Nicholas Charles Coops

Surveys to monitor forest regeneration are vital for ensuring sustainable forest management and informing long-term timber supply but are often challenged by high costs, safety, and availability of qualified field crews. Advances in fine spatial resolution imagery and digital aerial photogrammetry have enabled detailed 3D mapping of individual regenerating trees. Deep learning, utilizing convolutional neural networks like Mask R-CNN, offers high accuracy and automation for deriving regeneration survey data.This dissertation explores, in Alberta, Canada, automating the acquisition and analysis of regeneration survey data using these technologies. To do so, a Mask R-CNN model was trained to accurately delineate 85% of coniferous tree crowns across different growth conditions. I then derived photogrammetric height measurements that were strongly correlated with field measurements (r2 = 0.93, RMSE = 0.34 m). I then assessed the potential of near-infrared versus conventional optical imagery for the species classification of delineated lodgepole pine (Pinus contorta) and white spruce (Picea glauca) crowns, concluding that conventional optical imagery was suitable for this task. Using this optical imagery, I then applied transfer learning to extend the crown delineation model to instead delineate and classify crown species simultaneously, achieving a mAP of 0.72 and class F1 scores of 0.69 for lodgepole pine and 0.78 for white spruce. Additional fine-tuning on new data showed a potential for further improvement.To support long-term planning, I then developed a methodology to integrate these derived attributes with Alberta's existing growth and yield model. Despite some discrepancies between initial conditions estimated by these methods and conventional field-based surveys, the trajectories and end points of the growth and yield predictions of these datasets were comparable. This research demonstrates the feasibility of these methods and provides guidelines and recommendations for operational implementation, contributing towards more efficient and sustainable forest management in Alberta.

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Understanding changes in salmonid populations and their habitat is a critical issue given changing climate, their importance as a keystone species, and their cultural significance. Further, there is an increasing need to provide up to date, accurate, and spatially explicit information to forest managers to make informed decisions within a sustainable forest management context. The increasing availability of Airborne Laser Scanning (ALS) data for forest applications offers an opportunity to utilize these data for assessing the quality and quantity of habitat, which is often costly and difficult to characterize at broad scales. ALS data provides detailed and accurate Digital Elevation Models (DEMs) under forest canopies, which in turn enable the characterization of detailed stream networks, as well as stream and terrain attributes important to salmonids. The primary objective of this thesis is to examine the potential of ALS to characterize stream habitat features important for salmonids. To accomplish this a systematic review examining how remote sensing technologies have been used to characterize stream habitat features was completed. Next, workflows and models were developed to extract stream width and individual morphological features classified as pools, riffles, runs, and cascades using ALS data. Additionally, the ability of ALS to extract instream wood features was assessed, examining which ALS and environment attributes influenced the detection rate. Lastly, a broader perspective was used and a framework was developed to integrate ALS derived indicators of watershed condition and pressure into existing watershed status evaluation procedures. Ultimately, the research presented in this thesis describes a series of value-added approaches to better understand how ALS data can be used to characterize stream and riparian vegetation features important to salmonids in a forested environment.

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Tree improvement programs are critical to establishing high yield seed sources while maintaining genetic diversity and developing sustainable plantation forests. Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is commonly used in improvement programs due to its superior strength and stiffness properties. Trials in British Columbia (BC), Canada aim to increase stem volume without sacrificing wood quality. Progeny test trials are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of trees. To evaluate performance of improved stock, and to determine whether yield gains are being met, realized-gain trials are used. In both trials, accurate and timely collection of phenotypic data are critical for estimating and validating BVs with confidence. Currently, phenotypic variables collected focus on yield attributes; namely diameter at breast height, and height. Selection criteria in BC are evolving; branching traits are recognized as having a strong influence on strength and stiffness of Douglas-fir wood, however, they are rarely measured.High-density Airborne Laser Scanning (ALS), as well as Remotely Piloted Aerial Systems Laser Scanning Systems (RPAS-LS) produce three-dimensional point clouds which can be used to characterize individual tree structure. In this dissertation I utilized metrics derived from ALS and RPAS-LS to assess the performance in realized-gain trials, and predict genetic parameters in progeny trials. Additionally, new methods to estimate branch attributes of individual trees for inclusion as selection criteria in tree improvement programs were developed.This dissertation provides an insight into how ALS can be used to model branch attributes, while the ability to analyse trees by plot, individual tree, and individual branch attributes further allows researchers to maximise the value of ALS data. The findings are encouraging; they indicate that branch level metrics can be included as selection criteria in breeding programs, and that ALS-derived BVs are a suitable proxy for ground-based BVs. Given the cost efficiency of ALS, forest geneticists should explore this technology as tool to increase breeding programs’ overall efficiency. Findings from this research can be integrated into large-scale programs for monitoring trees, and identifying trees that display desirable attributes.

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Canada’s forests cover about 347 million ha, of which 157 million ha are actively managed for timber procurement and 114 million ha are located in northern remote areas and mostly unmanaged. Information about forest resources is needed to inform sustainable forest management practices and monitor the evolution of forested ecosystems, particularly in the context of climate change. In the past decades, airborne acquisitions of light detection and ranging (lidar) data have been increasingly used to map forest structure. However, their acquisitions remain expensive and coverage is often limited to targeted managed forest units. Recent technological advances led to the development of single photon lidar (SPL) systems that enable high-altitude airborne and spaceborne lidar acquisitions with an unprecedented amount of detail and across large geographic extents. Two SPL systems are currently available: the airborne SPL100 instrument, commercially released in 2017, and the spaceborne Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice Cloud and Elevation Satellite-2 (ICESat-2) launched in September 2018. However, the ability of these two systems to accurately characterize forest structure and resources is not well defined. The primary objective of this dissertation is to examine the role that SPL100 and ICESat-2 can play to provide spatially-explicit, timely and accurate estimates of some key information needs in managed and unmanaged boreal forests of Canada. To that end, an enhanced inventory of key forest attributes driven by SPL100 data was implemented across an actively managed boreal forest located in Ontario, Canada. Strengths and limitations of ICESat-2 metrics to accurately characterize canopy height, cover and vertical vegetation structure were also highlighted by comparing them to SPL100 counterparts. Moving on to a broader monitoring perspective, a method to extrapolate ICESat-2 samples of canopy height estimates across over 19 million ha of unmanaged boreal forests was implemented. Time-series of this wall-to-wall product were also produced and used to examine the recovery of vegetation height after disturbance. The research presented in this dissertation helps better understanding the ability of SPL100 and ICESat-2 to characterize vegetation structure in both managed and unmanaged forests in order to inform policy-making and sustainable forest management practices.

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To achieve comprehensive sustainable forest management the entire suite of ecosystem services that forests provide must be considered. Centuries of research have equipped forest managers with numerous tools to assess the value of timber, the main focus of most management plans in many countries, while cultural ecosystem services (CES) are often neglected. This is partly due to the challenges that CES pose given the nature of non-marketable goods. The ubiquitous approach to study CES is through the use of in-situ surveys, which are expensive and time-consuming. The overarching objective of this thesis is to explore new ways to characterize CES and develop methodologies for forest managers to integrate CES into forest management plans. To this end, a novel data source is exploited throughout this dissertation: geotagged social media images, acquired by visitors to British Columbia’s forested areas and provincial parks. These images and their metadata were crowdsourced from Flickr, analyzed with machine learning and deep learning techniques, and combined with various traditional economic methods such as travel cost and benefit transfer analysis. In doing so, it was possible to characterize, map, and estimate the value of the CES provided by forested areas across British Columbia. The number of recreational visits in BC’s forests was estimated to be over 44 million per year, and the most frequently identified activities were hiking (30.2%), skiing (16.1%), and water related activities such as fishing and kayaking (13.1%). Findings suggest that crowdsourced social media images are a useful, versatile, and unique data source. If used appropriately, they have the potential to tackle the mapping and valuing issues that historically limited the inclusion of CES in forest management plans.

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In Canada, prairie-oak ecosystems along the south-east coast of Vancouver Island are both the country’s most biodiverse and threatened. Anthropogenic land use change and fire suppression are altering the traditional composition of plant species and structure in these savannas, as well as reducing their total area. In 2001, Environment Canada published a study highlighting the impact of habitat fragmentation, the role of fire and the associated consequences of its removal, as well as the presence and effects of exotic species as fundamental ecosystem characteristics that are currently threatened, central to the recovery of prairie-oak savannas and require further scientific enquiry. Each of these fundamental characteristics required an enhanced understanding of the spatial distribution of plant species, lifeform and function. Due to the potential for high species richness and small individual plant footprints relevant data must be collected at fine spatial resolutions. Remote sensing, specifically drone-based imaging spectroscopy, presents a viable method for gathering information that can be used in analyses evaluating the aforementioned fundamental ecosystem characteristics. My research demonstrates the complexity facing studies examining prairie-oak savannas using imaging spectroscopy and provides evidence that plant diversity and function, as well as structural composition, can be spatially evaluated. This work also substantiates the capacity for remote sensing to detect a key exotic species, Cytisus scoparius (L.) Link (Scotch broom) and examine the effects of anthropogenic activities on the primary tree species associated with these ecosystems, Quercus garryana Douglas ex. Hook (Garry oak). Combined, this information directly addresses key knowledge gaps and provides land managers with a variety of new methods to monitor prairie-oak savannas.

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Due to climate change and historic land management practices, wildfires are becoming more prevalent, with increases in fire sizes, frequencies, and intensities. As a result, there is an increased need to accurately understand the environmental drivers of these wildfires, as well as their impacts. The primary objective of this dissertation was to meet this need by developing a suite of workflows that examine the utility provided by newly available remote sensing data that provide greater levels of detail than conventional data. This research was divided between pre- and post-fire management stages. The post-fire research questions evaluated the ability of remote sensing data acquired from cost-effective, remotely piloted aerial systems (RPAS) to create detailed pixel- and individual tree-based fire severity maps. The data used to produce these maps were acquired exclusively post-fire and included high resolution orthomosaics and dense digital aerial photogrammetric (DAP) point clouds. The pixel-based fire severity maps were produced at 5- and 1-meter resolutions using a supervised random forest classification approach, whereas the individual tree-approach estimated crown scorch height across segmented trees that were classified as having fire damage. The pre-fire questions were focused on characterizing the amount and arrangement of combustible fuels that drive wildfire behaviour, and did so at the plot- and individual tree-level. The plot-level approach estimated a full suite of canopy and surface fuel metrics that are commonly used in fire behaviour models. Due to the complex nature of these fuels, these estimations were carried out separately using RPAS-acquired light detection and ranging (LiDAR) and DAP point clouds, as well as ground-based mobile laser scanning (MLS) point clouds. The individual tree-based fuel characterizations were carried out by automatically segmenting and evaluating continuous portions of crown fuels from RPAS LiDAR point clouds and comparing against measurements collected from more detailed MLS point clouds. Through the investigation of these research questions, I provide detailed information that describes both the strengths and weaknesses of these data as well as a suite of methodologies that can be incorporated in conventional and next generation forest management frameworks.

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Sustainably managing the world’s forests requires detailed inventories of the resource at varying spatial and temporal scales. The structural and compositional diversity of the boreal mixedwood forest, one of Canada’s largest forest types, provides valuable timber resources and ecological services. However, the extent and complexity of this forest type poses challenges for inventories. The objective of this dissertation was to develop and assess the utility of three-dimensional remote sensing techniques for enhancing forest inventories by characterizing foreststructure in boreal mixedwood forests. These technologies are scalable and adaptable for use in forest inventory as they provide consistent spatial and temporal detail. Digital terrestrial photogrammetry from spherical cameras at known locations was used to model individual tree stems and sample plots. For individual trees, stem diameters at different heights were estimated very accurately (RMSE
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In order to sustainably manage forest resources, a contemporary, dynamic, and consistent description of their state and extent must exist. As well, there is a need for reliable information on the change to the forested land base to support future policy development and to act informatively on new and emerging issues. Experimentation and technological innovation have spurred remote sensing research to better characterize and inventory forests globally. This dissertation examines how, and to what extent, digital aerial photogrammetry (DAP) and associated spatial products are capable of informing forest planning and management. Alongside innovation in DAP, unmanned aerial systems (UAS) are becoming viable management tools for acquiring stereo-imagery. Fast operationalization, cost-effectiveness, and their ability to acquire high spatial and temporal resolution data sets makes UAS a niche operational inventory tool. To assess the capacity of these technologies, forests of differing stages of structural development, including post-harvest regeneration, and mature managed forest landscapes, were examined. DAP data from these sites were analyzed to determine how, and to what extent, standard and novel inventory attributes can be accurately derived under specific image capture and data model circumstances. I advocate and provide evidence throughout this dissertation that DAP is a technology with ample potential for integration into enhanced forest inventory (EFI) frameworks. This work elaborates on where, and under what conditions, DAP data is successful and limited in characterizing forests with the ultimate goal of improving information for operational, tactical, and strategic decision-making.

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Understanding pre-industrial fire patterns, in particular unburned or partially burned vegetation remnants, has become a research and forest management priority in Canada and beyond. To achieve these goals, it is crucial to better understand the variability of spatial fire patterns, as well as the relative importance of the environmental controls at broad scales. Open-source and freely available Landsat data has great potential to capture fire patterns in a repeatable and automated way across large and remote areas. However, critical challenges associated to (1) the reliance on very expensive field plot data for calibration/validation of the mortality maps; and (2) the lack of consistent spatial language and methods to analyze the spatial patterns, hindered the applicability of these methods across large areas and the comparability of the results obtained. The objective of this dissertation is to develop, test and demonstrate the value of a novel framework to help improve our understanding of historical spatial fire patterns across the Canadian boreal forest. The research advances our understanding of the variability and causality of spatial fire patterns across large remote boreal regions addressing both scientific and management communities. Major contributions from this research include:• Re-imagining how to capture and describe spatial fire patterns across large and remote areas of the boreal forest through an innovative and cost-effective framework that combines Landsat satellite data, polygons of mortality from aerial photo-interpretation and a consistent spatial language and metrics to capture key fire characteristics. • A demonstration of this new framework and how it can be extrapolated to other landscapes beyond the original formulation area. In particular, this research produced a fire pattern database comprising 507 new fires and 2.5 Mha – far in excess of any other study to date for the same area.• An examination of how the data generated could be used in combination with new tools and methods to reveal patterns of fire mortality not previously possible including (1) characterization and assessment of differences in fire pattern signatures between pre-defined ecological zonations, and (2) analysis of the interactions between spatial fire patterns and main biotic and abiotic environmental controls.

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Cities strive for economic strength while recognize the necessity of being environmentally sustainable. The balance between economic development and the environment has been challenging particularly for cities in the pan Pacific region, which is seeing some of the most rapid urban growth rates. Remotely sensed satellite images offer much larger and more consistent spatial and temporal coverages than conventional census data therefore are increasingly being utilized for regional and global urban studies. Two key remote sensing datasets, namely urban vegetation cover derived from Landsat time series, and brightness generated from NOAA’s nighttime lights datasets to represent urban development were the focus of this dissertation. I first extracted annual urban vegetation characteristics using spectral indices (e.g. EVI) as well as a spectral mixture analysis from 1984 to 2012. Nighttime lights brightness was used to assess urban expansion and its relationship with census-derived variables. Lastly, I examined the relationships between urban development and the environment using Environment Kuznets Curve (EKC) theory as a lens, addressing how urban vegetation responds to urban nighttime brightness in 25 cities across the pan Pacific region. I identified inter- and intra-city patterns of vegetation and brightness changes that were strongly related to social and economic contexts. Spectral indices demonstrated opposing trends between urban vegetation and built-up area both spatially and temporally. Spectral mixture analysis successfully extracted the urban vegetation fraction at a sub-pixel level, setting a robust base for cross-city comparisons. I found that urban vegetation changed linearly both positively and negatively with urban brightness, particularly in higher income cities in North America. Pixels with statistically strong quadratic relationships between vegetation and brightness were less prevalent but more spatially clustered in comparison to those that expressed a linear relationship. Overall, there are three key contribution of this dissertation. Firstly, the integration of gap-free satellite images and innovative processing techniques unlocked new ways of informing urban environmental and socio-economic dynamics. Secondly, a classic econometric model (i.e. Granger causality test) was used to examine the casual relationship between census and remote sensing nighttime lights data. Lastly, a pixel-based model fitting was use to confirm EKC at a sub-city scale.

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In western Canada, the effects of warming and increasing human activity may alter the structure, composition, and function of forests, producing quantitatively and qualitatively different understory light conditions. While difficult to measure directly, process-based models may facilitate inference of historical forest states. Yet, existing models are limited in the dynamics they represent. A promising new approach in hybrid modeling, first demonstrated here, is the fusion of machine learning and process-based models to simulate pattern-based processes. The objective of this dissertation was to simulate the effects of past-century climate and fire conditions on understory global solar irradiation trajectories across a 25.2 million ha landscape in Alberta, Canada. The LANDIS-II forest landscape model was applied to simulate past-century changes to competition, fire, and regeneration. Simulated tree species and age maps were classified into landcover types. A regression model of canopy light transmission as a function of landcover and site index showed good fit with field observations (R2 = 0.94) and was applied to a classification of LANDIS-II outputs. Canopy light transmission was multiplied by mean annual bare-earth global solar irradiation to produce understory light maps. Empirical and semi- mechanistic fire models were also applied. A variant of stochastic gradient descent was applied for parameter optimization, improving fire model performance (R² = 0.96; ΔR²= +0.14). Simulations showed a mild decline in forested area across the 1923-2012 period, attributable to a velocity of warming three times faster than migration. Migration was primarily controlled by fire and secondarily by regeneration. Simulated understory light levels declined across the period due to reduced mortality rates, preceding a likely long-term increase in light attributable to reduced regeneration rates. The key innovations of this work are as follows: characterization of human-dominated fire regimes in western Alberta (Chapter 4); advancement of the TACA-GEM regeneration model (Chapter 5); development of an algorithm for fire model parameter optimization (Chapter 6); development of new LiDAR models of canopy light transmission (Chapter 7); demonstration of a new hybrid modeling approach to simulating pattern-based processes, applied to understory light (Chapter 8); demonstration of long-term climatic regulation of understory solar irradiation through forest regeneration (Chapter 8).

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The Arctic is currently experiencing some of the most dramatic warming effects globally due to climate change. Barren ground caribou (Rangifer tarandus groenlandicus) herds in Canada’s north are particularly susceptible to climate change as they occupy Arctic and sub-Arctic environments and as grazers respond directly to changing vegetation conditions.Examining the associations between barren ground caribou and their environment across their entire range presents specific and substantial challenges. Large herd ranges make in-situ habitat monitoring studies difficult and expensive. Additionally, the environments barren ground caribou inhabit are extremely remote and not spatially consistent between years. As such, new techniques are required that address the large scale, remote, and temporally variable nature of these animals. Within this PhD Dissertation, I integrate newly developed remotely sensed environmental data sets with multiple caribou data sets to explore how changing environmental conditions are affecting barren-ground caribou movement and habitat use in Canada’s north. Barren ground caribou’s effects on summer range productivity were assessed to explore top down controls on vegetation productivity. Based on my results, I argue that while there is some association between barren ground caribou density and future summer range vegetation productivity, it is unlikely that range degradation is a major cause of herd declines in the herds examined here. Habitat conditions (vegetation productivity, lichen mat condition, and fire disturbance) were documented across herd ranges to assess how barren ground caribou habitat is changing through time. These habitat conditions were then linked to movement metrics derived from barren ground caribou telemetry data to assess how changing habitat conditions are affecting caribou movement patterns. I found widespread, rapid changes in barren ground caribou habitat in line with predicted and documented climate change effects in the Arctic, and I detected significant alterations in movement metrics associated with these changes in habitat.In all cases, remotely sensed environmental indicators were useful for describing aspects of barren ground caribou habitat. I was able to link habitat conditions to barren ground caribou at both the individual and herd levels and described novel linkages between barren ground caribou and their environment.

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Over the past decade, changes in climate have been sufficient to affect both the composition and function of forest ecosystems. The extent that projected climate change will continue to impact tree species vulnerabilities remains unclear and has been mainly assessed based on simple relationships between the distribution of mature trees and climate variables. The objective of this thesis was to assess the effects of regional climate and soil variations on the current and future distribution of 20 major conifer tree species across western North America and determine the impacts of changing environmental variables on tree species vulnerabilities. The spatial variation in properties of soil water availability and soil fertility was combined in the process-based model 3-PG to provide detailed projections of species shifts in response to changes in environmental conditions. The relative importance of limitations imposed on photosynthesis by suboptimal temperatures, frost, solar radiation, soil water and vapor pressure deficits was ranked in a decision tree analysis based on tree species occurrences across the region. The baseline distributions of the tree species were predicted with an average accuracy of 84% (κ = 0.79), based on their recorded presence and absence on 43,404 field survey plots. Inclusion of soil properties was crucial to improving the overall accuracy of the species distribution models and 75% of the species directly responded to changes in the soil water input. At the ecoregion level, this thesis identified the highest vulnerability of the 20 tree species analyzed to occur within the North American Deserts, particularly in the Thompson-Okanagan Plateau of British Columbia (BC). Comparison of areas suitable for tree species range expansion with a large empirical dataset on tree seedling occurrences in BC agreed on average 79%, serving as indicators of early species responses to climate shifts in the province. Outcomes of this thesis demonstrate species-specific responses to current and future climatic variations and can contribute to informing forest management for climate change adaptation.

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Anthropogenic disturbance regimes are anticipated to overwhelm Earth’s ecosystems during the Anthropocene. Boreal forests are particularly at risk of significant transition due to human appropriation of renewable and non-renewable resources. Forestry and energy development in the boreal forest have three primary ecological consequences: suppression of historical disturbance regimes such as fire; emergence of novel ecosystems; and the eradication of ecological memory, which maintains ecological integrity. The objective of this dissertation is to improve our understanding of the pattern characteristics of anthropogenic disturbance regimes in order to mitigate the negative, unintended outcomes of managed boreal forests.Anthropogenic disturbance from forest harvesting and energy development was mapped for industrialized landscapes of Alberta, Canada between 1949 and 2012. A comparative analysis using spatial models of unsuppressed fires sampled across Alberta and Saskatchewan and aerially-interpreted forest inventory data revealed that the anthropogenic disturbance patterns were beyond the historical range-of-variability in terms of disturbed area, largest patch size, and undisturbed forest remnants. When the spatial data were segmented based on a recent period of intensive energy development, it was determined that energy development in Alberta was a major driver of cumulative anthropogenic disturbance patterns. Levels of undisturbed forest remnants within anthropogenic disturbances declined between 18-34% and edge density increased between 15-175% following energy development.Landscape-level patterns of forest cover changes were assessed using a time series of satellite imagery between 1985 and 2010. Forest disturbance was classified as resource extraction or fire in the Foothills of Alberta with 94% overall accuracy. The rate of resource extraction exceeded fire, accounting for 86% of annual forest disturbance, indicating that fire was suppressed in the landscape. A time series pattern analysis approach applied across Canada demonstrated that managed boreal forests were associated with rising edge density, declining core forest cover, and declining largest forest patch size. Boreal forests that had low disturbance rates were characterized by inherent forest cover pattern variation.This dissertation advanced new perspectives on conceptualizing, detecting, and characterizing patterns of anthropogenic disturbance regimes. Future work is identified primarily around the development and interpretation of landscape structure thresholds and transition indicators.

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Forest structure is an important indicator of ecosystem function and carbon storage in above-ground biomass, yet observations of forest structure are scarce across Canada’s unmanaged bo-real. To reduce uncertainties in global carbon budgets, an improved understanding of spatial and temporal variability in forest structure is required across unmanaged boreal forests. The objective of this dissertation is to investigate how fire history and forest productivity together shape the structure of Canada’s boreal forests, and to develop methods to assess these relationships over large forested areas. Transects of airborne light detection and ranging (lidar) data, totaling 25,000 km in length, were collected across northern Canada in 2010, providing a unique opportunity to study spatial varia-bility in forest structure. To elucidate on the relationships between fire, productivity, and struc-ture, lidar measures of forest structure were combined with optical satellite indicators of disturb-ance history and forest productivity. Specifically, a 25-year chronosequence of forest regenera-tion following fire was developed, and the relationship between forest structure and productivity was assessed as a function of time since fire. In addition, the relationship between structure and productivity was assessed in stands with no recorded disturbances. Satellite-derived estimates of forest productivity were an important predictor of early stand de-velopment following fire, as lidar-derived estimates of canopy cover varied strongly along re-gional gradients of productivity after 15 years following fire (r = 0.63 – 0.72, p 50% canopy cover) prior to burning displayed faster growth and recovery compared to patches classified as open forest (20 – 50% canopy cover). Further, this research highlights the importance of monitoring multiple aspects of forest recovery, as lidar-derived estimates of canopy cover and stand height showed contrasting relationships to productivity in recently burned stands (1985 – 2009) as well as in stands with no recent disturbance. The results of this dissertation demonstrate the value of the airborne lidar transects for describing stand-level variability in forest structure over large areas, and demonstrate the need for lidar to validate wall-to-wall indicators of disturbance, productivity, and structure.

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Over the last century Canadian boreal forests have warmed by 2-3° C, causing growing seasons to lengthen and alterations to annual productivity, which result in numerous responses from boreal tree species. Both disturbance and recovery cycles are affected, although change in northern Canadian boreal forests is difficult to detect, since they remain non-inventoried and lack detailed spatially explicit descriptive data. Through the use of Landsat time series, remote sensing offers the ability to map and monitor large forested areas over time to provide valuable information about boreal forests. The overall objective of this dissertation is to assess the capacity of remotely-sensed spectral time series to characterize forest recovery following disturbance in Canadian boreal forests.Major findings produced from the research presented in this dissertation show:• Boreal forest attributes are better estimated with Landsat time series metrics than single date information, and the inclusion of recovery metrics substantially improves accuracy• Choice of spectral index to monitor recovery is important, and the use of multiple spectral indexes can provide better and meaningful insights into forest recovery• The East/West division of the Boreal Shield ecozone is reinforced due differing spectral forest recovery trajectories that are suggestive of distinct recovery processes in each region. • Forest recovery rates are not fixed across the Boreal and Taiga Shield ecozones, with Taiga Shield spectral forest recovery rates showing a consistent positive trend, possibly indicating forests are recently recovering at an accelerated rate. The research presented in this dissertation advances the use of remote sensing to detect post-disturbance recovery in boreal forest ecosystems. Monitoring large forested areas such as the boreal for change is increasingly important as climatic conditions alter, and the spectral time series methods shown herein provide new tools to observe change in boreal forests. Future research directions are identified around first lengthening time series across longer periods of time, then extending these spectral time series approaches across jurisdictional lines in the pan-boreal region, and finally incorporating the data generated from these methods to be incorporated into carbon accounting frameworks.

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Remote sensing is an important complementary data source to enable cost effective monitoring and mapping of biodiversity indicators over large extents in a consistent and repeatable manner. As such, remote sensing is capable of supporting the information needs of modern biodiversity conservation efforts. However, a number of critical challenges and opportunities deserve greater attention. The aim of this research is to advance the use of remote sensing and other geospatial techniques for large-area, multi-jurisdictional conservation of Canada’s boreal forest. Outcomes of this dissertation contributed to progress in each of four research themes: (i) assessing biodiversity across broad areas, (ii) identifying areas of high conservation priority (iii) evaluating the efficacy of current and hypothetical reserve networks, and (iv) incorporating future vegetation variability in conservation planning. The overall research findings indicate the tremendous capacity of the Canadian boreal forest to provide suitable areas for conservation investment and demonstrate the usefulness of these coarse-scale approaches for guiding ongoing research aimed at boreal conservation planning. Key findings included: (a) Reserves that were restricted to only intact forest landscapes were less flexible and efficient (more costly), (b) Reserves using accessibility (distance from road and human settlement) as a cost surrogate were able to satisfy a range of conservation targets and compactness levels while remaining remote from human influence, (c) Reserves (≥1000 km2;
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Globally, buildings are responsible for more than 40% of energy demand and contribute more than 30% of CO₂ emissions. Various strategies and policies have been developed to reduce the negative of effects of energy use in the building sector, specifically targeting energy conservation and energy supply from renewable resources. As a basis for these strategies, decision-makers require estimates of existing energy demand. Traditionally, broad building sector energy estimates are derived using top-down modelling approaches that establish relations between energy use and variables such as income, fuel prices and gross domestic product. In contrast, individual building energy modelling has evolved sophisticated physically based simulations, populated by an abundance of variables related to building construction materials and components. However, for governments and decision-makers tasked with developing local strategies, techniques are needed to provide a detailed itemization of the building and environmental attributes that impact energy demand, as offered in building simulations, while maintaining the scalability to large areas provided in top-down models. Advances to geospatial technologies and datasets offer novel opportunities to satisfy these two conditions. Of particular interest is light detection and ranging (LiDAR), since it provides spatially contiguous measurements of urban form, otherwise unattainable across large areas. This dissertation presents a novel approach that integrates LiDAR data with building energy models to provide detailed and spatially contiguous estimates of energy demand in the residential building sector. LiDAR is used to augment building energy models by relating measured building form to internal energy components including envelope resistivity, fenestration and air leakage, and by assessing building envelope solar gains after accounting for local occlusions. Outcomes demonstrate that a LiDAR-based approach to building energy assessment is able to produce results that closely match those from manually informed building simulation software, thus offering a time and cost effective option for extensive and detailed analysis of energy demand. By presenting methods to decompose building energy demand into the site-specific components that influence energy end-use, this dissertation offers innovative opportunities to analyze and design spatially targeted building energy policies and strategies.

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Advances in mobile computing provide an increasing number of possibilities for public participation in scientific research (PPSR). For example, a growing number of people have access to mobile computing devices, such as smartphones, equipped with sensors including a camera, global positioning system, the ability to record observations, and the ability transfer them over a network for collection and analysis. Literature has shown that PPSR-based approaches can have positive outcomes for volunteers (e.g., opportunities to pursue interests, develop skills, and influence decisions), for resource management (by providing data to inform management strategies), and for science. The objective of this dissertation is to explore how volunteers can use smartphones to collect data to inform forest management in a remote sensing project. The management of wildfires in communities near forested areas was chosen as a case study, and a smartphone application was developed and tested for collecting observations of the amount and arrangement of forest fuels by participants with a range of forestry experience living in fire-affected communities. First, to establish context, other projects using smartphones to collect Earth observation data were reviewed including related terms, concepts, challenges, and opportunities to identify methods of data collection and data processing. Second, questionnaires were given to the volunteers before and after using the application to collect data and were analyzed to understand the social and management considerations including the volunteers’ motivations, attitudes, and behaviours, and the potential of using a PPSR approach for wildfire management. Third, the locations where volunteers submitted data were re-measured and the quality of the data were assessed to provide guidelines for ensuring attribute accuracy and logical consistency. Fourth, the smartphone data was combined with multispectral remote sensing data and topography data to make estimates over broader areas. Finally, a framework was presented to direct future efforts using volunteered remote sensing data. This dissertation demonstrates an approach with potential to apply technology to help inform forest management in communities, with potentially positive outcomes for volunteers, communities, and forest managers.

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The productivity of autotrophic organisms affects all life on Earth; hence, gaining insight in the variability of autotrophic productivity has received significant research interest. At cell to organism level, much knowledge has been gained under controlled conditions through laboratory analysis. At the stand level and beyond, control over the driving variables is limited, and hence experiments have relied on extensive time series, and geospatial analysis to observe changes in productivity across a wide range of environmental conditions. Significant technologies at these scales are eddy covariance that provides point sample estimates of productivity by measuring CO₂ fluxes between land and atmosphere, and remote sensing that provides for extrapolating eddy-covariance measurements across the landscape using canopy-reflectance data. Challenges in fusing eddy covariance with remote sensing relate to the limited capacity of airborne and spaceborne instruments to observe changes in the biophysical state of deep canopy strata; hence, eddy-covariance estimates that capture the productivity of an arbitrarily dense canopy volume are extrapolated based on top-of-canopy reflectance data. Proximal-sensing technology extends the acquisition of reflectance data to arbitrary locations within the canopy; however, these data are affected by the immediate canopy structure surrounding the sensor that introduces a sensor-location bias, and the direct use of these data in stand-level models is therefore challenging. This thesis explores the simulation of photosynthetic down-regulation using geometrically explicit forest models and meteorological records. The geometrically explicit models are constructed by combining laser-scanning data with tree-regeneration models, and are used to simulate a time series of leaf-level incident radiation. The parameters of a leaf-level photosynthesis model are then optimized against eddy-covariance productivity estimates. Finally, the potential of geometrically explicit models for the fusion of remote sensing and proximal sensing data is discussed.

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The lodgepole pine (Pinus contorta) forests of British Columbia have been recently affected by mountain pine beetle (MPB) (Dendroctonus ponderosae), constituting one of the most destructive insect outbreaks in North America. In such a snow-dominated environment, a receding forest cover is associated with increases in snow accumulation during winter, enhancements of snowmelt rates and suppression of spring transpiration. These changes can elevate flooding risk and thus threaten society. However, the unprecedented extent of the disturbance and particular nature of the beetles’ severe but gradual effect on the forests’ integrity have challenged scientists aiming to quantify the real ecological impacts. Even though hydrologic models remain as the only tool currently available to evaluate the effects of MPB on hydrologic dynamics, they are impaired in their present form for relying on coarse and oversimplified characterizations of forest structure. Remote sensing technologies such as Airborne Laser Scanning (ALS), which provides detailed three-dimensional representations of canopy structure, offer a remarkable alternative to fill this knowledge gap. The main objective of this thesis is to determine how hydrologic modeling can be improved by remote sensing through a better characterization of forest structure. Given the variety and complexity of hydrologic models, the same research question is applied independently to the simplest forms of plot-level univariate empirical models and complex physically-based simulators operating at the watershed level. It was found that remotely-sensed forest metrics are better predictors of snow accumulation and ablation at the plot level than traditional ground-based variables, and that the accurate estimation of maximum snow accumulation and snow ablation with ultrasonic range devices significantly increases the quality of simple empirical models. It was also shown that a novel method, which minimizes the geometrical differences between ALS and traditional ground instruments’ data, was fundamental to obtain accurate plot-level estimates of forest structure metrics identified as primary drivers of snow processes. Wall-to-wall watershed-level coverage of hydrologically-relevant forest variables was successfully achieved by integrating ALS and Landsat metrics. The methods developed will result in better inputs for hydrologic models with the potential to improve the quality of snow process and streamflow predictions.

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This thesis investigated observed responses of forest productivity to environmental changeand their predictability using semi-empirical carbon (C) cycle models in temperate-maritimeconifer forests in coastal British Columbia, Canada. Effects of environmental stress andhistorical responses to environmental trends were constrained using observations of grossprimary production (Pg) from eddy-covariance flux towers and stemwood growth (Gsw) andmortality (Msw) from permanent forest inventory plots.Observations suggested a long-term increasing trend in Gsw extending back to the Little IceAge, with decadal fluctuations in association with several 20th century drought episodes.Statistical models driven with climate variability, alone, could not reproduce the observedtrend in Gsw, while climate variability and sensitivity to carbon dioxide (CO₂), combined,expressed a moderately strong capacity to reproduce past trends and variability. Observationsalso indicated substantial wave-like fluctuations in Msw that could not be explained by standdensity-dependent processes, alone, while additional functions of drought sensitivity vialinear-threshold functions of evapotranspiration (ET) and precipitation (P) improved modelpredictions.The capacity to predict tree productivity was explored within a more mechanistic modellingframework, focusing on evaluation of physical principles used to simulate Pg in productionefficiency models (PEMs) and subsequent application within the established forestproductivity model, 3-PG, to simulate Pg, Gsw, and Msw. Comparison with observationshighlighted several deficiencies in the representation of environmental stress in PEMs thatrestrict the capacity to accurately simulate transient responses to environmental change, someof which arise from the model reduction and scaling techniques employed by PEMs, whileothers reflect unsettled physiological understanding. Consistent with regression modelsimulations, absence of CO₂ fertilization in 3-PG led to inability to reproduce observedtrends in Gsw.This research demonstrated that representation of environmental sensitivity in models of Gswand Msw does not lead to appreciable increases in model precision, yet is absolutely necessaryto achieve temporally-unbiased simulations at the regional scale. Findings also demonstratethe critical role of observation networks, including permanent forest inventories and longtermcontinuous meteorological and hydrological measurements as a necessary means ofadvancing and implementing model representation of environmental controls on forestproductivity.

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Rain-on-snow (ROS) is a major contributor to flooding and landslides in many temperate coastal watersheds around the world. Research has shown that forest harvesting can increase melt rates during ROS at both the stand and watershed scale. Because of this, post disturbance hydrological recovery is of interest in watersheds where forest management is prevalent. Recent research that pairs events by frequency rather than chronologically has indicated that forest cover removal can have a significant effect on the magnitude and frequency of extreme events, which is counter to the dominant view in forest hydrology. Hydrological modelling provides a means to apply frequency based analysis in watersheds with short data records, but models must be tested and validated in coastal watersheds before they can be applied extensively. A key challenge to testing models is the inherent difficulty with collecting data in ROS environments. Therefore, the objectives of this research were to design a methodology that recorded previously unobserved processes, use these data to validate model simulations and assess stand level energy flux recovery during ROS. Data were collected at a range of elevations within recently harvested, regenerating and old growth forests. The Cold Regions Hydrological Model generally performed well at capturing the dynamics of snow accumulation and melt, however, snow water equivalent was generally over-predicted. Depths of transient snowpacks were generally under-predicted, however, once a snowpack was established model performance improved. Clear-cut forests had higher mean and greater variability of energy inputs resulting in large events occurring more frequently than in old or second growth forests. Energy flux recovery was evident within the regenerating forests; however, both the rates of recovery and differences among stands depended on the location and the variables compared. When either the mean or standard deviation of energy inputs differed from that of old growth forests, energy flux recovery was reduced as events became larger and less frequent. It is probable that results obtained from this study will translate to stream flow in watersheds with steep slopes, shallow soils and extensive preferential flow networks (i.e. high run-off coefficients), especially when run-off generating areas are synchronized.

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One of the critical issues in the prognoses of future climate change is a comprehensive understanding of the global carbon budget. Progress in C balance studies has been achieved either at stand or at continental scales. However, the coupled terrestrial carbon, nitrogen and hydrological dynamics are yet far from well understood and methods to estimate the land-atmosphere carbon fluxes at the landscape and regional scales are notably lacking. The major findings of this dissertation research are as follows: First, this dissertation improves our understanding of the terrestrial C processes, for example, at Douglas-fir stand in the Pacific Northwest: (i) Although the majority of carbon sequestration occurred during March through June, May through August was responsible for about 80% of the inter-annual variability of net ecosystem productivity (NEP). The major drivers of inter-annual variability of annual carbon fluxes were annual and spring mean temperatures (Ta) and water deficiency during late summer to autumn; (ii) Monthly GPP was linearly correlated with photosynthetic active radiation (Q) (r² = 0.85) and monthly Re was exponentially correlated with Ta (r² = 0.94); (iii) The responses of NEP to changes of Ta and Q were positive during the first and last four months of the year but were negative during the middle four months of the year. (iv) N fertilization increased annual NEP by ~83%, in the first year, resulted from increases in annual GPP by ~8% and from decreases in annual Re by ~5.8%. Secondly, this dissertation develops a pragmatic algorithm with synergy of footprint climatology and geospatial analyses for assessing the spatial representativeness of eddy-covariance flux tower measurements. This algorithm was then applied to the Canadian Carbon Program network. Thirdly, this dissertation develops an innovative up-scaling strategy by integrating ecosystem modeling, footprint climatology modeling, remote sensing, and data-model fusion for the scaling of C fluxes at stand, landscape and regional scales. And fourth, this dissertation develops an analytical scalar concentration footprint model to assess the influences of land surface heterogeneity on tower CO2 concentration measurements.Summarily, this dissertation research provides a sound basis for shaping future climate change adaptation policy related to carbon management

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Information representing the species composition and structural configuration of forested ecosystems is critical for effective, sustainable management. In Canada, the methods employed to map forest species and structure vary, however, they conventionally include photogrammetric techniques. Despite common use, aerial photograph delineation and interpretation is time consuming and laborious, often yielding subjective results which cannot be easily updated, and is thus not well suited for quantitative mapping over extensive areas. In contrast, advanced methods for remotely quantifying forest characteristics show promise for improving conventional approaches. Two data sources of particular interest are hyperspectral and light detection and ranging (LiDAR). Hyperspectral sensors acquire data simultaneously in upwards of hundreds of narrow spectral channels, providing an unprecedented tool for differentiating between vegetation species. LiDAR systems directly measure the vertical distribution of foliage, providing detailed information on height, cover, and structure. This thesis integrated new generation remote sensing technologies with field data to improve forest species and structural information in the British Columbian southern Gulf Islands (SGI). Results indicate the objective was met, providing a state-of-the-art, step-by-step protocol for forest managers and ecologists to undertake detailed and accurate species and structural mapping of protected areas, while decreasing associated labor, time and subjectivity, and increasing repeatability, at a cost comparable, if not less, than conventional aerial photography. The unique outcomes of this thesis include the first spectral library of dominant tree species in Canada’s coastal Pacific Northwest, the first SGI inventory of LiDAR-metrics able to characterize and differentiate forest structure, significantly improved data for rare Garry oak habitat, markedly more detailed and accurate distribution information for 11 dominant tree species derived using an innovative classification approach and newly developed LiDAR metrics, and the first assessment in any environ of hyperspectral metrics for describing and differentiating avifaunal guilds based on diversity. In addition, results provide the first tree species heterogeneity predictions for the SGI, yielded through an object-based classification incorporating airborne hyperspectral data and space-borne multispectral data. The innovative methods described are not limited to the SGI, and can be replicated where targeted species/structural characteristics can be defined and differentiated based on hyperspectral-derived and/or LiDAR-derived metrics.

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Bark beetle infestations in western Canada have caused damage at previously unrecorded levels. Conventional forest health surveys are conducted to collect information on these infestations; however, due to the widespread nature of attack digital remote sensing technologies have the potential to offer new methods to augment forest inventories. This thesis will investigate the utility of remotely sensed data to detect and monitor insect infestations and provide innovative approaches to determine forest health information. In the first section of the thesis the accuracies of conventional forest health surveys were reviewed and assessed in a series of plots at the edge of the infestation. Mitigation levels were shown to be 43%, which was inadequate to stop a doubling expansion rate. A review of the detection rates of digital remote sensing was also conducted and used in a simple expansion model to assess the capacity of digital techniques. In the second part of the thesis a series of innovative methods were applied over a hierarchy of remotely sensed data sets. Attacked trees identified during field surveys were delineated on fine scale imagery with an accuracy of 80.2%. From these delineations, tree [stem diameter (r = 0.71, p
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