geomorphic features of the basin have formed through glacial or fluvial activity and riparian environments found in the lower elevations of the Okanagan exhibit distinct features, including large alluvial plains and deep and narrow canyons formed by rapidly decreasing lake baseline water levels following the last ice age and residual isostatic rebalancing (Roed & Greenough, 2014). The Okanagan is characterized by a semi-arid climate, featuring warm summers and mild winters. Daytime maximum temperatures often surpass 30°C and occasionally reach 40°C in the summer, while average daily temperatures can differ by as much as 4°C between the south and north during the growing season, and winters experience freezing periods where temperatures can reach â25°C (Rayne & Forest, 2016). The Okanagan experiences a two-peak annual precipitation pattern, with the first peak occurring during the winter due to Pacific storms and the second peak in the summer driven by convective processes (Merritt et al., 2006). However, stream runoff patterns are heavily influenced by spring snowmelt, which strongly impacts streamflow dynamics throughout the region. Precipitation in the Okanagan ranges from approximately 320 mm per year in the south to 480 mm per year in the north (Rayne & Forest, 2016). Lower elevation areas of the Okanagan (< 610 m) have sparse vegetation due to higher summer evaporation compared to rainfall, while higher elevations (> 1,220 m) support sub-alpine vegetation and contribute substantially to spring runoff due to greater rainfall (Merritt et al., 2006). Regional water demands are substantial, and the Okanagan possesses the highest ratio of population to annual surface water yield among all Canadian basins (Statistics Canada, 2016). 31 The Okanagan Basin experiences natural disturbances from wind, landslides, insects, diseases, and wildfires, which can be influenced or intensified by human activities like logging, road construction, and anthropogenic development. High fuel loads and increasingly common summer drought conditions in the region contribute to significant wildfires (Pike et al., 2010a), such as the Kelowna-region Okanagan Mountain Park Fire of 2003 and McDougall Creek Wildfire of 2023. Insect outbreaks, such as mountain pine beetle infestations, affect many of the higher elevation forests of the Okanagan (Pike et al., 2010a; Stahl et al., 2006). There are approximately 11,667 km of streams within the Okanagan. Okanagan riparian environments exhibit diverse tree species, where black cottonwood (Populus balsamifera ssp. trichocarpa) is abundant across various elevations, and birch (Betula spp.) is common at lower elevations. Conifers like ponderosa pine (Pinus ponderosa) and interior Douglas-fir (Pseudotsuga menziesii var. glauca), are sparsely found in Okanagan riparian environments, while western redcedar (Thuja plicata) becomes more abundant in mid-elevation riparian environments. At higher elevations, streamside subalpine fir (Abies lasiocarpa) and spruce (Picea spp.) species are common. At lower elevations, urban and agricultural development have led to the loss of riparian vegetation in some areas, replaced by non-native tree species like tree-of-heaven (Ailanthus altissima) and weeping willow (Salix babylonica). In rural higher elevation riparian environments range livestock grazing is the greatest pressure forest understory plants and young tree saplings face, particularly fast-growing deciduous species such as black cottonwood. The loss and fragmentation of riparian and other terrestrial ecosystems within the Okanagan have endangered many communities of flora and fauna (Egan et al., 1997; Lea, 2008; Rae, 2005). In the Thompson-Okanagan region, the population is projected to increase from 618,000 people, to 738,000 people between 2020 and 2040 (Ip & Lavoie, 2019), which will increase pressures on Okanagan ecosystems. The remaining Okanagan riparian environments are important to preserve for the unique ecosystem services provided by these environments and 32 their vegetation (Naiman et al., 1993), including supporting biodiversity through habitat creation, providing wildlife corridors, and aiding in pollination and seed dispersal (Larson & Larson, 1997; Naiman & Décamps, 1997); enhancing water quality by filtering sediments and pollutants and promoting nutrient cycling (Honey-Rosés et al., 2013); mitigating flood risks by slowing water flow into streams (Barling & Moore, 1994); regulating climate through evapotranspiration, moderating temperature, and sequestering carbon (Klemas, 2014); and contributing to cultural significance by supporting recreation, tourism, cultural heritage, and educational opportunities (Okanagan Nation Alliance et al., 2019; Vollmer et al., 2015). Fish-bearing tributaries of the Okanagan River system are considered in this study due to the local importance of monitoring and managing riparian vegetation to increase or retain favourable conditions for use by declining anadromous species within the region (Okanagan Nation Allicance, 2017), as well as other fauna that rely on riparian habitat (Forrester et al., 2020). Large cottonwood snags and live cottonwoods, which grow rapidly within Okanagan riparian environments and have a relatively short lifespan (~ 100 to 200 years; Braatne et al., 1996), provide important habitat for numerous species of birds, reptiles, mammals, fish, amphibians, and invertebrates (Cannings, 2000; S. Cohen & Kulkarni, 2001; Egan et al., 1997; Forrester et al., 2020; Kelsey & West, 1998; Nelitz et al., 2007; Rae, 2005; Ramey & Richardson, 2017; Rivard-Sirois et al., 2018). Specifically, the study area focuses on riparian areas (represented by a 100 m buffer applied to stream line segments), covering the B.X., Bellevue, Coldstream, Eneas, Inkaneep, Lambly, Leon, McDougall, Mill, Mission, Naramata, Peachland, Penticton, Powers, Prairie Valley, Shingle, Shorts, Shuttleworth, Trepanier, Trout, Vaseux, Upper Vernon, Middle Vernon, and Lower Vernon watersheds (Figure 2.1, Table 1.2). 33 Figure 2.1. The study area includes 24 tributaries of the south-flowing Okanagan River system within the Okanagan Basin, BC. These tributaries are fish-bearing (Ministry of Forests Lands Natural Resource Operations and Rural Development, 2018; Rae, 2005), which signals the importance of monitoring and managing the riparian vegetation within these watersheds (Okanagan Nation Allicance, 2017). 34 2.3 Data 2.3.1 Training Data The tree species of interest for this study are black cottonwood (Populus balsamifera ssp. trichocarpa), interior Douglas-fir (Pseudotsuga menziesii ssp. glauca), lodgepole pine (Pinus contorta), ponderosa pine (Pinus ponderosa), white spruce (Picea glauca), Engelmann spruce (Picea engelmannii), subalpine fir (Abies lasiocarpa), trembling aspen (Populus tremuloides), western larch (Larix occidentalis), western red cedar (Thuja plicata), and various willow (Salix spp.) species. Due to common hybridization between Englemann spruce and white spruce, they were combined into one spruce class (Table 2.1). Tree species training data were collected via in-situ field visits over the course of the summers of 2021 and 2022. Locations were selected based accessibility of riparian areas (e.g., on public land and accessible by road) and within as many study watersheds and elevations as possible. At each location, an effort was made to record tree species that were representative of the forest at that location (i.e., if 60% of a stand was ponderosa pine and 40% was interior Douglas-fir, then the proportions of recorded species would be similar). Tree locations were collected with a GPS and the species type was recorded, along with additional explanatory information such as tree height, approximate crown width, elevation, and surrounding shrub and herb density. Less common native tree species that either tend to grow beneath the canopy of larger trees, such as birch and tree-height (â¥ 10 m) alder (Alnus spp.), or are rarely found at the upper limits of high-altitude forests (e.g., alpine larch (Larix lyallii) and whitebark pine (Pinus albicaulis)) were not considered (Demarchi, 2011). Coordinates of infrastructure, mainly buildings, were subsequently collected using imagery visualized in GIS to help inform an additional class in the model. The City of Kelowna open-access Tree Inventory of native species (locations and species) were used to supplement the field data (Table 2.1). 35 The training dataset included a total of 1642 observations. Any tree coordinates that had NA values (n = 5) were removed, resulting in 1637 observations. Then, the training data were filtered using a canopy height model (CHM), generated from LiDAR data to identify and remove observations where the CHM value was less than or equal to 2 m (n = 26), resulting in 1611 observations. Of the remaining 1611 observations, 1467 of those were tree observations, including ponderosa pine (n = 248), interior Douglas-fir (n = 238), and black cottonwood (n = 216), spruce (n = 202), lodgepole pine (n = 112), western redcedar (n = 99), willow (n = 96), trembling aspen (n = 94), subalpine fir (n = 90), and western larch (n = 72), while the remaining non-tree observations represented infrastructure (n = 144; Table 2.1). 36 Table 2.1. The number of trees included in the training database, as well as other relevant information. All tree coordinates (1467 features total) are located within the study area watersheds. Tree observations were collected from field work and existing geospatial databases. An additional 144 infrastructure class observations are not listed. Tree Species Class Total Observations Field Collected Observations City of Kelowna Tree Inventory Observations Tree Species Included Within Class black cottonwood 216 185 31 Populus balsamifera ssp. trichocarpa trembling aspen 94 89 5 Populus tremuloides subalpine fir 90 90 Abies lasiocarpa western redcedar 99 99 Thuja plicata interior Douglas-fir 238 105 133 Pseudotsuga menziesii var. glauca western larch 72 67 5 Larix occidentalis lodgepole pine 112 112 Pinus contorta ponderosa pine 248 69 179 Pinus ponderosa spruce 202 202 Picea engelmannii, Picea glauca, and hybrid spruce willow 96 81 15 Salix spp. (including: Salix babylonica, Salix lucida ssp. lasiandra, Salix scouleriana, Salix bebbiana, etc.) infrastructure 144* * Infrastructure observations were collected using imagery and GIS visualization. 37 2.3.2 LiDAR In 2018 and 2019, the Okanagan Basin Water Board Floodplain Mapping Project acquired aerial LiDAR data flown 1000 m above ground level for the whole Canadian portion of the Okanagan Basin (~863,500 ha), using a Riegl LMS VQ780i system. LiDAR was collected between April 2nd and October 16th in 2018, and between June 9th and June 12th 2019, and followed version 3.0 of BC provincial LiDAR collection specifications (GeoBC, 2023). Data were collected with a maximum scan angle of ± 30°, with a beam divergence of â¤ 0.25 milliradians at the point where the beam intensity has dropped to 1/e2 of its maximum value. The 2018 LiDAR data had an average of 12 pts/m, while the 2019 LiDAR returns had an average of 18 pts/m, with a combined point cloud density of 30 pts/m. The LiDAR point cloud included the spatial coordinates (x, y), elevation (z) represented as metres above sea level, intensity (i) where a higher value represents greater reflectivity or brightness of an object, and return number (rn), which explains the partial returns of a pulse that may interact with multiple objects and can help characterize multi-story forest. Points were classified (c) as unclassified (c = 1), ground returns (c = 2), or noise (c = 7), which was filtered from the dataset. Additionally, a 1 m resolution Digital Terrain Model (DTM) was derived from the LiDAR point cloud for the study area. 2.3.3 RGB Imagery Red, green, and blue (RGB) imagery is often used to characterize vegetation health, density, and composition based on the vegetationâs unique visual-spectrum signatures. The decision to use RGB bands exclusively in RS applications is a cost-effective approach, as RGB imagery is widely available and is generally more affordable than multispectral or hyperspectral imagery that includes near-infrared (NIR) bands. Additionally, RGB data is often collected in conjunction with aerial LiDAR data acquisition. 38 Monthly cloud-free image composites were acquired from the Planet PlanetScope Visual basemaps program, and contained 4 bands: band 1 â red (R), band 2 â green (G), band 3 â blue (B), and the unused band 4 â alpha. Monthly basemap quads (n = 87), with a resolution of approximately 3 m, were downloaded for the study area for March 2019 to November 2019 to match acquisition dates of the LiDAR data. Monthly image composites were acquired to represent differences in reflectance and composition of vegetation across seasons, influenced by factors like leaf growth, senescence, and phenological shifts. 2.3.4 Ancillary data The Riparian Management Area (RMA) system is used in BC to define the extents of riparian buffers used for protection of riparian ecosystems (Pike et al., 2010b), which requires estimates of stream locations. The Freshwater Atlas is a standardized dataset that maps hydrologic features such as streams, waterbodies, and watersheds within the province (Gray, 2010). Streams, waterbodies, and watershed boundaries information were collected from the BC Open Data Catalogue in order to set the bounds of the study area and understand the distribution of streams and waterbodies in the selected watersheds. 2.4 Methods 2.4.1 LiDAR metrics Several LiDAR vegetation structure metrics were derived to characterize the height, density, complexity, heterogeneity, and coverage of riparian tree species (Huylenbroeck et al., 2020). LiDAR metrics were calculated within 3 m raster cells using the lidR package in R (Roussel et al., 2020). Attributes from the LiDAR point cloud used to calculate metrics include the return height (z), return intensity (i), return number (rn) and return classification (c). Before metrics were 39 calculated, the point cloud was normalized by the DTM so that z values were representative of metres above ground instead of metres above sea level. Explanatory metrics (n = 18) were derived from the LiDAR return point cloud data to characterize riparian tree-height vegetation characteristics (Appendix A Table A1). LiDAR metrics specifically considered returns where return height was greater than 2 m, in an attempt to characterize the tree-height vegetation and exclude most shrub and herbaceous returns (Laslier et al., 2019; Matasci et al., 2018; Shen & Cao, 2017; Stackhouse et al., 2023; Thompson et al., 2016; Tompalski et al., 2017). After correlation between metrics were considered (Pearson correlation coefficient >= 0.7; Appendix A Figure A3), seven metrics were retained for inclusion in the RF model (Table 2.2). Retained LiDAR metrics representative of the vertical distribution of vegetation include the 75th percentile of return heights (z_75pct; Evans et al., 2009; Stackhouse et al., 2023), the kurtosis of return heights (z_Kurt; Evans et al., 2009; Matasci et al., 2018), and the standard deviation of return heights (z_SD; Evans et al., 2009; Stackhouse et al., 2023). Canopy cover (rn_CC; Stackhouse et al., 2023; Tompalski et al., 2017) represents the proportion of both returns greater than 2 m and returns classified as ground. The mean return intensity (i_Mean; Evans et al., 2009; Liu et al., 2017), the kurtosis of return intensity (i_Kurt; Evans et al., 2009), and the standard deviation of return intensity (i_SD; Evans et al., 2009; Liu et al., 2017) represent the density and surface reflectivity of tree height vegetation foliage (e.g., needles, leaves, and branches ;Evans et al., 2009). Given the variation in tree height among individual tree species within the study area, LiDAR metrics where formulas outputted a value in metres were normalized by maximum height, including z_75pct, z_Kurt, and z-SD. Maximum tree height was calculated as the 95th percentile of all return heights (Shen & Cao, 2017; Stackhouse et al., 2023; Tompalski et al., 2017; Zhong Xu et al., 2020). 40 2.4.2 RGB imagery indices Multiple indices were calculated from the acquired PlanetScope Visual basemaps. Indices were calculated using RGB values for each month and each index was summarized based on the mean, minimum, and coefficient of variation calculated from the nine surfaces, allowing differences in reflectance and composition of vegetation across seasons to be represented. Once indices were generated, they were resampled to the 3 m resolution LiDAR metrics. Thirteen explanatory indices, each of which had the mean, minimum, and coefficient of variation derived over the nine selected monthly surfaces (39 total statistical measures generated), were derived from the RGB visual data to characterize riparian tree-height vegetation reflective properties (Appendix A Table A1). Because of ambiguity in the physical interpretation of RGB indices within existing literature, many RGB indices were derived and subsequently assessed using a Pearson pairwise correlation to ensure that each index provided distinct information relevant to the riparian forests of this study. RGB indices included: i. The brightness index (BRI; Fraser et al., 2017; Michez et al., 2016), which assesses the overall reflection of tree-height vegetation within the visible portion of the electromagnetic spectrum. ii. The red-blue ratio index (RBRI; Michez et al., 2016; Wolter & Townsend, 2011), assess leaf senescence of tree-height vegetation. iii. The green-red ratio index (GRRI; Coops et al., 2006; Michez et al., 2016), which characterizes the spectral change in foliage from green to red. iv. The excess of green index ( EXG; Dale et al., 2020; Xue & Su, 2017) and the green leaf index (GLI; Barbosa et al., 2019; Xue & Su, 2017), which assesses the amount of green reflectance against the contributions of red and blue bands. 41 v. The red-green-blue vegetation index (RGBVI; Barbosa et al., 2019; Bendig et al., 2015), which assesses the amount of green reflectance against the contributions of red and blue. T vi. The normalized green-red vegetation index (NGRVI; Michez et al., 2016; Xue & Su, 2017) and the modified green-red vegetation index (MGRVI; Barbosa et al., 2019; Bendig et al., 2015), which assesses the relative intensity of green and red reflectance. vii. The normalized green-blue difference index (NGBDI; Michez et al., 2016; Xue & Su, 2017), which characterizes the relative intensity of green and blue reflectance. viii. The visual atmospheric resistance index (VARI; Dale et al., 2020; Xue & Su, 2017), which assesses the balance between green and red reflection with blue influence, while controlling for atmospheric effects sensitivities. ix. The visual normalized difference vegetation index (VNDVI; Costa et al., 2020; Gil & Pacheco, 2021), which is a proxy for the normalized difference vegetation index (NDVI) and represents vegetation health. After correlation between indices was considered (Pearson correlation coefficient >= 0.7; Appendix A Figure A3), Sixteen statistical measure surfaces representing 11 indices were retained for inclusion in the RF model (Table 2.2). 42 Table 2.2. Eighteen covariates, including seven LiDAR metrics and 11 RGB indices, that were included in the RF model (Pearson correlation coefficient < 0.7). LiDAR return attributes included metres above ground (z), intensity (i), and return number (rn). RGB Imagery bands included red (R), green (G), and blue (B), where each index was calculated over nine rasters each representing a month between March and November, allowing the mean (Mean), the minimum (Min), and the coefficient of variation (CoVar) to be calculated for each index. Covariate Formula Statistical Measures Reference LiDAR Metrics 75th percentile of return heights (z_75pct) z_75pct = (75th percentile of z > 2m) / z_Max N/A Evans et al., 2009; Stackhouse et al., 2023 Kurtosis of return heights (z_Kurt) z_Kurt = (kurtosis of z > 2m) / z_Max N/A Evans et al., 2009; Matasci et al., 2018 Standard deviation of return heights (z_SD) z_SD = (standard deviation of z > 2m) / z_Max N/A Evans et al., 2009; Stackhouse et al., 2023 Canopy Cover (rn_CC) rn_CC = (count of z > 2 & rn == 1) / (count of rn == 1) N/A Stackhouse et al., 2023; Tompalski et al., 2017 Mean of return intensities (i_Mean) i_Mean = mean of i where z > 2m N/A Evans et al., 2009; Liu et al., 2017 Kurtosis of return intensities (i_Kurt) i_Kurt = kurtosis of i where z > 2m N/A Evans et al., 2009 Standard deviation of return intensities (i_SD) i_SD = standard deviation of i where z > 2m N/A Evans et al., 2009; Liu et al., 2017 43 Covariate Formula Statistical Measures Reference RGB Indices Brightness (BRI) BRI = (R + G + B) / 3 Min Fraser et al., 2017; Michez et al., 2016 Red-blue ratio index (RBRI) RBRI = R / B Min Michez et al., 2016; Wolter & Townsend, 2011 Green-red ratio index (GRRI) GRRI = R / G Mean, CoVar Coops et al., 2006; Michez et al., 2016 Excess of Green (EXG) EXG = (G * 2) - R â B Mean, Min Dale et al., 2020; Xue & Su, 2017 Green leaf index (GLI) GLI = ((G * 2) - R - B) / ((G * 2) + R + B) Min Barbosa et al., 2019; Xue & Su, 2017 Red-green-blue vegetation index (RGBVI) RGBVI = (G - (B * R)) / ((G2) + (B * R)) Mean, CoVar Barbosa et al., 2019; Bendig et al., 2015 Normalized green-red vegetation index (NGRVI) NGRVI = (G - R) / (G + R) CoVar Michez et al., 2016; Xue & Su, 2017 Modified green-red vegetation index (MGRVI) MGRVI = (G2 â R2) / (G2 + R2) CoVar Barbosa et al., 2019; Bendig et al., 2015 Normalized green-blue difference index (NGBDI) NGBDI = (G - B) / (G + B) CoVar Michez et al., 2016; Xue & Su, 2017 Visual atmospheric resistance index (VARI) VARI = (G - R) / (G + R - B) Min, CoVar Dale et al., 2020; Xue & Su, 2017 Visual near difference vegetation index (VNDVI) VNDVI = (0.5268 * ((R-0.1294) * (G0.3389) * (B-0.3118)) Min, CoVar Costa et al., 2020; Gil & Pacheco, 2021 44 2.4.4 Random Forest A RF model was used to classify riparian tree-height vegetation within the Okanagan study watersheds. The RF model was trained using the field-collected tree coordinates supplemented with data from the City of Kelownaâs open-source Tree-Inventory tree coordinates (Figure 2.2). Each of the 23 explanatory covariatesâ values were extracted at each tree coordinate, to explain the attributes of each sample tree. 45 Figure 2.2. Workflow of the Random Forest classification model for Okanagan riparian forests, BC, Canada, showing how acceptability was assessed based on (a) adequacy of confusion matrix, variable importance, and partial dependence plots in demonstrating prediction ability for tree species, and (b) the ability of output classification to reflect expected tree species distribution. 46 2.4.4.2 Random forest hyperparameter tuning Okanagan riparian tree species were classified using a random forest (RF) model. The RF model was trained and model hyperparameters were tuned using mlr3 in R (Lang et al., 2019). The hyperparameters considered included the number of randomly selected explanatory covariates evaluated to determine how to split a node of a decision tree (mtry), the number of decision trees that are grown during training (num.trees), the size of the bootstrap sample used to train each decision tree (sample.fraction), the maximum number of splits in a decision tree before reaching a node (max.depth), and the minimum number of sample points that must be present in a node to allow for it to be split further (min.node.size), and these hyperparameters were tuned with a grid search (Table 2.3). The grid search was set to evaluate 200 iterations, which refers to training and testing the RF model with a specific combination of hyperparameters to measure its performance. To account for potential spatial autocorrelation, a spatial k-fold cross-validation resampling technique was used to split the data into 10 folds and then repeatedly train the model where each time the data from all but one fold is used in the model (Meyer et al., 2019). 47 Table 2.3. Hyperparameter gird search ranges and selected values used to train the RF classification model. Hyperparameter Description Range Selected Value mtry The number of randomly selected explanatory covariates evaluated to determine how to split a node of a decision tree. 4 - 23 4 num.trees The number of decision trees that are grown during training. 1,000 - 25,000 1000 sample.fraction The size of the bootstrap sample used to train each decision tree. 0.5 - 0.8 0.5 max.depth The number of splits that are based upon one explanatory covariate, called nodes, that a decision tree reaches. 1 - 10 10 min.node.size The minimum number of sample points that must be present in a node to allow for it to be split further. 1 - 10 3 2.4.4.3 Model evaluation A confusion matrix was used to determine the model accuracy per class (Table 2.4). Producer accuracy (PA) and user accuracy (UA) were evaluated for each class. PA provides a measure of how well the model can classify measures of a particular class (i.e., recall), while the user accuracy provides a measure of the accuracy of the predicted class for each sample (i.e., precision). The overall accuracy of the model was determined by summing all of the correctly predicted (true positive) values within the confusion matrix, and dividing them by the total number of observations. A variable importance (VI) plot was created to evaluate the importance of each covariate through the mean decrease in accuracy of the Gini index. The Gini impurity index VI score is calculated by weighing the sums of Gini impurity scores from the nodes of the decision trees in the RF model. A partial dependence plot (PDP) was also created for each explanatory covariate 48 to visualize the effect of each explanatory covariate on class predictions when all other covariates are held at their mean (Park et al., 2021). 2.4.4.4 Model prediction The tuned RF model was used to predict the probability for each class (black cottonwood, trembling aspen, subalpine fir, western redcedar, interior Douglas-fir, western larch, lodgepole pine, ponderosa pine, spruce, willow, and infrastructure) in the 3 m cells of each watershedâs riparian environments, where riparian environments were defined as cells occurring within 100 m of streams. Using a 25% threshold, each cell was classified by identifying the class with the highest probability. If no class had a probability â¥ 25%, then that cell was not classified. Similarly, cells with no LiDAR returns â¥ 2 m (e.g., the cutoff used to define trees) were also not classified. To summarize the predictions across the study area, cells were assessed using six BEC zones, as some riparian species are more commonly found in specific zones (Table 1.2, Figure 2.1). 2.5 Results 2.5.1 Model performance The RF model had an overall accuracy of 96.03%, an average user accuracy (UA) of 95.17%, and an average producer accuracy (PA) of 96.96%. All classes had a PA greater than 94%, except for the spruce class (81.93%), and a UA greater than 91%, except for the subalpine fir class (82.22%). The confusion matrix shows that 13 subalpine fir class observations were misclassified as spruce, while no spruce observations were misclassified as subalpine fir (Table 2.4). 49 Table 2.4. A confusion matrix visualizing the prediction ability of the RF model over each class. This confusion matrix finds the overall accuracy of the model, as well as the user accuracy (UA) and producerâs accuracy (PA) for each class1. Predicted Class True Class Act At Bl Cw Fdi Lw Pl Py S W IFST PA n Act 206 1 1 4 4 .994 216 At 88 1 4 1 .936 94 Bl 75 2 13 .833 90 Cw 98 1 .99 99 Fdi 1 230 2 5 .966 238 Lw 69 1 2 .958 72 Pl 1 101 10 .902 112 Py 1 246 1 .996 248 S 1 1 1 199 .985 202 W 1 1 1 2 91 .948 96 IFST 144 1 144 UA .995 .967 .987 1 .991 .986 .953 .965 .833 .989 1 Overall Accuracy 96.03% 1Classes include black cottonwood (Act), trembling aspen (At), subalpine fir (Bl), western redcedar (Cw), interior Douglas-fir (Fdi), western larch (Lw), lodgepole pine (Pl), ponderosa pine (Py), spruce (S), willow (W), and infrastructure (IFST). 2.5.2 RF model variable importance Of the 23 covariates used in the RF model, there were seven LiDAR-generated covariates and 16 RGB-generated covariates. In the top 50% of VI scores, there were five LiDAR-generated 50 covariates including z_SD (the most influential variable in the model), i_Mean, z_CC, i_SD, and z_75pct, as well as seven RGB-generated covariates including VNDVI_CoVar, NGBDI_CoVar, VNDVI_Min, RGBVI_Mean, GLI_Min, RGBVI_CoVar, and VARI_Min. LiDAR variables were generally more influential when compared to RGB variables (Figure 2.3). Figure 2.3. Variable importance (VI) plot displaying each of the 23 covariates used in the model on the x-axis, and the associated score calculated by weighting the sums of Gini impurity scores from the nodes of the decision trees in the RF model. 2.5.3 Partial dependence plots A partial dependence plot (PDP) shows how the prediction of each class changes over the range of values of a specific variable, where all other variables are held constant at the mean. 51 This allows the effect of a variable on the prediction to be interpreted, independent of the influence of other variables. PDPs were created for the top eight most influential variables (Figure 2.4). There were general patterns of the range of predicted probabilities for each class across most PDPs. The lowest predicted probabilities were consistently the trembling aspen, subalpine fir, western redcedar, western larch, lodgepole pine, and willow classes. Classes with higher predicted probabilities included black cottonwood, interior Douglas-fir, ponderosa pine, spruce, and the infrastructure class across all covariates (Figure 2.4). The interior Douglas-fir class had the highest predictive probability at a given covariate value in all 23 PDPs, ponderosa pine had the highest predictive probability at a given covariate value in nine of 23 PDPs, spruce had the highest predictive probability at a given covariate value within three of 23 PDPs, and black cottonwood and infrastructure each had the highest predictive probability at a given covariate value within two of 23 PDPs. The interior Douglas-fir class was the only class which had the highest predictive probability regardless of the covariate value, and did so within 10 of 23 PDPs while not having any intersections with other class-lines within those PDPs. The most influential variable was standard deviation of normalized return heights â¥ 2 m above the ground (z_SD, Figure 2.4A), which explains the variation in vertical structure of the vegetation within a particular cell. Infrastructure, ponderosa pine, trembling aspen, and subalpine fir classes had higher predicted probabilities when z_SD was low (< ~0.25), and interior Douglas-fir, black cottonwood, spruce, lodgepole pine, willow, western redcedar and western larch classes had higher predicted probabilities when z_SD was higher (>0.25). The coefficient of variation of monthly visual near difference vegetation indices (VNDVI_CoVar, Figure 2.4B) is an index designed to highlight vegetation greenness variation across the months of March to November (Carlson & Ripley, 1997; Costa et al., 2020), and was the second-most influential variable. At lower VNDVI_CoVar values (close to 0), classes such as 52 interior Douglas-fir, ponderosa pine, and infrastructure had higher predicted probabilities, while classes such as spruce, subalpine fir, and lodgepole pine had their highest predicted probabilities at higher VNDVI_CoVar values (> ~0.25). Classes such as black cottonwood, trembling aspen, willow, and western redcedar had their highest predicted probabilities with VNDVI_CoVar values between ~0.05 and ~0.25. Raw intensity LiDAR returns are influenced by vegetation density, broadness of vegetation foliage, or exposed ground beneath vegetation. The mean of return intensities â¥ 2 m above the ground (i_Mean, Figure 2.4C) influenced higher predicted probabilities in black cottonwood, infastructure, western larch, and western redcedar classes when the value was low (< 20,000), higher predicted probabilities in trembling aspen, willow, ponderosa pine, and lodgepole pine when i_Mean values were between 20,000 and 40,000, and higher predicted probabilities in interior Douglas-fir, spruce, and subalpine fir at higher i_Mean values (> 40,000). The coefficient of variation of monthly normalized green blue difference indices (NGBDI_CoVar, Figure 2.4D) is an index designed to highlight the variation in the difference between green and blue reflectivity of vegetation across the months of March to November. At lower values of NGBDI_CoVar (< 0.5), classes of interior Douglas-fir, black cottonwood, ponderosa pine, willow, and western redcedar all had higher predicted probabilities. At higher NGBDI_CoVar values (> 0.5), classes of spruce, infrastructure, trembling aspen, lodgepole pine, and subalpine fir had higher predicted probabilities. Canopy Cover (z_CC; Figure 2.4E) explained the proportion of the ground vegetation covers from above. At lower z_CC values (< 0.75), the classes of ponderosa pine, black cottonwood, trembling aspen, lodgepole pine, subalpine fir, and western larch had higher predicted probabilities. At higher z_CC values (> 0.75) the classes of interior Douglas-fir, infrastructure, spruce, willow, and western redcedar had higher predicted probabilities. 53 The standard deviation of return intensities (i_SD; Figure 2.4F) explains the variation in returns that are â¥ 2 m above the ground. When i_SD values were lower than ~7,500, classes of black cottonwood, infrastructure, willow, and western larch had their highest predicted probabilities. Between i_SD values of ~7,500 and ~12,500, the ponderosa pine, trembling aspen, and lodgepole pine classes had their highest predicted probabilities. The minimum of monthly visual near difference vegetation indices (VNDVI_Min, Figure 2.4G) is an index representing the minimum vegetation greenness across the months of March to November (Carlson & Ripley, 1997; Costa et al., 2020). When VNDVI_Min was low (< 0.35) spruce, infrastructure, trembling aspen, lodgepole pine, subalpine fir, and western larch classes had higher predicted probabilities. When VNDVI_Min was between 0.35 and 0.4, the black cottonwood, interior Douglas-fir, and willow classes had their highest predicted probabilities. Above VNDVI_Min values of 0.4, classes of ponderosa pine and western redcedar had their highest predicted probabilities. The mean of monthly red green blue vegetation index (RGBVI_Mean; Figure 2.4H) had the presence of a very large outlier value, making it difficult to interpret trends using the PDP. When RGBVI_Mean was low (close to 0), classes of infrastructure, interior Douglas-fir, ponderosa pine, spruce, and black cottonwood have their highest probabilities. As RGBVI_Mean values increase, the infrastructure class has much lower predicted probabilities. 54 Figure 2.4. Partial dependence plots (PDPs) that display each of the eleven classes of black cottonwood (Act), trembling aspen (At), subalpine fir (Bl), western redcedar (Cw), interior Douglas-fir (Fdi), western larch (Lw), lodgepole pine (Pl), ponderosa pine (Py), spruce (S), willow (W), and infrastructure (IFST), for each of the eight most important covariates based on the Gini impurity scores found through the RF model, including z_SD (A), VNDVI_CoVar (B), i_Mean (C), NGBDI_CoVar (D), z_CC (E), i_SD (F), VNDVI_Min (G), and RGBVI_Mean (H). The range of training samplesâ covariate values is given as small vertical black lines along the x-axis of each PDP to contextualize the distribution of data across a covariateâs range of values. 55 2.5.4 Riparian vegetation classification output For the riparian environments (100 m stream buffers) of the study area (129,802 ha), 85,679 ha (66% of the area) were classified as one of the 11 classes (Figure 2.5). The remaining 44,123 ha did not receive class probability values because they did not have predicted probabilities â¥ 25% (17,303 ha or 13.33% of the area) or did not contain LiDAR returns above 2 m (26,820 ha or 20.66% of the area). Across all watersheds, the spruce class was the most predicted class (54.16% of classified cells), followed by western redcedar (15.01% of classified cells), lodgepole pine (7.46% of classified cells), black cottonwood (6.76% of classified cells), interior Douglas-fir (5.44% of classified cells), subalpine fir (5.35% of classified cells), trembling aspen (2.58% of classified cells), ponderosa pine (1.86% of classified cells), willow (0.55% of classified cells), infrastructure (0.55% of classified cells), and western larch (0.4% of classified cells; Figure 2.6). 56 Figure 2.5. Area, in hectares, of classified and unclassified 3 m cells within 100 m of streams of the study area watersheds. Cells which were within riparian environments (100 m of streams), and also had at least one LiDAR return within the cell that was greater than 2 m in height, are summarized here. Some cells were not classified due to not having a class probability that was â¥ 25% (bottom column). 57 Figure 2.6. Predicted class for 100 m stream buffers in the study area watersheds. The Hardy Falls inset shows an example of the prediction at a low elevation next to Okanagan Lake, the Mill Creek Falls inset shows an example of the prediction at a low elevation further inland from major lakes of the region, and the Greyback Reservoir inset shows a higher elevation prediction. 58 When the classification output is considered from the lowest to highest elevation, each class shows a unique trend that reflects a tree species riparian ecosystem preference, and biogeoclimatic (BEC) zones can be used to loosely differentiate ecosystems based on elevation, climate, and dominant vegetation (Figure 2.6 and 2.10). In the Okanagan, Bunchgrass (BG) and Ponderosa Pine (PP) BEC zones represent the valley bottom elevations. Increasing in elevation, the Interior Douglas-fir (IDF) and Interior Cedar Hemlock (ICH) BEC zones separate BG and PP BEC zones from the highest elevation Montane Spruce (MS) and Engelmann Spruce â Subalpine Fir (ESSF) BEC zones. The black cottonwood class was predicted most in lower elevations (BG and PP), and was predicted less with increasing elevation. The ponderosa pine, interior Douglas-fir, willow, and infrastructure classes followed a similar pattern where the most of their classâs predictions occurred at lower elevations, and predictions decreased with elevation gained. The western redcedar class was predicted most within the middle-elevations (IDF and ICH), with slightly more predictions at lower elevations (BG and PP) than at higher elevations (MS and ESSF). trembling aspen class predictions occurred most in the middle-to-high elevations (IDF, ICH, and MS), and less at the lowest and highest elevations (BG/PP and ESSF). The spruce and subalpine fir classes were predicted most at the highest elevations (MS and ESSF), and were predicted less with decreasing elevation. The western larch class was never predicted over more than 1% of the classified area within any BEC zone, with the highest amount of area predicted within the MS BEC zone (0.74%; Figure 2.7). 59 Figure 2.7. A line graph that shows the proportion of classified cells for each BEC zone. BEC Zones are ordered along the x axis from lowest elevation on the left to highest elevation on the right to demonstrate how the model predicts at different elevations/regions. The amount of area of considered cells within each BEC zone is displayed in hectares beside each BEC zone name, to allow for an understanding of the difference in size between BEC zones of the study area. 60 2.6 Discussion The RF model effectively classified ~102,982 ha of riparian tree-height (> 2 m) vegetation for 11 classes within sub-watersheds of the Okanagan basin with a â¥ 25% threshold applied to the probability values. The inclusion of the â¥ 25% threshold helped limit classification errors related to prediction ambiguity. The high accuracy of this researchâs RF riparian forest classification model is comparable to riparian forest species composition studies that utilize a RF cell-based classification approach (Hayes et al., 2014; Laslier et al., 2019). Similar to this study, in both cases the use of RF classification models coupled with RS applications for riparian tree species delineation was effective for classifying riparian tree-species to high levels of accuracy (68% - 81%; Hayes et al., 2014; Laslier et al., 2019) and spatial resolution (< 3 m). However, neither of these studies used a combination of different RS data types, and instead employed only LiDAR (Laslier et al., 2019) or spectral (Hayes et al., 2014) datasets, which when combined can improve class prediction (Liu et al., 2017; Nevalainen et al., 2017; Qin et al., 2022; Shen & Cao, 2017; Shi et al., 2018; Zhong Xu et al., 2020). The RF model was heavily influenced by LiDAR-derived covariates, which represented four of the six most important covariates (Figure 2.3). The higher overall accuracy found within this research (96.03%) is evidence of the benefits of utilizing multiple types of RS datasets for generating RF covariates and explaining the distribution of riparian tree species. Many non-riparian forest classification studies that use RF modelling have employed multiple RS data types to achieve similar levels of classification accuracy found in this research (66.34% to 95.7%), and have also found that using multiple RS data types to inform classification improved the accuracy (Liu et al., 2017; Nevalainen et al., 2017; Qin et al., 2022; Shen & Cao, 2017; Shi et al., 2018; Zhong Xu et al., 2020). Other forest classification models that employ one RS data type can still 61 achieve very high accuracies, but generally require expensive and spatially limited hyperspectral data (e.g. 95.7%; Huang et al., 2017). This study is among the first to use multiple RS data types to inform a fine-scale, cell-based RF classification of riparian vegetation. Other studies that utilized RF modelling approaches for classifying riparian vegetation are not directly comparable for reasons related to using a binary riparian forest classification (Stackhouse et al., 2023), using a tree-segmentation approach instead of a cell-based approach (Michez et al., 2016), or organizing their output classes into ecosystem types instead of specific tree-species (Johansen et al., 2007; U. Nguyen et al., 2019; Thompson et al., 2016). One challenge of assessing riparian vegetation is the cost of data collection, whether in the field or RS data collection practices. Many riparian vegetation classification studies make the argument that the use of UAVs to collect data presents cheaper options than acquiring expensive fine-scale aerial or orbital RS data (Michez et al., 2016; U. Nguyen et al., 2019), though access to UAVs can be inherently expensive. Other riparian vegetation classification studies use freely available fine-scale aerial or orbital data to inform their classifications, though they differ from this study in only using a single data RS data source (e.g. LiDAR only; Stackhouse et al., 2023) or data that is only available in their country of study (e.g. National Agricultural Imagery Program spectral data; Hayes et al., 2014). In this research, an effort was made to use LiDAR and RGB spectral datasets as fine-scale open-source LiDAR datasets are increasingly becoming available, and it is common for fine-scale resolution imagery (RGB) to be collected in tandem. Additionally, this research is unique as it employed RGB spectral data, while other riparian vegetation classification studies commonly employ spectral datasets that contain NIR bands (Hayes et al., 2014; U. Nguyen et al., 2019; Richter et al., 2016). Other riparian vegetation classification studies that similarly included RGB-derived indices were not comparable as they generally included other 62 indices that required NIR data (Michez et al., 2013), or they were used to classify riparian forests based on vegetation health instead of vegetation species (Dunford et al., 2009). This research was able to classify broad reaches of riparian environments using one model (24 sub-watersheds), as opposed to classifying multiple sub-watersheds with different models (i.e. Stackhouse et al., 2023), which makes interpretation of species responses challenging. Tatum et al. (2021) claimed to be pioneers in using LiDAR for tree species modeling in Pacific Northwest riparian forests at a landscape scale (three sub-watersheds) using one classification model (a generalized linear model). Here, a RF model was used to address non-linearity and complex interactions between explanatory variables and the response, issues related to overfitting, and the ability to assess variable importance. Few studies have attempted to classify riparian forest tree species using RF approaches (Hayes et al., 2014; Laslier et al., 2019; Michez et al., 2016; Richter et al., 2016). Specifically in the Okanagan, the combination of LiDAR derived vegetation structure data with fine-scale RGB reflectance information facilitated prediction and mapping of riparian tree-height vegetation at fine spatial scales that have previously been unattainable. Currently, open-source provincial spatial datasets such as the vegetation resources inventory (VRI), terrestrial ecosystem mapping (TEM), and sensitive ecosystem inventory (SEI) are the available tools to understand forest vegetation in the Okanagan. For riparian environments specifically, these datasets only explain general ecosystem types (e.g. TEM and SEI), or classify multiple dominant tree-species within specific large elongated parcels around streams (e.g. VRI). Not only is the spatial accuracy of the landscape units in these three datasets insufficient for understanding riparian vegetation at fine spatial scales, but the temporal accuracy of classified tree species varies considerably, often dating back to the mid-1950s, particularly in the Okanaganâs valley bottom (Appendix A, Figure 5A). For these reasons, project managers seeking to understand riparian forest composition may 63 rely on expensive and time-consuming in-situ field-collection methods to attain a better understanding of present vegetation. Results of this RF classification approach improve upon the currently available spatial datasets (i.e., VRI) which can be used to describe riparian forest composition in British Columbia. VRI mapping is generally focused on timber resources at broad scales rather than riparian tree species. When considering landscape-level forest composition mapping, the use of RF modelling coupled with remotely sensed geospatial datasets, such as LiDAR and RGB imagery, can effectively explain and map forest composition based on vegetation structure and reflective properties (Huylenbroeck et al., 2020), overcoming limitations inherent in manual interpretation required of classification and mapping approaches like VRI. The use of ex-situ RS data also allowed privately owned or inaccessible riparian forests could be classified, which is not possible through traditional field-based approaches. While the output classification of this research can be useful to land managers for understanding the distribution of riparian tree species within a given Okanagan riparian forest, it is intended to be used as a tool to inform on riparian forest composition rather than the physical location of specific riparian trees. These Okanagan riparian forests are challenging to classify as they are commonly confined within narrow canyons, and also contain many different tree-height species in a multi-story canopy. Many tree species that are common to Okanagan riparian environments also have very small crowns, such as subalpine fir and younger trees with smaller crown diametres. Attempting to classify forested landscapes with smaller trees and multi-story canopies is difficult using moderate spatial resolution (e.g., 10-30 m) RS data. The use of finer spatial resolution spectral imagery has been shown to improve the accuracy of riparian forest RF classification models (Hayes et al., 2014), and the fine-spatial scale imagery used in this research (3 m) also allows for smaller-crowned trees to be adequately represented within the classification output. As black cottonwood (Populus balsamifera ssp. trichocarpa) were a focus species within this 64 research due to their importance in signifying riparian health and presence (Okanagan Nation Alliance et al., 2019), a cell-based approach was also beneficial as tree crown delineation struggles with tree species that do not commonly have vertical-structured stems (Laslier et al., 2019). The varying elevation and ecosystems of the Okanagan create conditions which can host diverse tree species. For example, lower elevation riparian environments can include common upland species like ponderosa pine and interior Douglas-fir, while higher elevation riparian forests are difficult to distinguish from upland forests due to similar species occurring in both, such as spruce, subalpine fir, and lodgepole pine. Black cottonwood is the only tree species that commonly reaches the top of the forest canopy and can be used to identify riparian environments at any elevation of the Okanagan, though they are much less common at higher elevations. While a cell-based classification approach has benefits in classifying deciduous and small-crowned species (e.g. subalpine fir), larger crowned species like black cottonwood require more training data to describe. For example, a very wide-crowned (e.g. ~ 20 m diameter) weeping willow (Salix babylonica) could be described by many cells (e.g. ~ 30 cells), meaning that each of those cells are describing different parts of the tree, such as the edges of the canopy and stem. Arguably, more training data is required to explain different areas of these larger treeâs crowns. In this study, an effort was made to collect sufficient training points in an attempt to characterize these large trees. Within riparian environments, vegetation types are heavily dependent on topographic characteristics, including height above the river, soil texture, and soil moisture (Dilts et al., 2010). Topographic metrics, easily produced from LiDAR derived digital terrain models (DTMs), would likely increase not only the modelâs classification accuracy, but also the accuracy of the output prediction. For example, in this study many cells that were expected (based on understanding of Okanagan riparian forests) to be classified as interior Douglas-fir were classified as western 65 redcedar. This was likely due to the co-occurrence and close proximity of interior Douglas-fir and western redcedar trees within riparian environments and overlapping canopies of training data points of those species. Topographic metrics that explain terrain and edaphic conditions related to aspect or moisture (e.g. topographic wetness index) may have helped to further separate co-occurring species. However, they were not used in this study as there was not enough tree observations to explain these topographic patterns within the training data and this remains an opportunity for future research. Okanagan riparian forests are expected to face increasing pressures related to changing climate and anthropogenic growth. Currently the Okanagan basin has the highest ratio of population to annual surface water yield when compared to other Canadian basins (Statistics Canada, 2016), and a rapidly growing population is expected to further increase the water-use related pressures that the basin is already experiencing. Projected population growth in the Thompson-Okanagan region anticipates an increase from 618,000 people to 738,000 people between 2020 and 2040 (Ip & Lavoie, 2019). While agricultural uses are responsible for the majority of the water withdrawal within the Okanagan, residential uses like lawn watering are the second largest water use category (Conrad & Yates, 2018), exemplifying how an increasing population will further strain water availability within the basin. Important riparian vegetation, such as black cottonwood, rely on flooding events to germinate their seeds, and a lack of stream flow could make it challenging for new generations of black cottonwoods to establish themselves. Also, as warm and dry climate trends shift further north with climate change, it is possible that the ranges of Okanagan trees such as western redcedar and paper birch become more restricted the in central and southern Okanagan riparian environments, whereas trees that thrive in warm and dry environments, such as ponderosa pine, increase their ranges. 66 2.7 Conclusion The use of RS applications coupled with RF classification modelling is increasingly used when mapping riparian tree species. The approach presented within this research improves upon common field-based or air-photo interpretation riparian mapping approaches that are time-consuming, expensive, and prone to observer biases, by providing a reproducible methodology that relies primarily on ex-situ data. It also exemplifies the ability for one RF classification model to predict tree species across broad riparian environments at fine spatial scales, and is the first riparian forest tree species classification within the Okanagan basin. Globally, riparian environments face pressures related to land use and climate change impacts, with the Okanagan basin exemplifying these pressures in Canada (Statistics Canada, 2016). These pressures, combined with decades of losses to riparian environments within the Okanagan reinforces the need for repeatable and consistent riparian forest monitoring and mapping. Currently, there is limited information on Okanagan riparian forest composition and the impacts of losses of these riparian environments on humans and wildlife. The approaches developed for predicting and mapping riparian forest composition presented within this research form the foundation for riparian forest monitoring and provides a tool that land managers can use to conserve and manage riparian forest composition within the Okanagan. 67 Chapter 3: Conclusion 3.1 Summary of research Remote sensing (RS) advancements over the past few decades have increased the availability of terrestrial information, supporting development of new methodologies for classifying riparian forests at broader scales and finer resolutions (Wulder et al., 2012). In the past, riparian forest classification projects in BC have been limited by data availability and computational challenges, which have recently been addressed through the availability of open-source remote sensing data, including multispectral (e.g., Landsat and Sentinel-2) and light detection and ranging (LiDAR). Given the availability of LiDAR data, as well as future plans to collect and distribute more open-source LiDAR data, the ability to understand riparian forest structure at broad scales within BC is increasing. This structural information can be used in conjunction with spectral indices to explain riparian forests and improve classifications. Classifying these forests using RS applications supports riparian monitoring efforts, as understanding riparian forest composition can directly relate to the overall health of riparian ecosystems (Okanagan Nation Alliance et al., 2019). Riparian forests are a main focus of riparian monitoring projects as they play a crucial role in maintaining biodiversity (Naiman & Décamps, 1997), regulating water quality (Honey-Rosés et al., 2013), and providing habitat for various plant and animal species (Klemas, 2014; Rae, 2005). Riparian environments are vital ecological corridors, serving as transitional areas between aquatic and terrestrial habitats, and their conservation is imperative to ensure the continuation of future ecosystem health. The goal of this study was to accurately classify riparian tree-height vegetation (â¥ 2 m) using a Random Forest (RF) modeling approach with LiDAR and spectral-derived parameters. To achieve this goal, data collection efforts included not only the acquisition of LiDAR and spectral imagery datasets, but also field-visits to create the training dataset of tree species locations. The RF model was trained using these datasets to 68 classify riparian vegetation into distinct classes within sub-watersheds of the Okanagan basin, using one RF model over all watersheds. Notably, the model's accuracy (96.03%) surpassed that of previous classification studies (68% - 81%; Hayes et al., 2014; Laslier et al., 2019), achieving a high level of precision in delineating riparian tree species composition. Unlike other studies, the combined effects of LiDAR parameters and spectral data allowed the study to effectively capture fine-scale variations in riparian vegetation structure and spectral properties simultaneously, where other studies commonly employ only LiDAR (Laslier et al., 2019) or spectral (Hayes et al., 2014) datasets. The LiDAR-derived covariates were very important in classification, as four of the six most important variables were LiDAR-derived (Figure 2.3). The model and outputs produced in this study can provide valuable insights for land managers and conservation efforts aiming to preserve and manage riparian ecosystems in the region, and could also form the basis of temporal riparian forest composition studies that may become possible with future open-source LiDAR and spectral data collection. The research presented here represents one of the first efforts employing multiple RS data types to enhance a fine-scale cell-based RF classification of riparian vegetation. Previous studies employing RF modeling for riparian vegetation classification are not directly comparable as they often employ binary riparian forest classifications (Stackhouse et al., 2023), tree-segmentation methods rather than a cell-based approach (Michez et al., 2016), or categorize output classes into ecosystem types instead of specific tree species (Johansen et al., 2007; U. Nguyen et al., 2019; Thompson et al., 2016). Assessing riparian vegetation is challenging due to the high costs of data collection, whether in the field or through RS methods. Some studies suggest using UAVs for cost-effective data collection (Michez et al., 2016; U. Nguyen et al., 2019), but access to UAVs can still be expensive. Other studies rely on freely available fine-scale aerial or orbital data but differ in their use of a single RS data source (e.g. LiDAR only; Stackhouse et al., 2023) or country-specific 69 data (e.g. National Agricultural Imagery Program spectral data; Hayes et al., 2014). This study utilized both LiDAR and RGB spectral datasets, capitalizing on the increasing availability of open-source LiDAR data and common collection of fine-scale RGB imagery. Notably, this research incorporates RGB spectral data instead of commonly used spectral datasets with near-infrared (NIR) bands (Hayes et al., 2014; U. Nguyen et al., 2019; Richter et al., 2016). Previous studies incorporating RGB-derived indices were not directly comparable due to additional indices requiring NIR data (Michez et al., 2013) or focusing on vegetation health rather than species classification (Dunford et al., 2009). While the inclusion of near-infrared (NIR) bands within spectral datasets would undoubtedly aid classification efforts, the cost of acquiring fine-scale spectral data that includes NIR bands would be challenging in projects with limited budgets to complete fine-scale riparian forest RF classifications. In the Okanagan region, existing open-source provincial spatial datasets like the vegetation resources inventory (VRI), terrestrial ecosystem mapping (TEM), and sensitive ecosystem inventory (SEI) provide limited information about riparian vegetation, either explaining general ecosystem types (e.g. TEM and SEI) or classifying dominant tree species within large parcels around streams (e.g. VRI). These datasets lack the spatial and temporal accuracy required for fine-scale riparian vegetation mapping and to understand dynamics of riparian ecosystems. Consequently, land managers often use costly and time-consuming field-collection methods or image interpretation to gain better insights into riparian vegetation. Combining LiDAR-derived vegetation structure data with fine-scale RGB reflectance information overcomes many of these challenges allowing riparian forests to be mapped at previously unattainable spatial scales, when open-source LiDAR data is available. Ex-situ RS data also enables the classification of privately owned or inaccessible riparian forests, which is often challenging through traditional field-based methods. Riparian forests also pose classification challenges due to their presence within narrow canyons and 70 diverse multi-story canopies, with many tree species having small crowns, like subalpine fir. The use of finer spatial resolution spectral imagery, such as the 3-metre imagery used here, improves classification accuracy (Hayes et al., 2014), especially for smaller-crowned trees. For black cottonwood (Populus balsamifera ssp. trichocarpa), a key species for determining riparian health (Okanagan Nation Alliance et al., 2019), a cell-based approach was beneficial due to the difficulties of tree crown delineation, particularly with species lacking vertical-structured stems (Laslier et al., 2019). The Okanagan's diverse elevation and ecosystems support a variety of tree species, with lower elevation riparian areas featuring relatively few instances of upland species like ponderosa pine and interior Douglas-fir, while higher elevation riparian forests resemble their neighbouring upland forests much more closely with abundant spruce, subalpine fir, and lodgepole pine. Black cottonwood is a key indicator of riparian environments across elevations, though less common at higher elevations. The output classification effectively predicted tree species across these elevational gradients. Classifying larger crowned species like black cottonwood requires ample training data due to their large canopies. For example, a weeping willow (Salix babylonica) may need multiple cells to describe different parts of the tree (~20 m diameter). This study prioritized collecting sufficient training points to characterize these larger trees effectively. Classes with tree species that commonly occur in mixed stands were more likely to be misclassified, such as interior Douglas-fir and western redcedar within lower elevation riparian environments and spruce and subalpine fir within higher elevation riparian environments. While misclassifications of species classes which co-occur, such as spruce and subalpine fir were found through the confusion matrix (Table 2.4), instances of misclassification between classes such as interior Douglas-fir and western redcedar were not found through the confusion matrix. Instead, investigation of the output classification showed that these two species were likely confused with one another in certain regions; for example, there were 71 instances where western red cedar was predicted on north-facing canyon slopes of the southern Okanagan, such as within the lower-elevation reaches of Vaseux Creek canyon, where abundant interior Douglas-fir would have been expected instead. 3.2 Key Findings This research has demonstrated several key findings for understanding riparian forest composition through random forest (RF) modelling and remote sensing (RS) applications: â¢ The use of RS applications for riparian forest classifications allows for inaccessible riparian environments (e.g. private land) to be evaluated and classified, which might not be possible with field-based approaches. â¢ It is possible to create fine-resolution (3 m) large-scale (85,679 ha) riparian forest classifications using data that are commonly available, including LiDAR and spectral RGB data. â¢ The combination of LiDAR and spectral derived explanatory variables are suitable for riparian forest classification studies, and the inclusion of both vegetation structure and spectral characteristics allows for a high RF overall accuracy (96.03%), average user accuracy (UA; 95.17%), and average producer accuracy (PA; 96.96%). â¢ The inclusion of vegetation structure metrics heavily influenced the high overall accuracy of the RF model, as four of the six most important variables in classification were LiDAR-derived vegetation structure metrics, where there was a total of 23 covariates that informed the model (seven vegetation structure, and 16 spectral indices). â¢ Tree species that commonly co-occur (e.g. western redcedar and interior Douglas-fir, or spruce and subalpine fir) are most likely to be mis-classified throughout the classification process (assessed through the confusion matrix), as well as within the output classification surface (assessed through visual assessment of output surface), 72 as opposed to similar tree species that do not commonly occur together (e.g. ponderosa pine and lodgepole pine). â¢ This research contributes to possible future riparian forest composition classification efforts in the Okanagan basin, providing a valuable framework to improve their understanding of riparian ecosystems and guide conservation efforts. 3.3 Future research Open-source LiDAR data are becoming increasingly available in BC, which provides opportunities for other regions of the province to conduct similar riparian forest classification efforts in more remote areas. The future availability of LiDAR data also presents opportunities for temporal aspects to be considered within riparian forest classifications, allowing for an understanding of the way in which riparian forest composition changes over time replated to disturbance and changing climate. Future efforts with suitable training data could consider topographic factors like elevation, soil texture, and moisture, as these characteristics also influence riparian vegetation patterns. Using topographic metrics from LiDAR data could improve model accuracy and prediction (Stackhouse et al., 2023; Wylie et al., 2019). For example, in this study, some cells expected to be interior Douglas-fir were classified as western redcedar due to close proximity and overlapping canopies of the tree species of those classes. Integrating topographic metrics could help distinguish co-occurring species, but was not possible in this research due to limited tree observations. Spatial RF models assume that training data are a thorough representation of all classes found in the landscape (Foody, 2021), so it is imperative that training datasets be large enough to effectively represent a given landscape. While the results of a spatial RF classification improve with larger training dataset, it is important to consider if the training data class proportions are representative of class proportions on the landscape, as well as if the training data has issues 73 related to spatial autocorrelation (Millard & Richardson, 2015). Here, these potential issues were considered through field-collection methods (e.g. collecting class proportions based on what was found at a particular site), as well as mitigating for spatial autocorrelation through the use of a spatial k-fold cross-validation resampling technique. For fine-scale spatial classification models (e.g. 3 m resolution raster cells), training data accuracy is important to consider. Here, training data were collected via GPS coordinates, but GPS-reported precision of a given coordinate may not always reflect the true location due to multipath errors originating from satellite and receiver sources, propagation delays through the atmosphere, and varying signal reflections from different surfaces, impacting the precision of collected coordinates (Bidikar et al., 2020). For riparian forest tree coordinate collection, multipath issues in particular can cause issues, as coordinates in deep and narrow canyons, or in forests with dense stands may have considerable spatial error (e.g., 3-5 m). Here, this was mitigated by collecting an observations approximate tree-height with a range finder, so that each observation could be cross-referenced with a canopy height model (CHM) generated from the LiDAR data used to generate the explanatory tree-structure surfaces. If an observation was supposed to represent a very tall tree, and the CHM cell at that location reported a much smaller height, than that point was removed from the dataset. LiDARâs ability to explain riparian vegetation structure can be leveraged further through stream shadow modelling of riparian forests to help explain surface water temperature gradients through stream courses (Tompalski et al., 2017). This kind of research would help understand where anadromous fishes may find thermal refuge in streams related to riparian forests in periods of warm weather, which is of concern in the Okanagan region especially (Torgersen et al., 1999). Research into stream shading from riparian vegetation can also give more context to the health of a riparian ecosystem, as increased riparian shading would likely correlate with an abundance of black cottonwood trees. 74 Okanagan riparian forests face growing pressures from climate change and human activity. The region's high population-to-water ratio and projected population growth suggest increasing strain on water resources (Statistics Canada, 2016). Agriculture and residential uses contribute significantly to water withdrawal, exacerbating water availability issues. Riparian species like black cottonwood rely on flooding events for seed germination, which may be hindered by reduced stream flow (Naiman, Décamps, et al., 2005). Climate change could further impact species distribution, potentially restricting ranges for species like western redcedar and paper birch while favouring others like ponderosa pine in warm, dry environments. By providing a baseline of important riparian tree species in the Okanagan, results from this study can be used to manage and monitor riparian ecosystems more effectively in the face of future challenges. 75 Bibliography Akay, A. E., Oǧuz, H., Karas, I. R., & Aruga, K. (2009). Using LiDAR technology in forestry activities. 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Journal of Soil and Water Conservation, 59(1), 19â27. 90 Appendix Appendix A Table A1. 57 covariates, (18 LiDAR metrics and 39 RGB imagery indices) considered for the RF classification model, were derived within raster cells of 3 m spatial resolution. 23 covariates were included within the RF classification model after correlation was assessed (Pearson's correlation coefficient >= 0.7), and are identified in bold within the covariate column. LiDAR return attributes included metres above ground (z), intensity (i), return number (rn), and classification (c), which includes âunclassifiedâ (c = 1), âgroundâ (c = 2), and ânoiseâ (c = 7). RGB imagery bands included red (R), green (G), and blue (B), where each index was calculated over nine rasters each representing a month between March and November, allowing the mean (Mean), the minimum (Min), and the coefficient of variation (CoVar) to be calculated for each index. Source Vegetation Characteristic Covariate Equation(s) & Additional Information Relevant Citations LiDAR Vertical distribution of tree-height vegetation Mean of z1,2 (z_Mean) z_Mean = (mean of z > 2m) / z_Max Assesses the mean z value placement within the vertical distribution of tree-height vegetation Evans et al., 2009; Laslier et al., 2019; Matasci et al., 2018; Qin et al., 2022; Thompson et al., 2016; Shi et al., 2018; Sothe et al., 2019; Stackhouse et al., 2023 25th, 50th ,and 75th percentiles of z1,2 (z_25pct, z_50pct, and z_75pct) z_25pct = (25th percentile of z > 2m) / z_Max z_50pct = (50th percentile of z > 2m) / z_Max z_75pct = (75th percentile of z > 2m) / z_Max Assesses the percentile z value placement within the vertical distribution of the tree-height vegetation. The 50th percentile is also the median value. Evans et al., 2009; Liu et al., 2017; Matasci et al., 2018; Qin et al., 2022; Shen & Cao, 2017; Sothe et al., 2019; Stackhouse et al., 2023; Xu et al., 2020 Skewness of z1,2 (z_Skew) z_Skew = (skewness of z > 2m) / z_Max Assesses the likelihood of many high z values within the vertical distribution of tree-height vegetation. Evans et al., 2009; Matasci et al., 2018 91 Source Vegetation Characteristic Covariate Equation(s) & Additional Information Relevant Citations LiDAR Vertical distribution of tree-height vegetation Kurtosis of z1,2 (z_Kurt) z_Kurt = (kurtosis of z > 2m) / z_Max Assesses the likelihood of many z values occurring at the tails of the distribution of z values within the tree-height vegetation. Evans et al., 2009; Matasci et al., 2018 Vertical complexity of tree-height vegetation Standard Deviation of z1,2 (z_SD) z_SD = (standard deviation of z > 2m) / z_Max Evans et al., 2009; Laslier et al., 2019; Liu et al., 2017; Matasci et al., 2018; Qin et al., 2022; Stackhouse et al., 2023 Vertical heterogeneity of tree-height vegetation Coefficient of Variation of z1,2 (z_CoVar) z_CoVar = (coefficient of variation of z > 2m) / z_Max Liu et al., 2017; Shen et al., 2017; Stackhouse et al., 2023; Thompson et al., 2016; Tompalski et al., 2017; Xu et al., 2020 Vertical density of tree-height vegetation Canopy Density â Above 25%, 50%, and 75% of z_Max1 (z_CDt25, z_CDt50, and z_CDt75) z_CDt25 = (count of z > (0.25 * z_Max)) / (count of all returns) z_CDt50 = (count of z > (0.5 * z_Max)) / (count of all returns) z_CDt75 = (count of z > (0.75 * z_Max)) / (count of all returns) Matasci et al. 2018; Shen et al., 2017; Stackhouse et al., 2023; Xu et al., 2020 Relative shape of tree-height vegetation Canopy Relief Ratio (z_CRR) z_CRR = ((mean of all z) - (min of all z)) / ((max of all z) - (min of all z)) Evans et al., 2009; Qin et al. 2022; Stackhouse et al., 2023 Ground coverage of tree-height vegetation Canopy Cover2 (rn_CC) rn_CC = (count of z > 2 & rn == 1) / (count of rn == 1) Grijseels et al., 2020; Laslier et al., 2019 Liu et al., 2017; Matasci et al. 2018; Shen et al., 2017; Stackhouse et al., 2023; Thompson et al., 2016; Tompalski et al., 2017 92 Source Vegetation Characteristic Covariate Equation(s) & Additional Information Relevant Citations LiDAR Reflectance distribution of tree-height vegetation Mean of i2 (i_Mean) i_Mean = mean of i where z > 2m Assesses the mean i value placement within the distribution of tree-height returns. Alonzo et al., 2014; Evans et al., 2009; Liu et al., 2017; Shi et al., 2018 Skewness of i2 (i_Skew) i_Skew = skewness of i where z > 2m Assesses the asymmetry of values of i within the distribution of tree-height returns. Alonzo et al., 2014; Evans et al., 2009 Kurtosis of i2 (i_Kurt) i_Kurt = kurtosis of i where z > 2m Assesses the presence of outliers or extreme values of i within the distribution of tree-height returns. Evans et al., 2009 Reflectance complexity of tree-height vegetation Standard Deviation of i2 (i_SD) i_SD = standard deviation of i where z > 2m Evans et al., 2009; Liu et al., 2017; Shi et al., 2018 Reflectance heterogeneity of tree-height vegetation Coefficient of Variation of i2 (i_CoVar) i_CoVar = coefficient of variation of i where z > 2m Evans et al., 2009; Liu et al., 2017 RGB Overall measure of reflected light from vegetation Brightness (BRI_Mean, BRI_Min, and BRI_CoVar) BRI_Mean = mean of BRI BRI_Min = min of BRI BRI_CoVar = (SD of BRI) / (mean of bright) where BRI = (R + G + B) / 3 Fraser et al., 2017; Michez et al., 2016; Xu et al., 2020 Senescing leaf detection Red-Blue Ratio Index (RBRI_Mean, RBRI_Min, and RBRI_CoVar) RBRI_Mean = mean of RBRI RBRI_Min = min of RBRI RBRI_CoVar = (SD of RBRI) / (mean of RBRI) where RBRI = R / B Enhances high red reflectance due to autumn senescence Michez et al., 2016; Sanjerehei, 2014; Wolter & Townsend, 2011 93 Source Vegetation Characteristic Covariate Equation(s) & Additional Information Relevant Citations RGB Green leaf detection Green-Red Ratio Index (GRRI_Mean, GRRI_Min, and GRRI_CoVar) GRRI_Mean = mean of GRRI GRRI_Min = min of GRRI GRRI_CoVar = (SD of GRRI) / (mean of GRRI) where GRRI = R / G Assesses the spectral change in foliage color from green to red. Coops et al., 2006; Michez et al., 2016; Sanjerehei, 2014; Shen & Cao, 2017; Tucker, 1979; Yang et al., 2008 Green-Blue Ratio Index (GBRI_Mean, GBRI_Min, and GBRI_CoVar) GBRI_Mean = mean of GBRI GBRI_Min = min of GBRI GBRI_CoVar = (SD of GBRI) / (mean of GBRI) where GBRI = G / B Assesses the balance between green and blue reflectance. Gower & King, 2012; Michez et al., 2016; Sanjerehei, 2014 Normalized Geen Index (NGI_Mean, NGI_Min, and NGI_CoVar) NGI_Mean = mean of NGI NGI_Min = min of NGI NGI_CoVar = (SD of NGI) / (mean of NGI) where NGI = G / (R + G + B) Distinguishes green reflectance from all reflectance, including ability to distinguish yellow and green foliage. Dale et al., 2020; Fraser et al., 2017; Xu et al., 2020 Excess of Green (EXG_Mean, EXG_Min, and EXG_CoVar) EXG_Mean = mean of EXG EXG_Min = min of EXG EXG_CoVar = (SD of EXG) / (mean of EXG) where EXG = (G * 2) - R â B Assesses the amount of green reflectance over the contributions of red and blue. Barbosa et al., 2019; Dale et al., 2020; Gil & Pacheco, 2021; Ziqian Xu et al., 2019; Xue & Su, 2017 94 Source Vegetation Characteristic Covariate Equation(s) & Additional Information Relevant Citations RGB Green Leaf Detection Normalized Green-Red Vegetation Index (NGRVI_Mean, NGRVI_Min, and NGRVI_CoVar) NGRVI_Mean = mean of NGRVI NGRVI_Min = min of NGRVI NGRVI_CoVar = (SD of NGRVI) / (mean of NGRVI) where NGRVI = (G - R) / (G + R) Assesses the relative intensity of green and red reflectance. Barbosa et al., 2019; Bendig et al., 2015; Costa et al., 2020; Dale et al., 2020; Fraser et al., 2017; Gil & Pacheco, 2021; Huang et al., 2017; Michez et al., 2016; Motohka et al., 2010; Sanjerehei, 2014; Shi et al., 2018; Shimada et al., 2012; Tucker, 1979; Xu et al., 2020; Xu et al., 2019; Xue & Su, 2017; Yang et al., 2008 Modified Green-Red Vegetation Index (MGRVI_Mean, MGRVI_Min, and MGRVI_CoVar) MGRVI_Mean = mean of MGRVI MGRVI_Min = min of MGRVI MGRVI_CoVar = (SD of MGRVI) / (mean of MGRVI) where MGRVI = (G2 â R2) / (G2 + R2) A modified way to assess the relative intensity of green and red reflectance. Barbosa et al., 2019; Bendig et al., 2015; Gil & Pacheco, 2021 Normalized Green-Blue Difference Index (NGBDI_Mean, NGBDI_Min, and NGBDI_CoVar) NGBDI_Mean = mean of NGBDI NGBDI_Min = min of NGBDI NGBDI_CoVar = (SD of NGBDI) / (mean of NGBDI) where NGBDI = (G - B) / (G + B) Assesses the relative intensity of green and blue reflectance. Michez et al., 2016; Sanjerehei, 2014; Shimada et al., 2012; Xue & Su, 2017 95 Source Vegetation Characteristic Covariate Equation(s) & Additional Information Relevant Citations RGB Green Leaf Detection Green Leaf Index (GLI_Mean, GLI_Min, and GLI_CoVar) GLI_Mean = mean of GLI GLI_Min = min of GLI GLI_CoVar = (SD of GLI) / (mean of GLI) where GLI = ((G * 2) - R - B) / ((G * 2) + R + B) Assesses the balance of green against red and blue reflectance. Barbosa et al., 2019; Gil & Pacheco, 2021; Xue & Su, 2017 Red-Green-Blue Vegetation Index (RGBVI_Mean, RGBVI_Min, and RGBVI_CoVar) RGBVI_Mean = mean of RGBVI RGBVI_Min = min of RGBVI RGBVI_CoVar = (SD of RGBVI) / (mean of RGBVI) where RGBVI = (G - (B * R)) / ((G2) + (B * R)) Assesses the balance of green against red and blue reflectance. Barbosa et al., 2019; Bendig et al., 2015 Visual Atmospheric Resistance Index (VARI_Mean, VARI_Min, and VARI_CoVar) VARI_Mean = mean of VARI VARI_Min = min of VARI VARI_CoVar = (SD of VARI) / (mean of VARI) where VARI = (G - R) / (G + R - B) Assesses the balance between green and red reflection with blue influence, while minimally sensitive to atmospheric effects. Costa et al., 2020; Dale et al., 2020; Gil & Pacheco, 2021; Gitelson et al., 2002; Sanjerehei, 2014; Xue & Su, 2017 Vegetation health Visual Normalized Difference Vegetation Index (VNDVI_Mean, VNDVI_Min, and VNDVI_CoVar) VNDVI_Mean = mean of VNDVI VNDVI_Min = min of VNDVI VNDVI_CoVar = (SD of VNDVI) / (mean of VNDVI) where VNDVI = (0.5268 * ((R-0.1294) * (G0.3389) * (B-0.3118)) VNDVI is a proxy for NDVI that uses RGB inputs. Costa et al., 2020; Gil & Pacheco, 2021 1 normalized by the max vegetation height (z_max = 95th percentile of all returns). 2 assessed returns where z > 2 m to explain tree-height vegetation returns specifically. 96 Figure A1. Elevation map of the Okanagan Basin displaying larger population centers, lakes, and highways, as well as the different channels of the Okanagan River. 97 Figure A2. Study area watersheds displayed with intersecting BEC zones. Classified cells were cells in which were 100 m from streams (riparian environments), and where the cell contained at least one LiDAR return greater than or equal to 2 m from ground height. the Leon Creek watershed was the only watershed in which riparian environments did not intersect each BEC zone within the watershed: Leon Creekâs ICH BEC zone (A), and Leon Creekâs MS BEC zone (B). 98 Figure A3. Covariate correlation plot displaying the output Pearsonâs correlation coefficients for each pairwise relationship. Coefficients which are bounded by a black border are where the coefficients are greater than or equal to 0.7. Bolded covariate names, as well as shaded cells for that covariate, highlight covariates in which were selected to avoid high correlations between covariates inputted into the random forest (RF) classification model. 99 Figure A4. Imagery that gives context to why the months between March and November were specifically selected for indices to be derived over, based on the snow accumulation seen in the study area within the year of the considered imagery (2019) as well as the knowledge of tree defoliation; snow was very close to being melted in most of the region by March with much of the region regaining snow accumulation until after November. Also, black cottonwood and trembling aspen each drop their leaves in October and November while higher elevation western larch commonly drop their needles in November. 100 Figure A5. Vegetation Resources Inventory (VRI) inventory units symbolized by reference year, which explains when the BC Provincial Governmentâs VRI inventory unitsâ assessed imagery were collected, for the Okanagan basin, BC.
Thesis/Dissertation
eng
Earth and Environmental Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bourbonnais, Mathieu
Classifying riparian forests of the Okanagan Basin, British Columbia, Canada, through random forest modelling with LiDAR and spectral derived parameters
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