Nicholas Charles Coops

Professor

Research Classification

Research Interests

Telemetry (Remote Sensing, Radar)
Space Techniques
Forestry Technology and Equipment
Plants and Forests

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters

 
 

Research Methodology

Image processing
Geographic Information Systems
Spatial statistics
Programming
Biometrics

Recruitment

Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round

Application of remote sensing technologies to forest productivity and conservation issues

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).
I am interested in hiring Co-op students for research placements.

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Postdoctoral Fellows

Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - May 2021)
Using three-dimensional point clouds to improve characterizations of forest structure across spatial and temporal scales in mixedwood forest stands (2020)

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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Assessing historical landscape patterns following fire in the Canadian boreal forest using remote sensing data (2018)

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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The relationships between rapid urban development and vegetation in the pan Pacific region: spatio-temporal quantification using satellite images (2018)

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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A hybrid modeling approach to simulating past-century understory solar irradiation in Alberta, Canada (2017)

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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How are changing environmental conditions affecting barren ground caribou movement and habitat use in Canada's north? (2017)

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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Mapping the distribution of conifer tree species in response to environmental changes across western North America using a physiologically based approach (2017)

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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Characterizations of boreal anthropogenic disturbance regimes from multi-scalar Earth observation (2016)

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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Characterizing the link between fire history, productivity, and forest structure across Canada's northern boreal using multi-source remote sensing (2016)

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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Remotely sensed image time series to inform on forest structure and post-disturbance recovery over Canadian boreal forests (2016)

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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Integration of Remote Sensing and Spatial Conservation Prioritization Approaches for Aiding Large-Area, Multi-Jurisdictional Biodiversity Conservation in Canada's Boreal Forest (2015)

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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Building energy modelling and mapping using airborne LiDAR (2014)

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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Public Participation to Estimate Forest Fuels Loading: The Development and Testing of an Application for Remote Sensing (2014)

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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Using forest structure to model vertical variations of canopy radiation and productivity (2014)

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 use of remote sensing to characterize forest structure and improve the modeling of snow processes in extensively disturbed watersheds (2013)

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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Impacts of environmental change on tree productivity in termperate-maritime forest ecosystems (2012)

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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Snowmelt energy balance recovery during rain on snow in regenerating forests (2012)

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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Towards an understanding of coupled carbon, water and nitrogen dynamics at stand, landscape and regional scales (2012)

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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Employing advanced airborne remotely sensed data to improve terrestrial ecosystem mapping (2011)

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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Integration of Multi-Source, Multi-Scale Remotely Sensed Imagery With Ground survey Information to Provide Forest Health and Inventory Data (2011)

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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Master's Student Supervision (2010 - 2020)
Characterization and quantification of forest secondary structure using airborne LiDAR (2020)

Knowledge of forest structure can be used to guide sustainable forest management decisions. Currently, Airborne Laser Scanning (ALS) has been well established as an effective tool to delineate and characterize canopy structure of forested biomes. However, the use of ALS to characterize forest secondary structure is less well developed. Secondary structure consists of suppressed sub-canopy trees, short-stature vegetation and coarse-woody-debris (CWD). I utilized discrete return ALS to develop methodologies which characterize two secondary structural units, sub-canopy trees and CWD, within natural forest stands in central British Columbia. I first segmented the forest vertically into canopy versus sub-canopy and computed a suite of ALS metrics to develop predictive models of sub-canopy stand attributes. Calibrated against 28 ground plots, models were developed using stepwise regression resulting in the strongest predictors being a combination of height, structure and cover-based metrics. Two sets of models were developed, one with the canopy removed and another with it retained. The sub-canopy set of models resulted in stronger cross-validated R-squared values for volume and basal area and as a result the sub-canopy volume model was used to map sub-canopy volume over the entire study area. The second structural unit, CWD, is a meaningful contributor to forest carbon levels and biodiversity. In this work I detail a novel methodology that isolates CWD returns from large diameter logs (>30cm) using a refined grounding algorithm, a mixture of height and pulse-based filters and linear pattern recognition to transform returns into measurable vectorized shapes. Height values are extracted directly from the point cloud to calculate volume for detected shapes. This approach is then demonstrated by successfully mapping CWD and estimates of volume as well as providing an assessment of individual log and plot-level attributes that influence successful detection. I compared plot volume totals calculated from ALS-derived CWD against field measured CWD and found a strong correlation. Lastly this methodology was applied over a larger region to quantify CWD volume differentials between stands. These methodologies demonstrate the capability to generate a secondary structure inventory that can highlight locations for selective logging, model fire susceptibility and carbon sequestration, and quantify wildlife habitat.

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Detecting the phenology of important vegetative grizzly bear foods using remote sensing and analysing their relationship to grizzly bear habitat selection (2020)

Understanding patterns in vegetation phenology under increasing anthropogenic pressures, and within a changing climate, is essential for determining the availability of key plant-food resources, which drive habitat selection of wildlife. In creating a fine scale phenology product to monitor daily phenology trends, this thesis determines how variations in availability of key plant-food species is affecting daily habitat selection of grizzly bears (Ursus arctos), across years, within the Yellowhead Bear Management Area, Alberta, Canada.Dynamic Time Warping is used to combine Landsat satellite data and Moderate Resolution Image Spectroradiometer (MODIS) imagery, to quantify daily changes in vegetation at a 30 m resolution, from 2000-2018. This approach, entitled DRIVE, was validated against the start and end of season dates (SOS and EOS respectively) derived from time-lapse imagery obtained from ground cameras. Results showed correlations of r = 0.73 at SOS and r = 0.85 at EOS with a mean absolute error of 7.17 and 10.76 days at SOS and EOS respectively. Analysis of the DRIVE product also indicated that SOS is advancing at a maximum rate of 0.78 days per year from 2000-2018.A set of new methods were then developed to create daily vegetative food species availability layers from 2000-2017. Annual species distribution models (SDMs) were created using maximum entropy modelling. SDMs were combined with DRIVE outputs to create daily plant-food availability layers for eight food species. Food availability layers were combined with environmental variables and grizzly bear GPS collar data to create resource selection functions modelling daily and seasonal selection. Results determined that in the dry spring, selection for roots was stronger and occurred earlier than in the average/wet years, in the wet summer the length of selection increased for forbs and in the dry fall, the period of selection for berries was longer than in the wet year.Through this research, I found that variations in phenology driven by climate and anthropogenic processes, has the potential to affect grizzly bear habitat selection into the future. The datasets and approaches developed here will provide resource managers with an important tool for use in grizzly bear habitat management and population recovery.

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Incorporating visual and auditory perception into understanding grizzly bear behavioural responses to roads in Alberta, Canada (2020)

Anthropogenic disturbances, including roads, are known to influence animal habitat selection and mortality. However, little is known about the role of sensory perception in animal responses to disturbance. The goal of this thesis was to investigate the effect of visual and auditory perception around roads on grizzly bears (Ursus arctos) in Alberta, Canada. As an apex predator, the greatest threat to grizzly bear populations in my study area is human-caused mortality near roads, yet grizzly bear behavioural responses to roads are not fully understood. In this thesis, detailed topographic and land cover data from airborne Light Detection and Ranging (lidar) and Landsat imagery were used to estimate visibility and audibility around roads. Using a modified semivariogram approach with data on step lengths from GPS-collared grizzly bears, I found that grizzly bears responded to roads at slightly further distances when roads were perceptible (80 m) than when roads were imperceptible (60 m). I extended the analysis of grizzly bear response by modelling habitat selection as a function of road perception and other environmental variables using integrated step selection analysis. I also assessed mortality risk in visible areas by comparing habitat selection between grizzly bears that died and grizzly bears that survived. Grizzly bears were less likely (p
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Processing and applying multisensor CubeSat data to map forest fire timing and patterns (2020)

Distributed systems of small satellites, called CubeSats, are generating a new type of remote sensing data: multispectral imagery with high spatial resolution and near-daily global coverage. Recent studies have shown that this data is valuable for monitoring land cover change. However, in order to achieve widespread application for this purpose, all satellites in the distributed system, or constellation, must acquire imagery with accurate geolocation and consistent radiometric properties. I developed a preprocessing method to automatically co-register and radiometrically normalize a temporally dense series of images using reference imagery. To demonstrate the effectiveness of this approach, I normalized a time series of CubeSat images at both the pixel and the polygon level. Images from the PlanetScope (PS) constellation were used, focusing on a forested area in British Columbia, Canada that was heavily affected by forest fires in 2017. By examining the normalized difference vegetation index (NDVI) before and after the fires, I found that this method allowed simple identification of burned and unburned areas, which was not readily possible without applying the normalization method. After establishing the validity of the preprocessing method, I developed a multi-temporal change detection method which integrates this technique. This methodological approach is resistant to cross-sensor radiometric inconsistencies. I illustrate the effectiveness of the approach using imagery from the PS constellation and the Harmonized Landsat Sentinel-2 (HLS) virtual constellation. The approach is two-fold; first, a bitemporal method is applied exclusively to PS data, and then a multi-temporal method is applied to radiometrically normalized PS and HLS data. I apply this method to generate a forest fire disturbance map at 3.0 m resolution with a sub-weekly time step. A comparison with a more conventional disturbance map from Landsat difference normalized burn ratio shows improved capture of fine-scale spatial heterogeneity in the burn patterns. This method allows for integrated radiometric normalization with high-resolution change mapping at a sub-weekly time step using CubeSat imagery, suitable for fine-scale land cover analysis. These approaches can help fully exploit remote sensing datasets that have high spatiotemporal resolution but contain radiometric inconsistencies in order to quickly identify land cover changes.

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Assessing terrain modelling and forest inventory capabilities of digital aerial photogrammetry from autonomous aerial systems (2019)

Autonomous aerial systems-based digital aerial photogrammetry (AAS-DAP) is an emerging technology that has the capacity to generate dense three-dimensional (3D) point clouds similar to airborne laser scanning (ALS). Over forested stands, these point-clouds can be used to model forest attributes using an area-based approach however, model accuracy is dependent on digital elevation model quality used to gather vegetation heights above ground. It is known that canopy occlusion contributes to larger gaps in terrain registration from AAS-DAP compared to ALS point clouds. Due to the recent emergence of AAS-DAP as a cost-effective remote sensing platform, few studies have investigated the terrain modelling and forest inventory capacity of AAS-DAP over complex conifer forests. In Chapter 3, through the use of a sensitivity analysis, I established a set of optimal ground points from AAS-DAP by using commercially provided ALS ground points as reference. This optimal set of ground points was then used to test common terrain surface interpolation routines in Chapter 4. Interpolation routines include inverse-distance weighted, natural neighbour, triangulated irregular network, and spline with tension. Using field-measured tree height and stem diameter, allometric relationships were established for dependent variables: mean tree height (Hmean), Lorey’s height (HLorey) and stem volume per hectare (Vstem). Models were then fit among dependent variables and metrics calculated from the vertical distribution of the AAS-DAP point cloud normalized by the different AAS-DAP terrain surfaces in addition to a reference surface generated from commercially provided ALS ground points. A Kruskal-Wallis with Dunn’s posthoc test found no significant difference between predictions derived from different terrain surfaces for all three dependent variables; however, the inverse-distance-weighted method produced a distribution of predictions most similar to those from the ALS-DEM. The best performing forest attributes models for Hmean, HLorey and Vstem yielded mean root-mean-square errors (RMSE) of 1.19 m (7.29%), 0.92 m (5.04%) and 54.55 m³·ha⁻¹ (26.66%) respectively across the four AAS-DAP terrain surfaces generated. Model performance was higher yet comparable when using the ALS-DEM for point cloud height normalization with RMSE of 0.73 m (4.43%), 0.59 m (3.24%) and 37.31 m³·ha⁻¹ (18.24%).

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Integrating spatial and temporal distribution of snow dynamics into mule deer winter range habitat selection (2019)

Many migratory terrestrial mammal species within North America rely on particular habitat characteristics to provide shelter from snow cover in order to assure inter-annual survivorship. Mule deer (Odocoileus hemionus) in particular, are reported to be in decline across South-central British Columbia, likely due to reductions in adequate winter range habitat, limiting the degree of shelter that can be utilized to avoid snow and low temperatures. This thesis sought to evaluate predictions from multiple step selection functions (SSF) by considering both mule deer responses to the timing and distribution of snow cover as well as forest stand attributes including canopy cover and forest edge. In order to generate such SSFs, increasing spatial and temporal information regarding snow timing and distribution across the landscape was required. Previously however, predictions of fine-scale snow dynamics across the landscape suitable for analysis with hourly telemetry data were limited. Therefore, the first component of this thesis was to utilize the strengths of both medium spatial resolution and high temporal resolution satellite imagery and develop a data fusion algorithm to predict snow cover dynamics at a 30m spatial resolution daily, since 2000 using Landsat data with MODIS (Moderate Resolution Imaging Spectroradiometer) snow map data as inputs. The final fused snow map product (MODSAT-NDSI) achieved an overall accuracy of 90% using 33 validation test sites, which included government snow pillow data and an installed camera network. Environmental covariates from MODSAT-NDSI snow maps and 77 deer’s GPS telemetry data in the mule deer SSFs were used to produce predictions of relative probability of use for population-level estimates of habitat selection patterns. The top-ranked SSF models (based on AIC) indicated that mule deer avoided areas with greater, and more persistent, snow cover, and selected areas closer to forest edge. Key thesis outcomes include generated snow cover maps that can be updated and utilized in further studies, a data fusion algorithm that can be replicated for other remote sensing metrics, and habitat selection models that may help to inform future mule deer habitat management.

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Investigating grizzly bear responses to spring snow dynamics through the generation of fine spatial and temporal scale snow cover imagery in Alberta, Canada (2019)

Snow dynamics influence seasonal behaviors of wildlife, such as denning patterns and habitat selection related to the availability of food resources. Under a changing climate, characteristics of the temporal and spatial patterns of snow are predicted to change, and as a result, there is a need to better understand how species interact with snow. Through the generation of fine-scale snow cover data, this thesis examines grizzly bear (Ursus arctos) spring habitat selection and use in the Yellowhead Bear Management Area, Alberta, Canada. First, a new approach was developed to create a daily time-series of 30-m resolution snow cover observations (called SNOWARP). SNOWARP was derived from daily Moderate Resolution Imaging Spectroradiometer (MODIS) data to capture the temporal dynamics of snow cover and Dynamic Time Warping to re-order historical Landsat observations to account for inter-annual variability. The SNOWARP product was produced for 2000-2018 and calibrated against a network of time-lapse cameras and snow pillows. Results indicate the root mean squared error of the fractional product ranges from 31.3% to 68.3%, while F score of the binary product ranges from 87.7% to 98.6%.Second, data from SNOWARP and other environmental variables were combined with GPS collar locations from grizzly bears to test the hypothesis that grizzly bears select for locations with less snow cover and areas where snow melts sooner during spring. Using Integrated Step Selection Analysis, a series of models were built to examine weather snow variables improved models constructed based on previous knowledge of grizzly bear selection during the spring. Comparing four different models fit to 62 individual bear-years, it was found that the inclusion of fractional snow covered area (fSCA) improved model accuracy 60% of the time based on Akaike Information Criterion tallies. Probability of use was then used to evaluate grizzly bear habitat use in response to snow and environmental attributes. The results of this thesis provide one example of the application of newly derived daily 30-m fSCA and indicate grizzly bears select for lower elevation, snow-free locations during spring, which has important implications for management of threatened grizzly bear populations in consideration of changing climatic conditions.

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Measuring height growth using airborne laser scanning and digital aerial photogrammetry in a disturbed Canadian boreal forest (2019)

Enhancing forest inventories using airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) is a spatially extensive means of providing accurate and consistent measures of forest stand structure. While the cost of multi-temporal ALS is still sometimes prohibitive to its integration for growth assessment, DAP point cloud data have been proposed as a cost-effective alternative to those from ALS for inventory re-measurement. As such, the primary objective of this thesis was to examine the capacity of ALS and DAP technologies to assess height growth (HL) in a disturbed boreal forest near Slave Lake, Alberta.First, this thesis determined the variables to be used in modeling height growth, and investigated how the predictive model errors responded to stand condition. To evaluate appropriate variables for predictive modeling, a model using only height metrics (growth_single) was compared with one using height, canopy cover and height variability metrics (growth_multi). The growth_multi model estimated height growth with an RMSE of 1.42 m (%RMSE = 164.18%) and the growth_single model estimated height growth with an RMSE of 1.76 m (%RMSE = 203.03%). To evaluate error response to stand condition, an iterative process was used to measure the accuracy of optimized height models while incrementing the mortality in the dataset. %RMSE increased with increasing plot-level mortality as a parabolic asymptotic curve. When the maximum allowable mortality was approximately 25% the %RMSE was just below 100%.Second, this thesis determined growth patterns near Slave Lake with respect to eight ecological variables, ecosite type and ecosite phase. Analysis of variance (ANOVA) tests were conducted to test the significances of differences between the means of height growth (ΔH). Patterns demonstrated by the ecological variables were most apparent using nutrient regime, moisture regime, species dominance and the soils classification. Growth patterns among ecosites and ecosite phases followed the patterns of the ecological variables that describe them.This research finds that, prior to utilizing multi-temporal remote sensing methods to assess stand-level height growth, forest managers must first understand local forest growth rates and mortality rates to ensure that the growth magnitudes and forest condition permit accurate height growth estimation using predictive models.

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Monitoring fireline construction in near real-time with Sentinel-1 (2019)

Near real-time mapping of anthropogenic linear networks (e.g. roads, seismic lines and fireguards) in forests has a range of applications including monitoring rapid management responses to disturbances such as fire. Synthetic aperture radar imagery is well suited for near real time monitoring because microwaves penetrate clouds and smoke, and satellite images are acquired weekly in many parts of the world, assuring regular coverage.In this study, we created maps of fireguard networks constructed during the 2017 wildfires in Alex Fraser Research Forest in interior British Columbia, Canada based on Sentinel-1 SAR image time series and Sentinel-2 image pairs.I developed two methods to summarize the Sentinel-1 backscatter time series in a single summary raster suitable for human interpretation in Google Earth Engine. The first method is to fit a trend line to the backscatter time series for each pixel, and display the value of this line at the start and end of the observation window in red and green. The second is to fit a single-step function and display the left and right tail values along with the R² value of the fit as red, green and blue values. I assessed the utility of these summary images for manual delineation of fireguard networks by simulating the accuracy and timeliness of fireguard detection based on acquisition in near real-time. For reference, I compared these methods with a straightforward before-after analysis of Sentinel-2 multispectral images and with ground truth maps.From the trend line and step function summary images, interpreters detected 22–41% and 24–55% of fireguard length respectively while delineation from multispectral imagery attained a detection rate of 84–86%. Delineation from Sentinel-2 images was most precise with an average deviation of 5–6 metres from the ground truth, followed by the trend image with 8–15 metres deviation and the step image with 11–16 metres. In the best case, a change was detected based on a step image within 6 days. The developed method can be used to monitor linear feature construction where more accurate methods are unavailable.

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New approaches for understanding urban greenspace using ecosystem services concepts and high spatial resolution mapping (2018)

Urban areas are where most people in the world directly benefit from ecosystem services (ES), yet there is evidence that ES are distributed inequitably with respect to socio-economic status which can lead to environmental injustices. There are a number of barriers to understanding UGS equity, some conceptual and some technical. One barrier is that few studies of environmental justice use similar quantitative methods, nor do they use concordant conceptual frameworks of UGS and ES equity. Another barrier is the fine scale information needed to accurately map greenspace can be difficult and expensive to obtain. In light of these barriers, this thesis seeks to contribute to UGS/ES equity studies in two fundamentally important ways.First, I explore the concepts of equity in ecosystem services as applied to urban settings. I undertake a review of trans-disciplinary literature on urban systems to answer the question “How has environmental justice been considered and incorporated into urban ES research?” I characterize types of urban ES and measure the breadth of justice issues addressed in each article using a new environmental justice index (EJI). I also highlight the methods and results of key quantitative and qualitative papers that can inform future urban ES justice frameworks. Second, I explore how new advances in remote sensing can better characterize UGS distributions via more accurate mapping of heterogeneous urban areas. I combine three-dimensional information from airborne Light Detection and Ranging (LiDAR) data with RapidEye high spatial resolution imagery in a Geographic Object-Based Image Analysis (GEOBIA) approach to classify urban landcover in a large metropolitan region. Though 5m RapidEye pixels were often mixed in urban areas, LiDAR data enabled accurate classification of fine spatial objects such as street trees and single-family dwellings. Ultimately, I propose that mapping ES distributions among urban socio-demographic groups and assessing potential ES tradeoffs is not enough to avoid injustices. Because ES are socio-political constructs, gaining a comprehensive understanding of urban ES injustices is not merely a process of mapping greenspace, but also understanding how the groups in question ascribe value to the ES supply sources around them.

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Assessing the utility of Airborne Laser Scanning for Terrestrial Ecosystem Mapping (2017)

Observing landscape patterns at various temporal and spatial scales is central to mapping ecosystems. Traditionally, ecosystem mapping uses a combination of fieldwork and aerial photography interpretation. These methods, however, are time-consuming, prone to subjectivity, and difficult to update. Airborne Laser Scanning (ALS) is an advanced remote sensing technology that has increased in application in the past decade and has the potential to significantly increase and refine information content of ecosystem mapping, especially in the vertical dimension. ALS technology provides detailed information on topography and vegetation structure and has considerable potential to be used for terrestrial ecosystem classification and mapping. In this thesis, the potential to use ALS data to advance ecosystem mapping is examined. The current state of the science for using ALS data to classify and map key ecosystem attributes within an existing ecosystem mapping scheme is discussed by focusing on British Columbia’s Terrestrial Ecosystem Mapping (TEM) and its associated Predictive Ecosystem Mapping (PEM). Based on a detailed literature review, a site-specific case study was also developed with the goal of mapping TEM polygons for a forested landscape on Vancouver Island, British Columbia. To do so an object-based image analysis approach was used. The analysis examined which were the best suite of ALS-based terrain and vegetation metrics to define and distinguish individual site series. It established a workflow for the classification of site series within the study site and examined the capacity to map site series based on ALS derived values. Best segmentation parameters were first established and then the study area was classified for slope position-wetness and finally into the specific site series. In the classification of site series two approaches were used. One approach used only terrain metrics and the other incorporated vegetation metrics. Overall accuracies were 59% and 56% respectively. While this workflow requires refinement, it shows potential for improved accuracies by applying suggestions discussed.The thesis concludes with a discussion summarizing the findings of this research and highlighting future refinement to the methods applied in the case study, while also providing recommendations for the current application of ALS technology to TEM.

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Using airborne lidar to map habitat structure and connectivity across Alberta's managed forest for biodiversity conservation (2017)

Vegetation structure is an important biodiversity indicator providing biological and physical environment that supports and maintains forest biodiversity. The airborne lidar (Light Detection and Ranging) systems have the advantage of directly measuring three-dimensional vegetation structure, and have been widely used in wildlife habitat mapping and species distribution modeling at the local scales. As lidar data are increasingly compiled into broad spatial coverage, the development of structural inventory and indicators to categorize habitat types and identify important patches would be beneficial to regional-level conservation planning and biodiversity monitoring. However, this area of research has not been adequately explored. Large-area mapping of critical habitat patches is also a fundamental step towards modeling habitat connectivity. Quantification and dynamic modeling of habitat connectivity under long-term influence of land cover change events provide insights into forest management and conservation planning, and including climate change constraints into the modeling framework also helps maintain ecosystem integrity and improve conservation effectiveness.Therefore, the objectives of this thesis are to 1) characterize vegetation structure and identify important habitat patches with critical structural traits using regional lidar dataset, and 2) build habitat networks to model connectivity dynamics under land cover change events. To do this, first, a novel approach using cluster analysis to process large-area lidar data into categorical classes representing natural groupings of habitat structure was applied to derive eight unique structure classes in the managed forested area in Alberta, Canada. Second, the structure classes indicating high levels of structure complexity combined with Landsat-derived forest cover types were used to identify important habitat patches to develop habitat networks. Lastly, spatial prioritization schemes based on different aspects of connectivity and climate constraints were generated and implemented through scenario-based simulations of land cover change events. Connectivity dynamics through the simulations were assessed and compared between scenarios. The result showed that the conservation strategies considering both habitat area and habitat spatial configuration were best at maintaining habitat connectivity, and taking climate constraints into consideration didn’t affect overall connectivity. Overall, this research provides an integrated approach to characterize habitat structure using large-area lidar data for dynamic connectivity modeling following land cover change simulations.

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Mapping the distributions of two invasive plant species in urban areas with advanced remote sensing data (2016)

Invasive plants are increasingly present in ecosystems, producing both positive and negative effects. Proactive management of plant invasions is critical to curbing their spreads, especially in urban areas which often act as centres of invasions. Therefore, municipalities require new tools to map invasions for both management and information. Remote sensing technologies provide opportunities to detect plant invasions over large areas at fine spatial resolutions. In Surrey, British Columbia, Canada, Himalayan blackberry (HB; Rubus armeniacus) and English ivy (EI; Hedera helix) are two understory invasive plants that can negatively influence native ecosystems and harm users of urban natural areas. Two remote sensing technologies, hyperspectral imagery and light detection and ranging (LiDAR) data, were utilized to map these two species across the entire area of Surrey. Analysis of spectral characteristics of HB and EI were used with hyperspectral imagery to examine the feasibility of spectrally detecting these species. Spectra were obtained from a ground-based handheld spectrometer from the two species and other common species in Surrey and processed through a spectral channel selection algorithm to identify key wavelengths for distinguishing these species. Once identified, a spectral classification routine used these wavelengths and training plots to detect HB and EI across open areas in Surrey. Results showed accuracies of 76.4% for HB and 80.0% for EI. Mapping HB and EI across all land covers of Surrey required detecting the two species in forested areas. Field plots, LiDAR-derived topographic and forest structure variables, hyperspectral data, a land cover classification, and a LiDAR-derived irradiance model were all used as inputs into random forest models to detect the species across the entire land base. Model accuracies ranged from 77.8% to 87.8%. Open areas were classified better than forested areas. EI was found more across the city than HB. The research in the thesis has advanced detection of invasive plants by demonstrating the feasibility of mapping understory invasions of EI and HB in urban areas at fine spatial resolutions and can form the basis for a future monitoring system using data acquired at regular intervals. Future work is recommended to enhance data collection and increase map specificity.

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Using airborne laser scanning to assist in substantial forest management decisions for Sechelt's community forest on British Columbia's Sunshine Coast (2016)

As of 2016, there were 57 community forestry organizations in British Columbia apart of various community forest agreements (CFA). Community forests allow for the development of multi-use management plans to reflect a diverse set of values. The availability of detailed information of the forested area is vital to maximizing a community’s benefits and profits. Airborne laser scanning (ALS) can provide estimates of conventional forest attributes, advance inventory attributes along with spatially describing ecosystem services (ES). This thesis combines ALS data, ground sampling data and vegetation resource inventory (VRI) data for the Sunshine Coast Community Forest (SCCF) located near Sechelt, British Columbia in a case study of the application of ALS data to benefit a community forest. Primary attributes (height, diameter at breast height, stem number, quadratic mean diameter, Lorey’s height, volume and biomass) were calculated using an area-based-approach. A secondary attribute (stem size distribution) was calculated using a two-parameter Weibull probability density function. Finally, a tertiary attribute - site indices - was calculated using maximum height from ALS. The reliability of primary attributes predictions varied, with stem number being the poorest (R²=0.51, p-value
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Assessing forest disturbances for carbon modeling: building the bridge between activity data and carbon budget modeling (2015)

Detailed observations of natural and anthropogenic disturbances that alter the forest structure and the distribution of carbon are essential to estimate changes in forest carbon sinks and sources. Remote sensing is one of the primary sources to provide observations of land cover and land-cover change for carbon studies and other ecological applications due to its ability to monitor the Earth’s surface on a regular and continuous basis. However, observations of change are often not attributed directly to an underlying disturbance type and are not well validated, especially in tropical areas.The overall objectives of this thesis are to 1) assess forest disturbances (natural and anthropogenic) and derive activity data for carbon budget modeling, and 2) estimate the impact of different activity data on the terrestrial carbon balance for REDD+ in Mexican tropical forests. To do so, a novel Multi-Source, Multi-Scale Disturbance (MS-D) assessment method was developed to: 1) characterize natural and anthropogenic forest disturbances; 2) obtain land-cover change observations; and 3) attribute land-cover changes to their most likely disturbance driver. Spatially-explicit layers of major disturbance types were generated in annual time steps for carbon modeling across the Yucatan Peninsula from 2005 to 2010. Using geospatial techniques and regression-tree analysis the MS-D approach successfully attributed 86% of land-cover changes derived from the MODIS satellite imagery to their underlying disturbance cause, creating synergies between remote-sensing products, forest inventory and ancillary datasets. Four remote-sensing products derived from Landsat and MODIS satellites were then compiled, providing inputs of activity data for carbon modeling with the CBM-CFS3. Two map sequences were generated for each product, with and without attributing land-cover changes to disturbance type with the MS-D approach. Annual carbon fluxes were simulated to compare the impact of: 1) spatial resolution, 2) temporal resolution, and 3) attribution/non-attribution of land-cover changes by disturbance type on carbon flux estimates. The results clearly demonstrated that different choices of satellite imagery and attribution of changes to disturbance types change the estimated carbon balance. This study provides an integral cost-effective approach to derive activity data for carbon modeling, and support policy and decision-making for forest monitoring and REDD+.

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Using RapidEye Satellite Imagery to Detect Forest Disturbances in British Columbia (2014)

Improving our ability to track and monitor changes on Earth’s surface will inevitably enhance our ability to manage and monitor the biosphere. Remote Sensing technologies developed to monitor the Earth’s surface have already improved our understating of dynamic land cover change at a variety of scales. Fundamental to the identification of land cover change is the detection of abrupt disturbance events. These events constitute direct changes to the composition and structure of ecological systems and may have long lasting effects. In a forestry context it is important to identify disturbances in a timely manner in order to inform management decisions. The RapidEye constellation is a series of five identical Earth orbiting optical sensors capable of achieving five meter spatial resolution imagery with a daily return time. In this thesis we present two studies which assess the capacity of RapidEye to detect (1) stand replacing disturbances and (2) non-stand replacing disturbances in British Columbia. In the first study we develop a robust method to identify stand-replacing disturbances across seven regions in British Columbia. Overall accuracy for the classification of forest disturbance ranged from 83.65 ± 0.77% to 97.65 ± 0.25% for individual 25 X 25 km test locations.In the second study the utility of the RapidEye constellation to detect and characterize a low severity fire in a dry Western Canadian Forest was examined. Estimates of burn severity from field data were correlated with a selected suite of common spectral vegetation indices. All correlations between the ground estimates and vegetation indices produced significant results (p
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Employing citizen-science avian data and environmental data for improved services distribution estimates and avian conservation in British Columbia (2013)

Previous work has shown that many populations of birds residing in British Columbia are declining in number. However, given the size and remoteness of many parts of the province, direct sampling of BC’s bird communities throughout the province is unlikely. Remote sensing has been shown to be an attractive option in these types of situations, providing environmental data in remote areas at spatial scales appropriate for a provincial level of analysis. Additionally, the spatial coverage of remotely sensed data allows for species occurrences to be estimated in un-sampled locations using models derived from areas where sampling has occurred.The overall objectives of this thesis are twofold, and were tested in two separate studies. First, the ability of remotely sensed environmental variables to predict the distribution of coastal bird species for the entire BC coast was investigated. Second, multiple environmental regionalization schemes were evaluated with regard to their ability to delineate avian Beta diversity across the province, and were compared to a regionalization built using species data directly.In Chapter 3, the distributions of 60 species of birds were estimated along the BC coast. Distribution models were built using species occupancy data linked to oceanic, terrestrial, and anthropogenic remotely sensed variables, as well as interpolated climate indices and spatial variables, in both single and ensemble models. The use of these four different types of environmental variables improved the distribution models’ ability to estimate species occurrence, as did the use of ensemble modeling and the inclusion of spatial variables. Significant changes in the amount of occupied habitat by year were detected in 16 species for the eight year study period. In Chapter 4, four environmental conservation regionalization schemes were compared using analysis of similarity (ANOSIM) tests to assess their ability to delineate Beta diversity. A new, species-based regionalization was then created to act as an ideal scenario, and was subsequently tested against each environmental regionalization scheme. These analyses demonstrated that all environmental regionalization schemes delineated significant patterns in Beta diversity, with the Bird Conservation Regions scoring highest in ANOSIM testing overall and being the most similar to the species-based regionalization.

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Integrating discrete-return scanning LIDAR and spaceborne radar to support aboveground biomass assessments (2013)

Forests are considered important reservoirs of organic carbon and have been identified as essential in moderating climate change. Measuring the amount of carbon stored in forests helps improve our understanding of the carbon budget and help with climate change adaptation strategies. Therefore, effective and accurate methods in characterizing changing forest cover and biomass densities are needed.Both LiDAR (light detection and ranging) and radar (radio detection and ranging) technologies can contribute towards the study of forest biomass but one sensor alone cannot provide all the information necessary to monitor forests. Understanding and investigating synergies between different remotely sensed data sets provides new and innovative opportunities to monitor forests.The overall objective reported in this thesis is to demonstrate novel methods to integrate two remotely sensed data sets (i.e., radar and LiDAR) for the application of biomass estimation. This research was divided into two main questions: (1) can shorter wavelength radar variables provide improved biomass estimates when combined with LiDAR data; and (2) can the use of space-borne radar extend aboveground biomass estimates over a larger area using spatial modeling methods.In the first study, relationships between biomass and biomass components with LiDAR and radar data were examined through regression analyses to determine the best combined parameters to estimate biomass. Results indicated that integrating radar variables to a LiDAR-derived model of aboveground biomass helped explain an additional 17.9% of the variability in crown biomass. This corresponded in an improvement in crown biomass estimates of 10% RMSE. Furthermore, InSAR coherence magnitudes from C-band and L-band radars provided the best estimate of aboveground biomass using radar alone.In the second study, aboveground biomass transects derived from plot-based field data and LiDAR, and wall-to-wall radar were spatially integrated using three kriging techniques. The results indicated the importance of correlation between primary and secondary variables when using these kriging approaches. Also a 1000 m distance between biomass transects, was found to provide reasonable compromise between ease of use, accuracy, and cost of obtaining LiDAR data for the study area. Insights into other opportunities for further development in spatial modeling techniques are discussed.

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Characterising moose habitat, abundance and ecosystem variability using satellite-derived indicators (2012)

Natural variability and disturbance events drive spatial and temporal variation in ecosystem processes and play key roles in ecosystem variety and the maintenance of species diversity. As a result, an improved understanding of the links between natural environmental variability and species diversity is needed to guide prioritisation of conservation and management actions. Ontario, the second largest province in Canada, covering approximately 1 million km², is environmentally diverse and is subject to a large amount of natural and anthropogenic disturbances. Remote sensing is uniquely capable of monitoring dynamic ecosystems over large areas in a repeatable and cost effective manner and has been shown to provide considerable benefit to assess species distribution and biodiversity.This thesis (1) examines an approach for detecting natural variability and disturbances of vegetation productivity from a remote sensing time-series and (2) demonstrates the use of satellite-derived indicators for the characterisation of moose habitat across Ontario. First, an approach was developed to assess temporal trends in vegetation productivity which utilised a Theil-Sen’s non-parametric statistical trend test over a 6-year period (2003-2008) of ten-day composites of Medium Resolution Imaging Spectroradiometer (MERIS) fraction of Photosynthetically Active Radiation (fPAR). Results indicated that this novel remote sensing approach can be used to characterise trends in landscape productivity patterns over large areas and can aid in provincial and national monitoring activities. Second, the research investigated the application of remotely sensed indicators such as vegetation productivity, land cover, topography, snow cover and natural and anthropogenic disturbances to predict moose occurrence and abundance. Results indicated that remotely sensed indicators were significantly correlated to moose habitat suitability with moose distribution being more accurately estimated than moose abundance. In addition to providing insights into the relative importance of the predictor covariates for moose occurrence and abundance, this study creates opportunities for further development of spatial models that closely examine the occurrence/abundance-habitat relationships which are highly valuable for habitat management decisions.

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Validating burn severity classifications using Landsat imagery across western Canadian National Parks (2010)

National parks in western Canada experience wildland fire events at differing frequencies, intensities, and burn severities. These episodic disturbances have varying implications for various biotic and abiotic processes and patterns. To predict burn severity, the differenced Normalized Burn Ratio (dNBR) algorithm, derived from Landsat imagery, has been used extensively throughout the wildland fire community. Researchers have often employed this approach to study the effects of fire across multiple contrasting landscapes. Many remote sensing scientists have concluded that incorporating pre-fire information into the current remote sensing dNBR methodology may make such models more transferable. In the first study the main purpose was to investigate the accuracies of the absolute dNBR versus its relative form (RdNBR) to estimate burn severity, in which was hypothesized that RdNBR would outperform dNBR based on former research by Miller and Thode (2007). The secondary purpose was to examine and compare the accuracies of RdNBR and dNBR algorithms in pre-fire landscapes with low canopy closure and high heterogeneity. Results indicate that the RdNBR-derived model did not estimate burn severity more accurately than dNBR (65.2% versus 70.2% classification accuracy, respectively) nor indicate improved estimates in the more heterogeneous and low canopy cover landscapes. In addition, we concluded that RdNBR is no more effective than dNBR at the regional, individual, and fine-scale vegetation levels. The results herein support the continued use of both the dNBR and RdNBR methods and the pursuit of developing regional models.In the second study, we compare the transferability of an overall model and those stratified by land cover and ecozone. Our second objective was to test the statistical benefit of incorporating pre- and post-fire information into standard dNBR approaches. We determined that an overall dNBR derived model successfully estimated burn severity for the majority of our study fires, which supports its transferability across multiple western Canadian landscapes. Results indicate that both pre- and post-fire remote sensing information provides a means of further understanding the different post-fire responses as well as showing minimal statistical burn severity estimates across the majority of fires, however, significant improvement was evident for three of the ten study fires.

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