The BioSat indices designed for biodiversity science and conservation - Volker C. Radeloff; Eduarda M.O. Silveira; Akash Anand; Duanyang Liu; Kathleen A. Carroll; Nicholas C. Coops; Gabriel C. Costa; Maxim Dubinin; Laura S. Farwell; Paul R. Elsen; Martina L. Hobi; Elena Razenkova; et al. (Remote Sensing of Environment, 2026)

Framework for commercial thinning assessment with multitemporal lidar and spatially explicit growth simulation - Liam A.K. Irwin; Nicholas C. Coops; Kirk M. Johnson; Alexis Achim; Ignacio Barbeito; Dominik Roeser; Catherine Bealle Statland (Forest Ecology and Management, 2026)

Composite2Change (C2C) on Google Earth Engine: Time-series change detection and metrics characterizing disturbance and recovery - Txomin Hermosilla; Michael A. Wulder; Joanne C. White; Nicholas C. Coops; Daniel Coelho; Giovanni Ciatto; Noel Gorelick; Emma Bambagioni; Saverio Francini (Environmental Modelling & Software, 2026)

Climate-driven changes in tree species dominance across Canada - Christopher Mulverhill; Nicholas C. Coops; Txomin Hermosilla; Michael A. Wulder; Joanne C. White; Elizabeth M. Campbell; Amy R. Wotherspoon (Regional Environmental Change, 2026)
Differential tree species response to a changing climate could serve to drive changes in tree species dominance that alter forest ecosystem dynamics and have cascading socio-ecological impacts. This study projected climate change–driven shifts in tree species dominance across Canada’s forests by the end of the twenty-first century. To do this, we mapped predictions of dominant tree species under possible future climate conditions using remotely sensed data products and climate projections. We applied species distribution models incorporating spectral, structural, topographic, geographic, and climatic predictors to estimate dominant species and probability of occurrence across Canada’s forested ecozones in both 2020 and 2100. While holding other predictors constant, we altered climatic model inputs (i.e., precipitation and temperature) to relate possible future conditions under a moderate climate change scenario (SSP2-4.5). Comparisons of the model outputs for 2020 and 2100 were analyzed to assess changes in species dominance across environmental gradients and ecozones. Results indicate that approximately 18.7% (68.2 Mha) of Canada’s 367 Mha of treed area is projected to experience changes in dominant tree species by 2100. Of pixels projected to change, approximately 10.9% represented a transition from coniferous to broadleaf species, while approximately 7.8% reflect a shift from broadleaf to coniferous species. The greatest changes in species dominance were projected in areas of lower topographic wetness, high-elevation regions, and in ecozones near the southwestern and southeastern coasts, as these are expected to have relatively large climatic changes. By quantifying potential shifts in dominant species across ecological gradients, this study contributes to the broader understanding of climate-induced changes in forested ecosystems, enabling effective long-term silvicultural and conservation strategies.

Modifying Stand-level Growth under a Changing Climate using a Model Fusion Approach - Kirk M. Johnson; Nicholas C. Coops; Alexis Achim; Verena C. Griess; Ignacio Barbeito (Forest Science, 2026)

Synthesizing Spectral and Field Observations of Post-fire Conifer Recovery in Dry Conifer Forests - Sarah M. Smith-Tripp; Nicholas C. Coops; Joe Meyer; Joanne C. White; Sarah Gergel; Jodi Axelson; Dominik Roeser (Ecosystems, 2026)

Drone multispectral imaging reveals seasonal microphytobenthos drivers at shorebird foraging sites - Tristan J. Douglas; Nicholas C. Coops; Mark C. Drever; Brian P.V. Hunt; Tara G. Martin; Robert W. Elner (Remote Sensing Applications: Society and Environment, 2026)

M3FNet: Multi-modal multi-temporal multi-scale data fusion network for tree species composition mapping - Yuwei Cao; Nicholas C. Coops; Brent A. Murray; Ian Sinclair; Robere-McGugan Geordie (ISPRS Journal of Photogrammetry and Remote Sensing, 2026)

Enhancing tree species composition mapping using Sentinel-2 and multi-seasonal deep learning fusion - Yuwei Cao; Nicholas C. Coops; Brent A. Murray; Ian Sinclair (International Journal of Remote Sensing, 2025)

A Review of Dendrochronology and Remote Sensing Integration for Forest Growth and Disturbance Monitoring - Florence Leduc; Nicholas C. Coops; Alexandre Morin-Bernard; Guillaume Moreau; Alexis Achim (Current Forestry Reports, 2025)

Genealogical Analysis of Replicate Flower Colour Hybrid Zones in Antirrhinum - Arka Pal; Daria Shipilina; Alan Le Moan; Adrian J. McNairn; Jennifer K. Grenier; Marek Kucka; Graham Coop; Yingguang Frank Chan; Nicholas H. Barton; David L. Field; Sean Stankowski (Molecular Ecology, 2025)
A major goal of speciation research is identifying loci that underpin barriers to gene flow. Population genomics takes a ‘bottom‐up’ approach, scanning the genome for molecular signatures of processes that drive or maintain divergence. However, interpreting the ‘genomic landscape’ of speciation is complicated, because genome scans conflate multiple processes, most of which are not informative about gene flow. However, studying replicated population contrasts, including multiple incidences of secondary contact, can strengthen inferences. In this paper, we use linked‐read sequencing (haplotagging), F ST scans and genealogical methods to characterise the genomic landscape associated with replicate hybrid zone formation. We studied two flower colour varieties of the common snapdragon, Antirrhinum majus subspecies majus , that form secondary hybrid zones in multiple independent valleys in the Pyrenees. Consistent with past work, we found very low differentiation at one well‐studied zone (Planoles). However, at a second zone (Avellanet), we found stronger differentiation and greater heterogeneity, which we argue is due to differences in the amount of introgression following secondary contact. Topology weighting of genealogical trees identified loci where haplotype diversity was associated with the two snapdragon varieties. Two of the strongest associations were at previously identified flower colour loci: Flavia , that affects yellow pigmentation, and Rosea/Eluta , two linked loci that affect magenta pigmentation. Preliminary analysis of coalescence times provides additional evidence for selective sweeps at these loci and barriers to gene flow. Our study highlights the impact of demographic history on the differentiation landscape, emphasising the need to distinguish between historical divergence and recent introgression.

Methodological considerations for studying spectral-plant diversity relationships - Christine I.B. Wallis; Anna L. Crofts; Robert Jackisch; Shan Kothari; Guillaume Tougas; J. Pablo Arroyo-Mora; Paul Hacker; Nicholas Coops; Margaret Kalacska; Etienne Laliberté; Mark Vellend (Remote Sensing of Environment, 2025)

Tree species proportion prediction using airborne laser scanning and Sentinel-2 data within a deep learning based dual-stream data fusion approach - Brent A. Murray; Nicholas C. Coops; Joanne C. White; Adam Dick; Ahmed Ragab (International Journal of Remote Sensing, 2025)

The assessment of individual tree canopies using drone-based intra-canopy photogrammetry - Lukas G. Olson; Nicholas C. Coops; Guillaume Moreau; Richard C. Hamelin; Alexis Achim (Computers and Electronics in Agriculture, 2025)

Forest aboveground biomass estimation using deep learning data fusion of ALS, multispectral, and topographic data - Harry Seely; Nicholas C. Coops; Joanne C. White; David Montwé; Ahmed Ragab (International Journal of Remote Sensing, 2025)

Seasonal vegetation dynamics for phenotyping using multispectral drone imagery: Genetic differentiation, climate adaptation, and hybridization in a common-garden trial of interior spruce (Picea engelmannii × glauca) - Samuel Grubinger; Nicholas C. Coops; Gregory A. O'Neill; Jonathan C. Degner; Tongli Wang; Olivia J.M. Waite; José Riofrío; Tiziana L. Koch (Remote Sensing of Environment, 2025)

Detection of non-stand replacing disturbances (NSR) using Harmonized Landsat-Sentinel-2 time series - Madison S. Brown; Nicholas C. Coops; Christopher Mulverhill; Alexis Achim (ISPRS Journal of Photogrammetry and Remote Sensing, 2025)

Advances in Laser Scanning to Assess Carbon in Forests: From Ground-Based to Space-Based Sensors - Nicholas C. Coops; Liam A.K. Irwin; Harry S. Seely; Spencer J. Hardy (Current Forestry Reports, 2025)

Characterizing long-term tree species dynamics in Canada’s forested ecosystems using annual time series remote sensing data - Txomin Hermosilla; Michael A. Wulder; Joanne C. White; Nicholas C. Coops; Christopher W. Bater; Geordie W. Hobart (Forest Ecology and Management, 2024)

Linking microphytobenthos distribution and mudflat geomorphology under varying sedimentary regimes using unoccupied aerial vehicle (UAV)-acquired multispectral reflectance and photogrammetry - Tristan J. Douglas; Nicholas C. Coops; Mark C. Drever; Brian P.V. Hunt; Tara G. Martin (Science of The Total Environment, 2024)

Corrigendum to “Characterizing satellite-derived freeze/thaw regimes through spatial and temporal clustering for the identification of growing season constraints on vegetation productivity” [Remote Sensing of Environment Volume 309 (2024) 114210] - Ramon Melser; Nicholas C. Coops; Chris Derksen (Remote Sensing of Environment, 2024)

Characterizing satellite-derived freeze/thaw regimes through spatial and temporal clustering for the identification of growing season constraints on vegetation productivity - Ramon Melser; Nicholas C. Coops; Chris Derksen (Remote Sensing of Environment, 2024)

Predicting the site productivity of forest tree species using climate niche models - Yueru Zhao; Gregory A. O’Neill; Nicholas C. Coops; Tongli Wang (Forest Ecology and Management, 2024)

Characterizing post-fire northern boreal forest height dynamics - Martin Queinnec; Nicholas C. Coops; Joanne C. White (International Journal of Remote Sensing, 2024)

Integration of tree-ring data, Landsat time series, and ALS-derived topographic variables to quantify growth declines in black spruce - Alexandre Morin-Bernard; Alexis Achim; Nicholas C. Coops; Joanne C. White (Forest Ecology and Management, 2024)

Multidecadal mapping of status and trends in annual burn probability over Canada’s forested ecosystems - Christopher Mulverhill; Nicholas C. Coops; Michael A. Wulder; Joanne C. White; Txomin Hermosilla; Christopher W. Bater (ISPRS Journal of Photogrammetry and Remote Sensing, 2024)

Enhanced watershed status evaluation: Towards an integrated framework to assess fish habitat in forested watersheds using airborne laser scanning data - Spencer Dakin Kuiper; Nicholas C. Coops; Joanne C. White; Scott G. Hinch; Piotr Tompalski; Leanna A. Stackhouse (Forest Ecology and Management, 2024)

Facets of functional diversity support niche‐based explanations for Australian biodiversity gradients - Margaret E. Andrew; Douglas K. Bolton; Gregory J. M. Rickbeil; Nicholas C. Coops (Journal of Biogeography, 2024)
Aim There is widespread support that species richness increases with the available energy of an ecosystem, but the mechanisms underlying this driver of biodiversity patterns remain elusive. We evaluated gradients of functional diversity to test whether the higher species richness of productive, structurally diverse environments is due to a greater range of niches being supported by the abiotic conditions present (environmental filtering), greater availability of biotic resource and habitat niches (more niches) or increasing functional similarity of species (niche packing). Location Australia. Taxon Birds and mammals. Methods We used structural equation modelling to evaluate the relative contributions of climatic harshness (actual evapotranspiration, AET) and the availability of resource (gross primary productivity, GPP) and habitat (tree height) niches on taxonomic richness and functional richness, dispersion and evenness. We performed parallel analyses within 15 bioclimatic zones and continentally to evaluate the scaling of biodiversity gradients and the shifting balance between niche‐based mechanisms along environmental gradients. Results All continental diversity gradients were primarily associated with energy variables, but while species richness of both taxa and all functional diversity measures of bird assemblages increased with AET, mammal functional diversity was more strongly associated with GPP gradients. Results were more variable at the regional scale, but species richness gradients along tree height (birds and mammals) and GPP (mammals) within bioclimatic zones tended not to be paralleled by increases in functional richness or dispersion. Main Conclusions The niche‐based explanations of biodiversity gradients varied in importance with scale, position on environmental gradients and taxonomic group. At the continental extent, bird biodiversity gradients were structured by environmental filtering by climatic harshness, while mammal biodiversity was related to the increasing availability of resource niches with increasing productivity. Niche packing was more prominent at the regional scale, especially in bioclimatic zones where productivity and vegetation height were less limiting, and in mammal assemblages, suggesting that biodiversity patterns scale differently for birds and mammals.

Modeling instream temperature from solar insolation under varying timber harvesting intensities using RPAS laser scanning - Leanna A. Stackhouse; Nicholas C. Coops; Spencer Dakin Kuiper; Scott G. Hinch; Joanne C. White; Piotr Tompalski; Alyssa Nonis; Sarah E. Gergel (Science of The Total Environment, 2024)

Landsat assessment of variable spectral recovery linked to post-fire forest structure in dry sub-boreal forests - Sarah M. Smith-Tripp; Nicholas C. Coops; Christopher Mulverhill; Joanne C. White; Jodi Axelson (ISPRS Journal of Photogrammetry and Remote Sensing, 2024)

Estimating tree species composition from airborne laser scanning data using point-based deep learning models - Brent A. Murray; Nicholas C. Coops; Lukas Winiwarter; Joanne C. White; Adam Dick; Ignacio Barbeito; Ahmed Ragab (ISPRS Journal of Photogrammetry and Remote Sensing, 2024)

Modelling height growth of temperate mixedwood forests using an age-independent approach and multi-temporal airborne laser scanning data - José Riofrío; Joanne C. White; Piotr Tompalski; Nicholas C. Coops; Michael A. Wulder (Forest Ecology and Management, 2023)

Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common‐garden trials of interior spruce ( Picea engelmannii × glauca ) - Samuel Grubinger; Nicholas C. Coops; Gregory A. O'Neill (Global Change Biology, 2023)
Common‐garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common‐garden trials of interior spruce ( Picea engelmannii × glauca ) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height ( R 2 = .98–.99, root mean square error [RMSE] = 0.06–0.10 m) and diameter at breast height (DBH, R 2 = .71–.97, RMSE = 2.57–3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant ( p

Advances in remote sensing of freshwater fish habitat: A systematic review to identify current approaches, strengths and challenges - Spencer Dakin Kuiper; Nicholas C. Coops; Scott G. Hinch; Joanne C. White (Fish and Fisheries, 2023)
Remote sensing technology offers the ability to derive information on freshwater fish habitats across broad geographic areas and has the potential to transform approaches to monitoring. However, the numerous platforms, sensors and analytical software that are available may overwhelm those interested in utilizing this important technology and thus limit its broad application and uptake. Our review is intended to shed light on the capacity of this technology to transform freshwater fish habitat monitoring by examining the fundamental characteristics of the major remote sensing technologies that have been used for characterizing freshwater habitats, conducting a systematic literature review of studies that have used remote sensing technologies to characterize freshwater fish habitats and, highlighting some of the key habitat features, fish species and regions, that have been examined. Lastly, we identify the relative strengths and weaknesses of the various remote sensing technologies that can be used, recommend future research that could help improve the use of these technologies, and, provide a series of important considerations for those who are interested in utilizing these technologies for freshwater fish habitat characterization.

An assessment approach for pixel-based image composites - Saverio Francini; Txomin Hermosilla; Nicholas C. Coops; Michael A. Wulder; Joanne C. White; Gherardo Chirici (ISPRS Journal of Photogrammetry and Remote Sensing, 2023)

Summer physical habitat associations and movement of sympatric juvenile rainbow trout (Oncorhynchus mykiss) and Dolly Varden char (Salvelinus malma) in coastal streams of British Columbia - Alyssa Nonis; Scott G. Hinch; Nicholas C. Coops (Environmental Biology of Fishes, 2023)

Estimating and mapping forest age across Canada's forested ecosystems - James C. Maltman; Txomin Hermosilla; Michael A. Wulder; Nicholas C. Coops; Joanne C. White (Remote Sensing of Environment, 2023)

Abundance patterns of mammals across Russia explained by remotely sensed vegetation productivity and snow indices - Elena Razenkova; Maxim Dubinin; Anna M. Pidgeon; Martina L. Hobi; Likai Zhu; Eugenia V. Bragina; Andrew M. Allen; Murray K. Clayton; Leonid M. Baskin; Nicholas C. Coops; Volker C. Radeloff (Journal of Biogeography, 2023)
Aim Predicting biodiversity responses to global changes requires good models of species' distributions. Both environmental conditions and human activities determine population density patterns. However, quantifying the relationship between wildlife population densities and their underlying environmental conditions across large geographical scales has remained challenging. Our goal was to explain the abundances of mammal species based on their response to several remotely sensed indices including the Dynamic Habitat Indices (DHIs) and the novel Winter Habitat Indices (WHIs). Location Russia, the majority of regions. Taxon Eight mammal species. Methods We estimated average population densities for each species across Russia from 1981 to 2010 from winter track counts. The DHIs measure vegetative productivity, a proxy for food availability. Our WHIs included the duration of snow‐free ground, duration of snow‐covered ground and the start, end and length of frozen season. In models, we included elevation, climate conditions, human footprint index. We parameterized multiple linear regression and applied best‐subset model selection to determine the main factors influencing population density. Results The DHIs were included in some of the top‐twelve models of every species, and in the top model for moose, wild boar, red fox and wolf, so they were important for species at all trophic levels. The WHIs were included in top models for all species except roe deer, demonstrating the importance of winter conditions. The duration of frozen ground without snow and the end of frozen season were particularly important. Our top models performed well for all the species ( R 2 adj 0.43–0.87). Main Conclusions The combination of the DHIs and the WHIs with climate and human‐related variables resulted in high explanatory power. We show that vegetation productivity and winter conditions are key drivers of variation in population density of eight species across Russia.

Continuous monitoring and sub-annual change detection in high-latitude forests using Harmonized Landsat Sentinel-2 data - Christopher Mulverhill; Nicholas C. Coops; Alexis Achim (ISPRS Journal of Photogrammetry and Remote Sensing, 2023)

Effects of climate change on forest plantation productivity in Chile - Gonzalo Carrasco; Auro C. Almeida; Mark Falvey; Guillermo Federico Olmedo; Peter Taylor; Fernando Santibañez; Nicholas C. Coops (Global Change Biology, 2022)
Forest plantations in Chile occupy more than 2.2 million ha and are responsible for 2.1% of the GDP of the country's economy. The ability to accurately predictions of plantations productivity under current and future climate has an impact can enhance on forest management and industrial wood production. The use of process‐based models to predict forest growth has been instrumental in improving the understanding and quantifying the effects of climate variability, climate change, and the impact of atmospheric CO 2 concentration and management practices on forest growth. This study uses the 3‐PG model to predict future forest productivity Eucalyptus globulus and Pinus radiata . The study integrates climate data from global circulation models used in CMIP5 for scenarios RCP26 and RCP85, digital soil maps for physical and chemical variables. Temporal and spatial tree growth inventories were used to compare with the 3‐PG predictions. The results indicated that forest productivity is predicted to potentially increase stand volume (SV) over the next 50 years by 26% and 24% for the RCP26 scenario and between 73% and 62% for the RCP85 scenario for E. globulus and P. radiata , respectively. The predicted increases can be explained by a combination of higher level of atmospheric CO 2 , air temperatures closer to optimum than current, and increases in tree water use efficiency. If the effect of CO 2 is not considered, the predicted differences of SV for 2070 are 16% and 14% for the RCP26 scenario and 22% and 14% for RCP85 for the two species. While shifts in climate and increasing CO 2 are likely to benefit promote higher productivity, other factors such as lack insufficient availability of soil nutrients, events such as increasing frequency and duration of droughts, longer periods of extreme temperatures, competing vegetation, and occurrence of new pests and diseases may compromise these potential gains.

Tree species, crown cover, and age as determinants of the vertical distribution of airborne LiDAR returns - Etienne B. Racine; Nicholas C. Coops; Jean Bégin; Mari Myllymäki (Trees, 2021)
Key message We assessed even-aged stand vertical distributions of LiDAR returns and found that tree species, age, and crown cover each have a distinct pattern that together explains up to 47% of the variation. Abstract Light detection and ranging (LiDAR) provides information on the vertical structure of forest stands enabling detailed and extensive ecosystem study. The vertical structure is often summarized by scalar features and data-reduction techniques that limit the interpretation of results. Instead, we quantified the influence of three variables, species, crown cover, and age, on the vertical distribution of airborne LiDAR returns from forest stands. We studied 5428 regular, even-aged stands in Quebec (Canada) with five dominant species: balsam fir [ Abies balsamea (L.) Mill.], paper birch ( Betula papyrifera Marsh), black spruce [ Picea mariana (Mill.) BSP], white spruce ( Picea glauca Moench) and aspen ( Populus tremuloides Michx.). We modeled the vertical distribution against the three variables using a functional general linear model and a novel nonparametric graphical test of significance. Results indicate that LiDAR returns from aspen stands had the most uniform vertical distribution. Balsam fir and white birch distributions were similar and centered at around 50% of the stand height, and black spruce and white spruce distributions were skewed to below 30% of stand height ( $$p$$ p <0.001). Increased crown cover concentrated the distributions around 50% of stand height. Increasing age gradually shifted the distributions higher in the stand for stands younger than 70-years, before plateauing and slowly declining at 90–120 years. Results suggest that the vertical distributions of LiDAR returns depend on the three variables studied.

Detection and Quantification of Coarse Woody Debris in Natural Forest Stands Using Airborne LiDAR - Lukas R Jarron; Nicholas C Coops; William H MacKenzie; Pamela Dykstra (Forest Science, 2021)
Coarse woody debris (CWD) is a meaningful contributor to forest carbon cycles, wildlife habitat, and biodiversity and can influence wildfire behavior. Using airborne laser scanning (ALS), we map CWD across a range of natural forest stand types in north-central British Columbia, Canada, providing forest managers with spatially detailed information on the presence and volume of ground-level woody biomass. We describe 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 ALS returns into measurable, vectorized shapes. We then assess the accuracy of CWD detection at the individual log level and predict CWD volume at the plot level. We detected 64% of CWD logs and 79% of CWD volume within our plots. Increased elevation of CWD significantly aided detection (P = 0.04), whereas advanced stages of decay hindered detection (P = 0.04). ALS-predicted CWD volume totals were compared against field-measured CWD and displayed a strong correlation (R = 0.81), allowing us to expand the methodology to map CWD over a larger region. The expanded CWD volume map compared ALS volume predictions between stands and suggests greater volume in stands with older and more heterogeneous stand structure.

Publisher Correction: Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data - Piotr Tompalski; Nicholas C. Coops; Joanne C. White; Tristan R. H. Goodbody; Chris R. Hennigar; Michael A. Wulder; Jarosław Socha; Murray E. Woods (Current Forestry Reports, 2021)

Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data - Piotr Tompalski; Nicholas C. Coops; Joanne C. White; Tristan R.H. Goodbody; Chris R. Hennigar; Michael A. Wulder; Jarosław Socha; Murray E. Woods (Current Forestry Reports, 2021)
Purpose of Review The increasing availability of three-dimensional point clouds, including both airborne laser scanning and digital aerial photogrammetry, allow for the derivation of forest inventory information with a high level of attribute accuracy and spatial detail. When available at two points in time, point cloud datasets offer a rich source of information for detailed analysis of change in forest structure. Recent Findings Existing research across a broad range of forest types has demonstrated that those analyses can be performed using different approaches, levels of detail, or source data. By reviewing the relevant findings, we highlight the potential that bi- and multi-temporal point clouds have for enhanced analysis of forest growth. We divide the existing approaches into two broad categories— – approaches that focus on estimating change based on predictions of two or more forest inventory attributes over time, and approaches for forecasting forest inventory attributes. We describe how point clouds acquired at two or more points in time can be used for both categories of analysis by comparing input airborne datasets, before discussing the methods that were used, and resulting accuracies. Summary To conclude, we outline outstanding research gaps that require further investigation, including the need for an improved understanding of which three-dimensional datasets can be applied using certain methods. We also discuss the likely implications of these datasets on the expected outcomes, improvements in tree-to-tree matching and analysis, integration with growth simulators, and ultimately, the development of growth models driven entirely with point cloud data.

Changing spring snow cover dynamics and early season forage availability affect the behavior of a large carnivore - Gregory J. M. Rickbeil; Nicholas C. Coops; Ethan E. Berman; Cameron J. R. McClelland; Douglas K. Bolton; Gordon B. Stenhouse (Global Change Biology, 2020)
Changing climates are altering wildlife habitats and wildlife behavior in complex ways. Here, we examine how changing spring snow cover dynamics and early season forage availability are altering grizzly bear ( Ursus arctos ) behavior postden emergence. Telemetry data were used to identify spring activity dates for 48 individuals in the Yellowhead region of Alberta, Canada. Spring activity date was related to snow cover dynamics using a daily percent snow cover dataset. Snow melt end date, melt rate, and melt consistency explained 45% of the variation in spring activity date. We applied this activity date model across the entire Yellowhead region from 2000 to 2016 using simulated grizzly bear home ranges. Predicted spring activity date was then compared with a daily spring forage availability date dataset, resulting in “wait time” estimates for four key early season forage species. Temporal changes in both spring activity date and early season forage “wait times” were assessed using non‐parametric regression. Grizzly bear activity date was found to have either remained constant (95%) or become earlier (5%) across the study area; virtually no areas with significantly later spring activity dates were detected. Similarly, the majority of “wait times” did not change (85%); however, the majority of significant changes in “wait times” for the four early season forage species indicated that “wait times” were lessening where changes were detected. Our results show that spring activity date is largely dictated by snow melt characteristics and that changing snow melt conditions may result in earlier spring activity. However, early season food stress conditions are likely to remain unchanged or improve as vegetation phenology also becomes earlier. Our findings extend the recent work examining animal movement in response to changing phenology from migratory birds and ungulates to an apex predator, further demonstrating the potential effects of changing climates on wildlife species.

Forest Road Status Assessment Using Airborne Laser Scanning - Katalin Waga; Piotr Tompalski; Nicholas C Coops; Joanne C White; Michael A Wulder; Jukka Malinen; Timo Tokola (Forest Science, 2020)
Forest roads allow access for silvicultural operations, harvesting, recreational activities, wildlife management, and fire suppression. In British Columbia, Canada, roads that are no longer required must be deactivated (temporarily, semipermanently, or permanently) in order to minimize the impact on the overall forested ecosystem. However, the remoteness and size of the road network present challenges for monitoring. Our aim was to examine the utility of airborne laser scanning data to assess the status and quality of forest roads across 52,000 hectares of coastal forest in British Columbia. Within the forest estate, roads can be active or deactivated, or have an unknown status. We classified road segments based on the vegetation growth on the road surface, and edges, by classifying the height distribution of airborne laser scanning returns within each road segment into four groups: no vegetation, minor vegetation, dense understory vegetation, and dense overstory vegetation. Validation indicated that 73 percent of roads were classified correctly when compared to independent field observations. The majority were classified as active roads with no vegetation or deactivated with dense vegetation. The approach presented herein can aid forest managers in verifying the status of the roads in their management area, especially in remote areas where field assessments are costly and time-consuming.

Building a perceptual zone of influence for wildlife: delineating the effects of roads on grizzly bear movement - Bethany M. Parsons; Nicholas C. Coops; Gordon B. Stenhouse; A. Cole Burton; Trisalyn A. Nelson (European Journal of Wildlife Research, 2020)

Characterizing variations in growth characteristics between Douglas-fir with different genetic gain levels using airborne laser scanning - Francois du Toit; Nicholas C. Coops; Piotr Tompalski; Tristan R. H. Goodbody; Yousry A. El-Kassaby; Michael Stoehr; Darren Turner; Arko Lucieer (Trees, 2020)

Effect of ground surface interpolation methods on the accuracy of forest attribute modelling using unmanned aerial systems-based digital aerial photogrammetry - Alexander N. V. Graham; Nicholas C. Coops; Piotr Tompalski; Andrew Plowright; Mike Wilcox (International Journal of Remote Sensing, 2020)

Asian Cities are Greening While Some North American Cities are Browning: Long-Term Greenspace Patterns in 16 Cities of the Pan-Pacific Region - Jiali Jin; Sarah E. Gergel; Yuhao Lu; Nicholas C. Coops; Cheng Wang (Ecosystems, 2020)

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions - Tristan R. H. Goodbody; Nicholas C. Coops; Joanne C. White (Current Forestry Reports, 2019)

Long-term changes in the primary productivity of migratory caribou (Rangifer tarandus) calving grounds and summer pasture on the Quebec-Labrador Peninsula (Northeastern Canada): the mixed influences of climate change and caribou herbivory - Allen Brett Campeau; Gregory J. M. Rickbeil; Nicholas C. Coops; Steeve D. Côté (Polar Biology, 2019)

Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems - Tristan R.H Goodbody; Nicholas C Coops; Txomin Hermosilla; Piotr Tompalski; Patrick Crawford (International Journal of Remote Sensing, 2018)

How global climate change and regional disturbance can expand the invasion risk? Case study of Lantana camara invasion in the Himalaya - Ninad Avinash Mungi; Nicholas C. Coops; K. Ramesh; Gopal S. Rawat (Biological Invasions, 2018)

Land cover 2.0 - Michael A. Wulder; Nicholas C. Coops; David P. Roy; Joanne C. White; Txomin Hermosilla (International Journal of Remote Sensing, 2018)

Changing northern vegetation conditions are influencing barren ground caribou ( Rangifer tarandus groenlandicus ) post‐calving movement rates - Gregory J. M. Rickbeil; Txomin Hermosilla; Nicholas C. Coops; Joanne C. White; Michael A. Wulder; Trevor C. Lantz (Journal of Biogeography, 2018)
Aim To quantify changes in vegetation productivity over the past three decades across five barren ground caribou ( Rangifer tarandus groenlandicus ) herd ranges and assess how these changes are influencing caribou movement rates. Location Northwest Territories and Nunavut, Canada. Methods As an indicator of vegetation productivity, the enhanced vegetation index ( EVI ) was calculated on newly developed cloud‐free, gap‐free, Landsat surface reflectance image composites representing 1984–2012. Changes in EVI were assessed on a pixel basis using Theil‐Sen's nonparametric regression and compared across herd ranges and land cover types using generalized least squares regression. Animal movement velocity was calculated from caribou telemetry data and generalized additive mixed models were used to link movement rates with vegetation productivity during the post‐calving phase of the year (July and August). Results Vegetation productivity increased across the five caribou herd ranges examined. The largest productivity increase occurred over the ranges of the most western herds, with the largest observed changes in grassland or shrub habitats. Caribou tended to move more slowly through tundra habitats with elevated levels of productivity to a point, while grasslands movement rates decreased linearly with increasing productivity. Movement velocities peaked at intermediate productivity levels in shrub habitats. Main conclusions Over the three decades of collected data, barren ground caribou habitats have become more productive, which is consistent with other studies that have documented increases in Arctic vegetation productivity. The more western herds, whose ranges are also closest to the Arctic Ocean, experienced the largest increases in productivity. Finally, we demonstrate that barren ground caribou movement patterns will likely change as a result of changing vegetation productivity in complex manners depending on herd, habitat type and the magnitude of change in vegetation productivity.

Assessing variability in post‐fire forest structure along gradients of productivity in the Canadian boreal using multi‐source remote sensing - Douglas K. Bolton; Nicholas C. Coops; Txomin Hermosilla; Michael A. Wulder; Joanne C. White (Journal of Biogeography, 2017)
Aim Forest regeneration following fire is an important component of the global carbon cycle, but it is difficult to monitor over large and remote forested regions, such as Canada's north. In this study, we aim to (1) characterize how forest regeneration following fire varies across the Canadian boreal and (2) determine if this variability is captured by satellite‐derived estimates of productivity. Location Canadian boreal. Methods We relate structural measurements from light detection and ranging (lidar) data to gross primary productivity ( GPP ) estimates from the MOD erate Resolution Imaging Spectroradiometer ( MODIS ) along a 25‐year chronosequence of forest regeneration following fire. Over 400 patches that burned from 1985–2009 were analysed, with fire information obtained from a national Landsat‐derived record of forest change. Results In the first 15 years since fire ( YSF ), estimates of percent canopy cover (> 2 m) were typically low regardless of GPP (mean = 11.0–16.0%, SD = 7.8–8.9%) and correlations to GPP were relatively weak ( r = 0.18–0.48). Canopy cover was more variable between stands by 16–25 YSF (mean = 16.2–21.7%, SD = 16.0–17.1%), and correlations to GPP were stronger ( r = 0.63–0.71, P YSF (mean = 4.9–5.0 m, SD = 0.9–1.1 m) and weakly related to GPP ( r = 0.16–0.21). Main conclusions Satellite‐derived estimates of productivity capture differences in canopy structure across the boreal, but only after 15 YSF . While canopy cover varied strongly along gradients of productivity from 16–25 YSF , differences in vertical growth were less pronounced due to slow boreal growth rates. Our results provide important insights into how satellite‐derived estimates of productivity are realized structurally, as understanding regional variation in forest regeneration is critical to quantifying carbon dynamics in forests. Combining lidar‐derived estimates of structure with Landsat‐derived disturbance history is a valuable approach for characterizing variability in post‐fire structure over large forested areas.

Updating residual stem volume estimates using ALS- and UAV-acquired stereo-photogrammetric point clouds - Tristan R. H. Goodbody; Nicholas C. Coops; Piotr Tompalski; Patrick Crawford; Ken J. K. Day (International Journal of Remote Sensing, 2017)

Relationships between individual‐tree mortality and water‐balance variables indicate positive trends in water stress‐induced tree mortality across North America - Robbie A. Hember; Werner A. Kurz; Nicholas C. Coops (Global Change Biology, 2017)
Accounting for water stress‐induced tree mortality in forest productivity models remains a challenge due to uncertainty in stress tolerance of tree populations. In this study, logistic regression models were developed to assess species‐specific relationships between probability of mortality ( P m ) and drought, drawing on 8.1 million observations of change in vital status ( m ) of individual trees across North America. Drought was defined by standardized (relative) values of soil water content ( W s,z ) and reference evapotranspiration ( ET r,z ) at each field plot. The models additionally tested for interactions between the water‐balance variables, aridity class of the site ( AC ), and estimated tree height ( h ). Considering drought improved model performance in 95 (80) per cent of the 64 tested species during calibration (cross‐validation). On average, sensitivity to relative drought increased with site AC (i.e. aridity). Interaction between water‐balance variables and estimated tree height indicated that drought sensitivity commonly decreased during early height development and increased during late height development, which may reflect expansion of the root system and decreasing whole‐plant, leaf‐specific hydraulic conductance, respectively. Across North America, predictions suggested that changes in the water balance caused mortality to increase from 1.1% yr −1 in 1951 to 2.0% yr −1 in 2014 (a net change of 0.9 ± 0.3% yr −1 ). Interannual variation in mortality also increased, driven by increasingly severe droughts in 1988, 1998, 2006, 2007 and 2012. With strong confidence, this study indicates that water stress is a common cause of tree mortality. With weak‐to‐moderate confidence, this study strengthens previous claims attributing positive trends in mortality to increasing levels of water stress. This ‘learn‐as‐we‐go’ approach – defined by sampling rare drought events as they continue to intensify – will help to constrain the hydraulic limits of dominant tree species and the viability of boreal and temperate forest biomes under continued climate change.

Barren‐ground caribou ( Rangifer tarandus groenlandicus ) behaviour after recent fire events; integrating caribou telemetry data with Landsat fire detection techniques - Gregory J. M. Rickbeil; Txomin Hermosilla; Nicholas C. Coops; Joanne C. White; Michael A. Wulder (Global Change Biology, 2017)
Fire regimes are changing throughout the North American boreal forest in complex ways. Fire is also a major factor governing access to high‐quality forage such as terricholous lichens for barren‐ground caribou ( Rangifer tarandus groenlandicus ). Additionally, fire alters forest structure which can affect barren‐ground caribou's ability to navigate in a landscape. Here, we characterize how the size and severity of fires are changing across five barren‐ground caribou herd ranges in the Northwest Territories and Nunavut, Canada. Additionally, we demonstrate how time since fire, fire severity, and season result in complex changes in caribou behavioural metrics estimated using telemetry data. Fire disturbances were identified using novel gap‐free Landsat surface reflectance composites from 1985 to 2011 across all herd ranges. Burn severity was estimated using the differenced normalized burn ratio. Annual area burned and burn severity were assessed through time for each herd and related to two behavioural metrics: velocity and relative turning angle. Neither annual area burned nor burn severity displayed any temporal trend within the study period. However, certain herds, such as the Ahiak/Beverly, have more exposure to fire than other herds (i.e. Cape Bathurst had a maximum forested area burned of less than 4 km 2 ). Time since fire and burn severity both significantly affected velocity and relative turning angles. During fall, winter, and spring, fire virtually eliminated foraging‐focused behaviour for all 26 years of analysis while more severe fires resulted in a marked increase in movement‐focused behaviour compared to unburnt patches. Between seasons, caribou used burned areas as early as 1‐year postfire, demonstrating complex, nonlinear reactions to time since fire, fire severity, and season. In all cases, increases in movement‐focused behaviour were detected postfire. We conclude that changes in caribou behaviour immediately postfire are primarily driven by changes in forest structure rather than changes in terricholous lichen availability.

An ecoregion assessment of projected tree species vulnerabilities in western North America through the 21st century - Amanda S. Mathys; Nicholas C. Coops; Richard H. Waring (Global Change Biology, 2017)
Forest ecosystems across western North America will likely see shifts in both tree species dominance and composition over the rest of this century in response to climate change. Our objective in this study was to identify which ecological regions might expect the greatest changes to occur. We used the process‐based growth model 3‐ PG , to provide estimates of tree species responses to changes in environmental conditions and to evaluate the extent that species are resilient to shifts in climate over the rest of this century. We assessed the vulnerability of 20 tree species in western North America using the Canadian global circulation model under three different emission scenarios. We provided detailed projections of species shifts by including soil maps that account for the spatial variation in soil water availability and soil fertility as well as by utilizing annual climate projections of monthly changes in air temperature, precipitation, solar radiation, vapor pressure deficit and frost at a spatial resolution of one km. Projected suitable areas for tree species were compared to their current ranges based on observations at >40 000 field survey plots. Tree species were classified as vulnerable if environmental conditions projected in the future appear outside that of their current distribution ≥70% of the time. We added a migration constraint that limits species dispersal to −1 to provide more realistic projections on species distributions. Based on these combinations of constraints, we predicted the greatest changes in the distribution of dominant tree species to occur within the Northwest Forested Mountains and the highest number of tree species stressed will likely be in the North American Deserts. Projected climatic changes appear especially unfavorable for species in the subalpine zone, where major shifts in composition may lead to the emergence of new forest types.

Predicting large wildfires across western North America by modeling seasonal variation in soil water balance - Richard H. Waring; Nicholas C. Coops (Climatic Change, 2016)

Forest recovery trends derived from Landsat time series for North American boreal forests - Paul D. Pickell; Txomin Hermosilla; Ryan J. Frazier; Nicholas C. Coops; Michael A. Wulder (International Journal of Remote Sensing, 2016)

Attributing changes in land cover using independent disturbance datasets: a case study of the Yucatan Peninsula, Mexico - Vanessa S. Mascorro; Nicholas C. Coops; Werner A. Kurz; Marcela Olguín (Regional Environmental Change, 2016)

Characterizing Forest Growth and Productivity Using Remotely Sensed Data - Nicholas C. Coops (Current Forestry Reports, 2015)

Assessing scalar concentration footprint climatology and land surface impacts on tall-tower CO2 concentration measurements in the boreal forest of central Saskatchewan, Canada - Baozhang Chen; Huifang Zhang; Nicholas C. Coops; Dongjie Fu; Douglas E. J. Worthy; Guang Xu; T. Andy Black (Theoretical and Applied Climatology, 2014)

Assessing coastal species distribution models through the integration of terrestrial, oceanic and atmospheric data - Gregory J. M. Rickbeil; Nicholas C. Coops; Mark C. Drever; Trisalyn A. Nelson (Journal of Biogeography, 2014)
Aim When considering multiple species in distribution models, environmental variables should be selected that describe the group of species' environmental requirements. This can, however, be challenging in locations such as coastal areas, where different species may respond to terrestrial, oceanic and/or atmospheric conditions. Here, we evaluate the use of remotely sensed ( RS ) terrestrial, oceanic and interpolated climate variables, as well as a more detailed shore‐zone data set, as a means of modelling the distributions of coastal bird species with diverse habitat requirements. Location Coastal British Columbia, Canada. Methods Boosted regression trees were used to model the distributions of 60 species of coastal birds using each environmental variable group, run individually and in combination. Models were assessed for their predictive ability and model fit, as well as for model overfitting. Results Models that incorporated terrestrial data were found to produce the highest model fit and predictive ability of the broad‐scale environmental groups. Incorporating the fine‐scale shore‐zone data offered little improvement, as did selecting the best model in terms of predictive ability by species. Model fit and predictive ability were also found to vary by functional feeding group, with insectivores and benthivores being the best‐modelled. Main conclusions Having access to both broad‐scale RS environmental data and more detailed coastal shore‐zone data produced modest improvements over employing RS environmental data alone. Models using only the terrestrial data set, however, performed similarly to the best single model type (terrestrial + shore‐zone), indicating that broad‐scale environmental data also offer an effective means of estimating coastal bird distributions. Testing multiple environmental variable groups in different combinations and selecting the best model allowed models to be optimized by species; conversely, the results of a ‘one model fits all’ approach were comparable to those of the best models, indicating that a single‐model approach is also valid.

Characterization of an alpine tree line using airborne LiDAR data and physiological modeling - Nicholas C. Coops; Felix Morsdorf; Michael E. Schaepman; Niklaus E. Zimmermann (Global Change Biology, 2013)
Understanding what environmental drivers control the position of the alpine tree line is important for refining our understanding of plant stress and tree development, as well as for climate change studies. However, monitoring the location of the tree line position and potential movement is difficult due to cost and technical challenges, as well as a lack of a clear boundary. Advanced remote sensing technologies such as Light Detection and Ranging ( LiDAR ) offer significant potential to map short individual tree crowns within the transition zone despite the lack of predictive capacity. Process‐based forest growth models offer a complementary approach by quantifying the environmental stresses trees experience at the tree line, allowing transition zones to be defined and ultimately mapped. In this study, we investigate the role remote sensing and physiological, ecosystem‐based modeling can play in the delineation of the alpine tree line. To do so, we utilize airborne Li DAR data to map tree height and stand density across a series of altitudinal gradients from below to above the tree line within the Swiss National Park ( SNP ), Switzerland. We then utilize a simple process‐based model to assess the importance of seasonal variations on four climatically related variables that impose non‐linear constraints on photosynthesis. Our results indicate that all methods predict the tree line to within a 50 m altitudinal zone and indicate that aspect is not a driver of significant variations in tree line position in the region. Tree cover, rather than tree height is the main discriminator of the tree line at higher elevations. Temperatures in fall and spring are responsible for the major differences along altitudinal zones, however, changes in evaporative demand also control plant growth at lower altitudes. Our results indicate that the two methods provide complementary information on tree line location and, when combined, provide additional insights into potentially endangered forest/grassland transition zones.

Measuring forest structure along productivity gradients in the Canadian boreal with small-footprint Lidar - Douglas K. Bolton; Nicholas C. Coops; Michael A. Wulder (Environmental Monitoring and Assessment, 2013)

Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots - Brice Mora; Michael A. Wulder; Geordie W. Hobart; Joanne C. White; Christopher W. Bater; Francois A. Gougeon; Andrés Varhola; Nicholas C. Coops (International Journal of Remote Sensing, 2013)

Describing avifaunal richness with functional and structural bioindicators derived from advanced airborne remotely sensed data - Trevor G. Jones; Peter Arcese; Tara Sharma; Nicholas C. Coops (International Journal of Remote Sensing, 2013)

Ecosystem classifications based on summer and winter conditions - Margaret E. Andrew; Trisalyn A. Nelson; Michael A. Wulder; George W. Hobart; Nicholas C. Coops; Carson J. Q. Farmer (Environmental Monitoring and Assessment, 2013)

Accelerating regrowth of temperate‐maritime forests due to environmental change - Robbie A. Hember; Werner A. Kurz; Juha M. Metsaranta; T. Andy Black; Robert D. Guy; Nicholas C. Coops (Global Change Biology, 2012)
To understand how environmental changes have influenced forest productivity, stemwood biomass ( B ) dynamics were analyzed at 1267 permanent inventory plots, covering a combined 209 ha area of unmanaged temperate‐maritime forest in southwest British Columbia, Canada. Net stemwood production (Δ B ) was derived from periodic remeasurements of B collected over a 40‐year measurement period (1959–1998) in stands ranging from 20 to 150 years old. Comparison between the integrated age response of net stemwood production, Δ B ( A ), and the age response of stemwood biomass, B ( A ), suggested a 58 ± 11% increase in Δ B between the first 40 years of the chronosequence period (1859–1898) and the measurement period. To estimate extrinsic forcing on Δ B , several different candidate models were developed to remove variation explained by intrinsic factors. All models exhibited temporal bias, with positive trends in (observed minus predicted) residual Δ B ranging between of 0.40 and 0.64% yr −1 . Applying the same methods to stemwood growth ( G ) indicated residual increases ranging from 0.43 and 0.67% yr −1 . Higher trend estimates corresponded with models that included site index ( SI ) as a predictor, which may reflect exaggeration of the age‐decline in SI tables. Choosing a model that excluded SI , suggested that Δ B increased by 0.40 ± 0.18% yr −1 , while G increased by 0.43 ± 0.12% yr −1 over the measurement period. Residual G was significantly correlated with atmospheric carbon dioxide ( CO 2 ), temperature ( T ), and climate moisture index ( CMI ). However, models driven with climate and CO 2 , alone, could not simultaneously explain long‐term and measurement‐period trends without additional representation of indirect effects, perhaps reflecting compound interest on direct physiological responses to environmental change. Evidence of accelerating forest regrowth highlights the value of permanent inventories to detect and understand systematic changes in forest productivity caused by environmental change.

Beta‐diversity gradients of butterflies along productivity axes - Margaret E. Andrew; Michael A. Wulder; Nicholas C. Coops; Guy Baillargeon (Global Ecology and Biogeography, 2012)
Aim Several lines of evidence suggest that beta diversity, or dissimilarity in species composition, should increase with productivity: (1) the latitudinal species richness gradient is most closely related to productivity and associated latitudinal beta‐diversity relationships have been described, and (2) the scale dependence of the productivity–diversity relationship implies that there should be a positive productivity–beta‐diversity relationship. However, such a pattern has not yet been demonstrated at broad scales. We test if there is a gradient of increasing beta diversity with productivity. Location Canada. Methods Canada was clustered into regions of similar productivity regimes along three remotely sensed productivity axes (minimum and integrated annual productivity, seasonality of productivity) and elevation. The overall (β j ), turnover (β sim ) and nestedness (β nes ) components of beta diversity within each productivity regime were estimated with pairwise dissimilarity metrics and related to cluster productivity with partial linear regression and with spatial autoregression. Tests were performed for all species, productivity breadth‐based subsets (e.g. species occurring in many and a moderate number of productivity regimes), and pre‐ and post‐1970 butterfly records. Beta diversity between adjacent clusters along the productivity gradients was also evaluated. Results Within‐cluster β j and β sim increased with productivity and decreased with seasonality. The converse was true for β nes . All species subsets responded similarly; however, productivity–beta‐diversity relationships were weaker for the post‐1970 temporal subset and strongest for species of moderate breadth. Between‐cluster beta diversity (β j ) and nestedness (β nes ) declined with productivity. Main conclusions As predicted, beta diversity of communities within productivity regimes was observed to increase with productivity. This pattern was driven largely by a gradient of species turnover. Therefore, beta diversity may make an important contribution to the broad‐scale gradient of species richness with productivity. However, this species richness gradient dominates regional beta diversity between productivity regimes, resulting in decreasing between‐productivity dissimilarity with productivity driven by a concurrent decline in nestedness.

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment - Christopher W. Bater; Nicholas C. Coops; Michael A. Wulder; Thomas Hilker; Scott E. Nielsen; Greg McDermid; Gordon B. Stenhouse (Environmental Monitoring and Assessment, 2011)

Tracking plant physiological properties from multi-angular tower-based remote sensing - Thomas Hilker; Anatoly Gitelson; Nicholas C. Coops; Forrest G. Hall; T. Andrew Black (Oecologia, 2011)

A process-based approach to estimate lodgepole pine (Pinus contorta Dougl.) distribution in the Pacific Northwest under climate change - Nicholas C. Coops; Richard H. Waring (Climatic Change, 2011)

Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand - Thomas Hilker; Martin van Leeuwen; Nicholas C. Coops; Michael A. Wulder; Glenn J. Newnham; David L. B. Jupp; Darius S. Culvenor (Trees, 2010)

Implications of differing input data sources and approaches upon forest carbon stock estimation - Michael A. Wulder; Joanne C. White; Graham Stinson; Thomas Hilker; Werner A. Kurz; Nicholas C. Coops; Benôit St-Onge; J. A. Trofymow (Environmental Monitoring and Assessment, 2010)

Curve fitting of time-series Landsat imagery for characterizing a mountain pine beetle infestation - Nicholas R. Goodwin; Steen Magnussen; Nicholas C. Coops; Michael A. Wulder (International Journal of Remote Sensing, 2010)

Characterizing the forest fragmentation of Canada’s national parks - Nicholas O. Soverel; Nicholas C. Coops; Joanne C. White; Michael A. Wulder (Environmental Monitoring and Assessment, 2010)

Estimating the reduction in gross primary production due to mountain pine beetle infestation using satellite observations - Nicholas C. Coops; Michael A. Wulder (International Journal of Remote Sensing, 2010)

Integration of LIDAR and Digital Aerial Imagery for Detailed Estimates of Lodgepole Pine ( Pinus contorta ) Volume Killed by Mountain Pine Beetle ( Dendroctonus ponderosae ) - Christopher W. Bater; Michael A. Wulder; Joanne C. White; Nicholas C. Coops (Journal of Forestry, 2010)
The Canadian province of British Columbia is currently experiencing the most severe mountain pine beetle infestation on record. Estimating the volume of pine killed across the land base is critical for understanding the impacts of the infestation and for adjusting annual timber supply forecasts and allocating mitigation resources. At present, coarse strategic data sets are used to quantify pine volume losses associated with mountain pine beetle; however, we posit that high spatial resolution remotely sensed data sets may facilitate more refined estimates of volume losses. In this article, we proposed a method whereby LIDAR and digital aerial imagery are used in a sampling approach to estimate volume losses at the plot level. Fifty-five 0.25-ha photo-plots were established. Mean plot dominant stand heights were derived from LIDAR data. Tree species, mountain pine beetle attack status (red or gray), dbh, stem density, and mean plot age were estimated via manual interpretation of the aerial imagery. These attributes were then combined using species-specific equations to estimate the volume of pine killed within each of the sample photo-plots. The plot-level average volume killed by mountain pine beetle was estimated to be 40 m3 (SD = 27 m 3 ), or 159 m 3 /ha (SD = 109 m 3 /ha), representing approximately 42% of total pine by volume. The plot-level procedures for producing estimates of infestation impact are developed to form the basis for a large-area, sample-based monitoring program.

Employing ground-based spectroscopy for tree-species differentiation in the Gulf Islands National Park Reserve - Trevor G. Jones; Nicholas C. Coops; Tara Sharma (International Journal of Remote Sensing, 2010)

Seasonal controls on interannual variability in carbon dioxide exchange of a near‐end‐of rotation Douglas‐fir stand in the Pacific Northwest, 1997–2006 - BAOZHANG CHEN; T. ANDREW BLACK; NICHOLAS C. COOPS; PRAVEENA KRISHNAN; RACHHPAL JASSAL; CHRISTIAN BRÜMMER; ZORAN NESIC (Global Change Biology, 2009)
This study analyzes 9 years of eddy‐covariance (EC) data carried out in a Pacific Northwest Douglas‐fir ( Pseudotsuga menzesii ) forest (58‐year old in 2007) on the east coast of Vancouver Island, Canada, and characterizes the seasonal and interannual variability in net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration ( R e ) and primary climatic controls on these fluxes. The annual values (± SD) of NEP, GPP and R e were 357 ± 51, 2124 ± 125, and 1767 ± 146 g C m −2 yr −1 , respectively, with ranges of 267–410, 1592–2338, and 1642–2071 g C m −2 yr −1 , respectively. Spring to early summer (March–June) accounted for more than 80% of annual NEP while late spring to early autumn (May–August) was mainly responsible for its interannual variability (∼80%). The major drivers of interannual variability in annual carbon (C) fluxes were annual and spring mean air temperatures ( T a ) and water deficiency during late summer and autumn (July–October) when this Douglas‐fir forest growth was often water‐limited. Photosynthetically active radiation ( Q ), and the combination of Q and soil water content ( θ ) explained 85% and 91% of the variance of monthly GPP, respectively; and 91% and 96% of the variance of monthly R e was explained by T a and the combination of T a and θ , respectively. Annual net C sequestration was high during optimally warm and normal precipitation years, but low in unusually warm or severely dry years. Excluding 1998 and 1999, the 2 years strongly affected by an El Niño/La Niña cycle, annual NEP significantly decreased with increasing annual mean T a . Annual NEP will likely decrease whereas both annual GPP and R e will likely increase if the future climate at the site follows a trend similar to that of the past 40 years.

Bird diversity: a predictable function of satellite‐derived estimates of seasonal variation in canopy light absorbance across the United States - Nicholas C. Coops; Richard H. Waring; Michael A. Wulder; Anna M. Pidgeon; Volker C. Radeloff (Journal of Biogeography, 2009)
Aim To investigate the relationships between bird species richness derived from the North American Breeding Bird Survey and estimates of the average, minimum, and the seasonal variation in canopy light absorbance (the fraction of absorbed photosynthetically active radiation, fPAR) derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). Location Continental USA. Methods We describe and apply a ‘dynamic habitat index’ (DHI), which incorporates three components based on monthly measures of canopy light absorbance through the year. The three components are the annual sum, the minimum, and the seasonal variation in monthly fPAR, acquired at a spatial resolution of 1 km, over a 6‐year period (2000–05). The capacity of these three DHI components to predict bird species richness across 84 defined ecoregions was assessed using regression models. Results Total bird species richness showed the highest correlation with the composite DHI [ R 2 = 0.88, P R 2 = 0.79, P R 2 = 0.74, P R 2 = 0.55–0.88) of the observed variation in bird species richness. Main conclusions The strong relationship between the DHI and observed avian biodiversity suggests that seasonal and interannual variation in remotely sensed fPAR can provide an effective tool for predicting patterns of avian species richness at regional and broader scales, across the conterminous USA.

Assessing Tower Flux Footprint Climatology and Scaling Between Remotely Sensed and Eddy Covariance Measurements - Baozhang Chen; T. Andrew Black; Nicholas C. Coops; Thomas Hilker; J. A. (Tony) Trofymow; Kai Morgenstern (Boundary-Layer Meteorology, 2009)

What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration - Sarah E. Gergel; Yulia Stange; Nicholas C. Coops; Kasper Johansen; Kathryn R. Kirby (Ecosystems, 2007)

Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR - Nicholas C. Coops; Thomas Hilker; Michael A. Wulder; Benoît St-Onge; Glenn Newnham; Anders Siggins; J. A. Trofymow (Trees, 2007)