In response to the limited number and distribution of in-situ carbon flux observations, remote sensing-based methods are increasingly relied upon for the estimation of Gross Primary Productivity (GPP) at regional to global scales. These remote sensing-informed estimates are commonly derived through process-based modelling frameworks which prescribe functional relationships between model inputs and target GPP. Across highly heterogeneous landscapes like the Canadian boreal, these parameters are difficult to constrain and often site-specific. Recent work has determined that parameterization alone may not improve model performance, instead requiring additional model inputs to capture the complex drivers of vegetation productivity across land cover types. In response to these challenges, we applied the remote sensing-based CAN-TG framework to estimate boreal GPP, leveraged through a random forest (RF) machine learning approach that does not assume linear or functional relationships between input variables and productivity. Stratified by land cover, fire disturbance history, and topography, models were assessed for their ability to capture reference GPP from NASA's complex, process-based Soil Moisture Active Passive (SMAP) GPP product. Across all boreal strata, model r2 values ranged from 0.93 to 0.96, demonstrating that the variability in substantially more complex models can be successfully captured using a simple, interpretable remote sensing-based framework. Through the addition of remote sensing variables capturing freeze/thaw and soil moisture dynamics to surface temperature and greenness, the CAN-TG model demonstrated an improved ability to capture GPP compared to a benchmark GPP model. Seasonal RF models across key boreal land cover, fire disturbance history and topographic strata further demonstrated varying and complex non-linear relationships between model variables and GPP. Spring and fall models generally outperformed winter and summer models, reaffirming model strengths whilst also highlighting remaining uncertainty and areas for future model improvement.

Quantifying the store and flux of carbon across space and time from trees to forest stands, and ultimately at a global scale, has become paramount for a broad range of applications, including individual tree based allometry, landscape scale forest carbon accounting as well as derivation of globally required climate change related variables. Despite this significant information need, the measurement of forest carbon using field methods remains laborious, expensive and logistically complex. Laser scanning technologies mounted on terrestrial, unmanned aerial vehicles or drones, aircraft or satellites have revolutionised the estimation of forest carbon at a variety of spatial and temporal scales with each providing detailed and often unique information about the distribution of biomass and carbon within a stand. In this review, we examined the use of laser scanning technologies for this purpose. To do so we focus on the recently published (within 10 years) peer reviewed literature and consider studies across four information needs, individual tree, stand, regional / national, and global scales. We consider the type of laser scanning data that is typically acquired, data processing pipelines and the products that are produced. After reviewing these studies, we conclude with a discussion of remaining issues associated with the mapping of forest carbon using laser scanning technologies. We also highlight a number of future research directions to further expand the use of this technology for forest carbon mapping globally.

The frequency of extreme heat and drought events is predicted to increase in temperate forests throughout the 21st century. The effects of these stressors on established trees with ranging height and crown conditions, and the potential trade-offs between rapid growth and vulnerability to extremes, are poorly understood – even for drought-adapted species. In this study, we use drone-mounted LiDAR and multispectral sensors to analyze spectral responses by tree size in 24-year-old coastal Douglas-fir during the summer of 2022, which experienced a heat event with temperatures reaching 38°C and a seasonal drought where soil water content (m3/m3) fell by over 65% to 0.06 m3/m3. Comparing pre-heat to heat event vegetation indices, all structural clusters show significant (α = 0.01) decreases in the photochemical reflectance index (PRI), slope of the red edge (RE slope), and normalized difference red edge index (NDRE717); small trees express the greatest drop in PRI and large trees show the greatest decrease in RE slope and NDRE717. This suggests that small trees experience a greater photoprotective response via decreases in photosynthetic activity and large trees undergo a greater response in terms of structural health. Under drought conditions, all structural clusters show significant (α = 0.01) declines in the green chromatic coordinate (GCC), chlorophyll carotenoid index (CCI), and RE slope. Declines in GCC and CCI suggest an onset of yellowing and shifts in seasonal pigment pools from chlorophylls to carotenoids, however this response is not able to be pinpointed to drought as it cannot be untangled from the natural physiological response of pigment shifts and yellowing that occur in the late summer. Large trees experience a greater decrease in RE slope between pre- and mid-drought acquisitions compared to medium and small trees; however, the RE slope of small trees is significantly (α = 0.01) lower than both medium and large trees mid-drought. This suggests that although large trees undergo a greater decrease in RE slope, they are able to maintain more photosynthetic pigments and greater structural health throughout the drought period compared to small trees. Overall, this work demonstrates the potential for multispectral phenotyping of heat response in various sizes of costal Douglas-fir.

In times of unprecedented global change, forest management education, especially in silviculture, must evolve to prepare future forest managers with relevant skills and a comprehensive view of adaptive silviculture. As a data-driven science, silviculture must now integrate multidisciplinary technical and professional expertise to shape the forests of tomorrow—by planning and assessing treatment impacts while also meeting socioeconomic needs. As a result, silviculture education, particularly in undergraduate programs, needs to advance to meet these future challenges to ensure students have the essential tools and analytical skills required to undertake holistic, field-based silviculture practice. To support this advancement, we propose a vision for silviculture education that emphasizes fundamental knowledge (e.g., forest ecology, mensuration, governance), while also adapting to evolving concepts driven by socioeconomic factors, new silvicultural systems, a focus on ecosystem services, and the availability of new geospatial technologies. This new vision calls for strong leadership in experiential education to foster active learning through innovative tools, work-integrated experiences, and interdisciplinary collaboration. Such a curriculum will equip students with the skills required to integrate knowledge and adapt holistically, preparing them to meet the evolving demands of modern silviculture.

Climate change poses a significant global threat, requiring rapid and effective mitigation strategies to limit future warming. Tree planting is a commonly proposed and readily implementable natural climate solution. It is also a vital component of habitat restoration for the threatened woodland caribou ( Rangifer tarandus) . There is potential for the goals of caribou conservation and carbon sequestration to be combined for co-benefits. We examine this opportunity by estimating the carbon sequestration impacts of tree planting in woodland caribou range in British Columbia (BC), Canada. To do so, we couple Landsat-derived datasets with Physiological Processes Predicting Growth, a process-based model of forest growth. We compare the sequestration impacts of planting informed by woodland caribou habitat needs to planting for maximum carbon sequestration under multiple future climate scenarios including shared socio‐economic pathways (SSP) 2, representing ∼2.7 °C warming, and SSP5, representing ∼4.4 °C warming. Trees were modelled as planted in 2025. Province-wide by 2100, planting for maximum-carbon sequestration averaged 1062 Mg CO _2 · ha ^−1 planted, while planting for caribou habitat resulted in an average of 930 Mg CO _2 · ha ^−1 planted, a reduction of 12%. We found that relative sequestration between herds remained similar across warming scenarios and that, for most ecotypes, sequestration increased from 5% to 7% between the coldest (∼2.7 °C warming) and warmest (∼4.4 °C warming) scenario. Variability in the relative sequestration impacts of planting strategies was observed between herds, highlighting the importance of spatially-explicit, herd-level analysis of future forest growth when planning restoration activities. Our findings indicate a large potential for co-benefits between carbon sequestration and woodland caribou habitat restoration across BC in all warming scenarios modelled. They also underscore the value of process-based forest growth models in evaluating the carbon implications of tree planting and habitat restoration across large areas under a changing climate.

The Spectral Variation Hypothesis (SVH) posits that higher spectral diversity indicates higher biodiversity, which would allow imaging spectroscopy to be used in biodiversity assessment and monitoring. However, its applicability varies due to ecological and methodological factors. Key methodological factors impacting spectral diversity metrics include spatial resolution, shadow removal, and spectral transformations. This study investigates how these methodological considerations affect the application of the SVH across ecosystems and sites. Using field and hyperspectral data from forest and open (e.g., wetland, grassland, savannah) ecosystems from five sites of the Canadian Airborne Biodiversity Observatory (CABO), we analyzed three variance-based spectral diversity metrics across and within vegetation sites, examining the effects of illumination corrections, spatial resolution, and shadow filtering on the spectral-plant functional diversity relationship. Our findings highlight that the relationship between spectral diversity metrics and functional diversity are strongly influenced by methods, especially spectral transformations. These illumination corrections notably impacted the spectral regions of importance and the resulting relationships to plant functional diversity. Depending on methodological choices, we observed correlations that varied not only in strength but also direction: in open vegetation we saw negative correlations when using brightness normalization, and positive correlations when using continuum removal. Shadow removal and spatial resolution were important but had less impact on the correlations. By systematically analyzing these methodological aspects, our study not only aims to guide researchers through potential challenges in SVH studies but also highlights the inherent sensitivity of spectral-functional diversity relationships to methodological choices. The variability and context-dependence of these relationships across and within sites emphasize the need for adaptable, site-specific approaches, presenting a key challenge in developing robust methods to enhance biodiversity monitoring and conservation strategies.

Non-stand replacing disturbances (NSRs) are events that do not result in complete removal of trees and generally occur at a low intensity over an extended period of time (e.g., insect infestation), or at spatially variable intensities over short time intervals (e.g., windthrow). These disturbances alter the quality and quantity of forest biomass, impacting timber supply and ecosystem services, making them critical to monitor over space and time. The increased accessibility of high frequency revisit, moderate spatial resolution satellite imagery, has led to a subsequent increase in algorithms designed to detect sub-annual change in forested landscapes across broad spatial scales. One such algorithm, the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) has shown promise with sub-annual change detection in temperate forested environments. Here, we evaluate the sensitivity of BEAST to detect NSRs across a range of severity levels and disturbance agents in Central British Columbia (BC), Canada. Moderate resolution satellite time series data were utilized by BEAST to produce rasters of change probability, which were compared to the occurrence, severity, and timing of disturbances as mapped by the annual British Columbia Aerial Overview Survey (BC AOS). Differences in the distributions of BEAST probabilities between agents and levels of severity were then compared to undisturbed pixels. In order to determine the applicability of the algorithm for updating forest inventories, BEAST probability distributions of major NSRs (> 5 % of total AOS disturbed area) were compared between consecutive years of disturbances. Cumulatively, all levels of disturbances had higher and statistically significant (p < 0.05) mean BEAST change probabilities compared with historically undisturbed areas. Additionally, 16 disturbance agents observed in the area had higher statistically significant (p < 0.05) probabilities. All major NSRs showed an upwards and statistically significant (p < 0.05) progression of BEAST probabilities over time corresponding to increases in BC AOS mapped area. The sensitivity of BEAST change probabilities to a wide range of NSR disturbance agents at varying intensities suggests promising opportunities for earlier detection of NSRs to inform continuously updating forest inventories and potentially inform adaptation and mitigation actions.

Biodiversity science requires effective tools to predict patterns of species diversity at multiple temporal and spatial scales. The Dynamic Habitat Indices (DHIs) are remotely sensed indices that summarize aboveground vegetation productivity in a way that is ecologically relevant for biodiversity assessments. Existing global DHIs, derived from MODIS at 1-km resolution, predict species richness at broad scales well, but that resolution is coarse relative to the grain at which many species perceive their habitat. With the much finer spatial resolution of Sentinel-2 and Landsat data, plus Landsat's longer data record, it is possible to track potential changes of vegetation and its impacts on biodiversity at a finer grain over longer periods. Here, our main goals were to derive the DHIs from 10-m Sentinel-2, 30-m Landsat, and 250-m MODIS data for the conterminous US and compare all DHIs at two spatial extents, and to evaluate the ability of these DHIs to predict bird species richness in 25 National Ecological Observatory Network terrestrial sites. In addition, we derived the Landsat DHIs for 1991-2000 and investigated how they changed by 2011-2020. We found that the Sentinel-2, Landsat, and MODIS DHIs were highly correlated when summarized by ecoregion (Spearman correlation ranging from 0.89 to 0.99), indicating good agreement between them and that we were able to overcome the lower temporal resolution of Sentinel-2 and Landsat. Sentinel-2 and Landsat DHIs outperformed MODIS in modeling species richness for all bird guilds, explaining up to 49% of variance of grassland affiliates in linear regression models. Furthermore medium-resolution DHIs (10-30 m resolution) captured spatial heterogeneity much better than MODIS DHIs. We observed considerable changes in Landsat DHIs from 1991-2000 to 2011-2020, such as increased cumulative DHI along the West Coast, in mountain ranges, and in the South, but lower cumulative DHI in the Midwest. Our newly derived DHIs for the conterminous US have great potential for use in biodiversity science and conservation.

Forests with high ecological integrity are fundamental for biodiversity conservation and provide integral ecosystem services. These forests have natural or near-natural ecosystem structure, function, and composition. Anthropogenic pressures such as habitat loss, overexploitation of natural resources, and land use changes are leading to the degradation or loss of high-integrity forests. As a result, assessing forest integrity over large areas is increasingly important for a range of conservation initiatives. In this study, we used remote sensing-derived forest structural and functioning metrics alongside a high-quality reference state to calculate ecological dissimilarity as a proxy for ecological integrity. We examined stand-level integrity and focused on forest structural attributes such as canopy height, cover, complexity, and biomass, as well as the Dynamic Habitat Indices, which summarize annual energy availability relevant for biodiversity. We further refined our reference states by using coarsened exact matching to ensure our comparisons were drawn from suitable protected analogs. We applied these methods to Vancouver Island, Canada, where we assessed the distance, in structural and functional space, to matched high-integrity forests found in the island’s oldest and largest protected area. We also assessed how individual and cumulative anthropogenic pressure affect the ecological integrity of forests on the island. We found that mean forest structural dissimilarity increased from 0.79 to 1.61 under high levels of anthropogenic pressure (ANOVA; p < 0.001), while functional dissimilarity was not impacted by any anthropogenic pressure (ANOVA; p > 0.05). This indicates that anthropogenic pressures were observed to directly influence forest canopy characteristics, and less so energy availability. For individual pressures, we found that built environments, harvesting, and population density influenced structural dissimilarity (ANOVA; p < 0.05), while roads did not influence structural dissimilarity (ANOVA; p > 0.05). These methods for identifying high-integrity forests can be used to identify areas to be prioritized for protection or restoration, which in turn progresses towards the Kunming-Montreal Global Biodiversity Framework’s goal of 30 % of all ecosystems protected, while focusing on high-integrity ecosystems.

Estuarine mudflats are colonized by biofilm-forming microphytobenthos (MPB), which support primary production, stabilize sediment, and provide critical food for benthic invertebrates and shorebirds. MPB biomass fluctuates intra-daily, peaking post-emersion and declining before tidal immersion. Understanding these dynamics requires high-resolution monitoring, yet traditional methods, including sediment sampling and satellite imagery, lack the necessary spatial and temporal precision. We used unoccupied aerial vehicles (UAVs) to map MPB distribution, biomass, and mudflat morphology at shorebird-relevant scales. Hourly multispectral surveys over two 12-hour tidal emersion cycles at the Fraser River Estuary, British Columbia-an internationally significant shorebird stopover-captured diel MPB dynamics. Optical imagery was processed using a photogrammetric co-alignment approach to generate continuous chl-a maps (via the normalized vegetation index), digital surface models, and topographic position index layers. UAV-derived data were integrated with climate variables to model MPB variability and quantify diel biofilm patch dynamics. A modified Z-score normalization of pseudo-invariant features stabilized reflectance data, allowing fine-scale analysis of MPB distribution. Our diel model explained 31.6 % of MPB variation, with biomass peaking seven hours post-emersion, consistent with vertical migration of microalgae. Mudflat morphology significantly influenced MPB biomass, and spatial metrics revealed interactions between MPB dynamics, microtopography and shorebird foraging ecology. The study demonstrates the efficacy of high-temporal-resolution UAV imagery for monitoring MPB and mudflat morphology, enabling detailed examination of MPB diel vertical migration in response to emersion timing and light availability. Such new insights into estuarine ecology provide a framework for advancing conservation strategies and habitat management in intertidal environments.

Estimating forest aboveground biomass (AGB) and its components (wood, branch, bark, foliage) is critical for forest inventories and provides important information for timber harvesting and carbon accounting. Current approaches for modelling forest AGB at the stand scale often employ airborne laser scanning (ALS) data which provide robust AGB estimates. However, in structurally complex forest ecosystems, ALS-based models may not estimate forest biomass and its components with sufficient accuracy. One method to improve ALS-based model performance is through data fusion. Deep neural networks (DNNs) are effective for data fusion because they can combine different data modalities without the need to modify the original data resolution. This study evaluated the effectiveness of a data fusion DNN that combines ALS, multispectral, and topographic data for forest biomass estimation (total and component). We implemented a DNN architecture consisting of three convolutional neural network (CNN) modules: Octree-CNN for ALS data; 1-D CNN for Landsat-8 multispectral data; and 2-D CNN for topographic data. Variants of the DNN architecture combining different input data modalities were trained and tested using sample plots from New Brunswick, Canada (n = 2,336). The model, including all three data modalities, performed best overall for total AGB estimation (R2 = 0.77; RMSE = 28.38 Mg/ha) and explained an additional 2-5% variation in wood, bark, and foliage biomass compared to the ALS-only model. This study demonstrates the effectiveness of a novel data fusion DNN architecture that extracts information directly from input data modalities for improving forest biomass estimates. However, relatively small performance gains should be weighed against computational resources and domain knowledge required to implement and interpret DNNs.

Bamboo forests are natural habitat for the giant panda which is one of the most vulnerable mammal species. In structurally complex natural forests, bamboos are normally located under the canopy of taller trees, which makes them difficult to be quantified accurately. Although Light Detection and Ranging (LiDAR) technologies have been well established as the effective tool for forest structure assessment, the use of LiDAR to assess understory bamboo in structurally complex natural forests is less well known. We present a novel vertical vegetation classification (VVC) approach to map the structure of understory bamboos for giant panda forage in natural forests. An optimized demarcation point identification (DPI) model was developed for stratifying different vertical layers from coarse to fine scales. Three-dimensional understory bamboo point clouds were successfully isolated from the forest point cloud, then bamboo structure predictive models were developed through understory bamboo point cloud metrics and applied over the entire study area to generate spatially continuous maps of understory bamboo structure. Our results indicate that the isolation of the understory bamboo point cloud using the developed VVC approach performs well and has small bias, the extracted maximum height is close to fieldmeasured maximum height (R2 = 0.77, rRMSE = 15.02 %). Height-related metrics have higher correlations with bamboo structure (mean natural and true height, basal diameter, and total aboveground biomass) than other metrics (r > 0.8), and understory bamboo structures are estimated with relatively high accuracy (R2 = 0.84 - 0.91, rRMSE = 10.87 - 29.41 %). We also find varying effects of topography on the spatial distribution of different understory bamboo species. This study demonstrates the benefits of utilizing LiDAR data to ascertain fine-scale understory bamboo resources, providing critical supports for giant panda habitat assessment and conservation.

Abstract Proximally sensed laser scanning presents new opportunities for automated forest ecosystem data capture. However, a gap remains in deriving ecologically pertinent information, such as tree species, without additional ground data. Artificial intelligence approaches, particularly deep learning (DL), have shown promise towards automation. Progress has been limited by the lack of large, diverse, and, most importantly, openly available labelled single‐tree point cloud datasets. This has hindered both (1) the robustness of the DL models across varying data types (platforms and sensors) and (2) the ability to effectively track progress, thereby slowing the convergence towards best practice for species classification. To address the above limitations, we compiled the FOR‐species20K benchmark dataset, consisting of individual tree point clouds captured using proximally sensed laser scanning data from terrestrial (TLS), mobile (MLS) and drone laser scanning (ULS). Compiled collaboratively, the dataset includes data collected in forests mainly across Europe, covering Mediterranean, temperate and boreal biogeographic regions. It includes scattered tree data from other continents, totaling over 20,000 trees of 33 species and covering a wide range of tree sizes and forms. Alongside the release of FOR‐species20K, we benchmarked seven leading DL models for individual tree species classification, including both point cloud (PointNet++, MinkNet, MLP‐Mixer, DGCNNs) and multi‐view 2D‐based methods (SimpleView, DetailView, YOLOv5). 2D Image‐based models had, on average, higher overall accuracy (0.77) than 3D point cloud‐based models (0.72). Notably, the performance was consistently >0.8 across scanning platforms and sensors, offering versatility in deployment. The top‐scoring model, DetailView, demonstrated robustness to training data imbalances and effectively generalized across tree sizes. The FOR‐species20K dataset represents an important asset for developing and benchmarking DL models for individual tree species classification using proximally sensed laser scanning data. As such, it serves as a crucial foundation for future efforts to classify accurately and map tree species at various scales using laser scanning technology, as it provides the complete code base, dataset, and an initial baseline representative of the current state‐of‐the‐art of point cloud tree species classification methods.

The recent mountain pine beetle (Dendroctonus ponderosae) outbreak has resulted in widespread mortality of pine trees across western Canada over the past two decades. The changes to forest structure caused by the beetle are well known through ground-based observations. However, the potential changes to fuels for wildfires associated with altered forest structure are not well known nor incorporated into fire fuel models. In this study, we used light detection and ranging (LiDAR) to quantify variations in forest structure and wildfire fuels caused by mountain pine beetle (MPB) infestation. From this data, we created models that characterize fuels following MPB attack. LiDAR metrics were extracted from three-dimensional point clouds acquired using remotely piloted aircraft systems (RPAS) and mobile laser scanning (MLS), both individually and combined. Fuel components in the stand were then modeled across a range of MPB attack severities. Results indicated the fused model was most accurate at predicting canopy fuel load (R2 = 0.80), while MLS had the best model performance for shrub fuel load (R2 = 0.68) and coarse woody debris fuel load (R2 = 0.68). This study demonstrates the ability of LiDAR to accurately characterize forest fuel loads in MPB-infested forests.

Management of forest genetics is shifting from a paradigm focused on increasing timber volume to a prioritization of climate adaptation. Functional traits related to foliar structure, photosynthetic and photoprotective pigments, and stress underlie climate adaptation and have spectral signatures that can be quantified with remote sensing. Common-garden trials present an opportunity to assess the genetic basis of multispectral reflectance dynamics across genotypes. We analyzed multitemporal drone remote sensing of 1350 individual trees from 88 populations from diverse geographic and climatic provenances in a provenance trial of interior spruce (Picea engelmannii, P. glauca, and their hybrids) to assess patterns of genetic differentiation, local adaptation to climate, and hybridization from multispectral reflectance. We quantified early-summer, mid-summer, late-summer, and late-winter multispectral vegetation indices for each population and derived variables describing changes in these indices during winter-to-summer photosynthetic green-up and early-to-late-summer decline. Spectral traits revealed moderate population differentiation (V-pop = 14.4 % - 39.9 %) and significant (P < .005) patterns of local adaptation to provenance warmest-month temperature and elevation. Derived green-up and decline indices revealed additional relationships for coldest-month temperature, date of first frost, precipitation-as-snow, and climatic moisture deficit. Principal components described leaf area greenness, the magnitude of green-up, and seasonal decline in the red edge. Hierarchical clustering of these principal components identified eight geographically and climatically distinct clusters which captured major patterns in hybridization. Seasonal dynamics of vegetation indices, assessed with multitemporal drone remote sensing, can identify important patterns in hybridization and adaptation to climate which are not evident from spectral reflectance assessed at one time of year. These dynamic spectral traits have the potential to quantify the functional basis of local adaptation in common-garden trials and facilitate the selection of resilient genotypes for future climates.

Predictions of individual tree crown growth provide key insights into future crown size and condition, and are important for sustainable forest management. This study presents a novel approach for forecasting three-dimensional (3D) crown growth using crown structural metrics derived from multi-temporal airborne lidar (ALS) at two-time intervals. Model development consisted of segmenting tree crowns from ALS point clouds, and producing convex hulls with matching vertex datums created for each crown pair (n = 110). A machine learning approach was then used to model the vertex shift (triangle-xyz) between time-points, estimating growth for 33 independent crowns. Predictions of crown height (H), volume (V), and area (A(2D)) showed good to strong correlations (H R-2 = 0.97, V R-2 = 0.62, A(2D) R-2 = 0.6). All metrics showed negative bias, with V and A(2D) to a greater extent than H, aligning with triangle-z being 5.6 times greater than triangle-xy. Future iterations of this model should be investigated at plot scale, incorporating model variables such as surrounding crown structure and competition.

Vegetation inventories characterizing potential fuels represents critical information underpinning wildfire management and emergency response planning. Available fuels can be characterized in terms of burn probability, which describes the degree to which a set of biotic and abiotic conditions corresponds to known or simulated burned areas. Changes in future climate are expected to result in corresponding shifts in burn probability. In this study, existing burn probability models based on climate, vegetation, and topographic conditions were used as inputs with variables from four future climate scenarios to examine the spatiotemporal distribution of burn probability in the 21st century. Changes were calculated and analyzed for all pixels in forest-dominated ecozones in Canada and 160 forest-adjacent communities by comparing future projections to contemporary values of burn probability. By 2100, overall median projected burn probability increased by 17% across scenarios, ranging from 4 - 60% across individual ecozones. Burn probability likewise increased for the majority of forest-adjacent communities, although the magnitude of the increase was highly variable. The results of this study show the spatiotemporal distribution of changes in burn probability under future climate scenarios and provide valuable information for those interested in implementing mitigation techniques (e.g., prescribed burning, thinning, creation of defensible spaces or firebreaks) to reduce the impacts of future fires. Les inventaires forestiers caract & eacute;risant les combustibles potentiels constituent une information essentielle & agrave; la gestion des feux de for & ecirc;t et & agrave; la planification des interventions d'urgence. Les combustibles pr & eacute;sents peuvent & ecirc;tre caract & eacute;ris & eacute;s par la probabilit & eacute; de br & ucirc;lage, qui d & eacute;crit le degr & eacute; auquel un ensemble de conditions biotiques et abiotiques correspond & agrave; des zones br & ucirc;l & eacute;es connues ou simul & eacute;es. Les changements climatiques futurs devraient modifier la probabilit & eacute; de br & ucirc;lage. Dans cette & eacute;tude, des mod & egrave;les de probabilit & eacute; de br & ucirc;lage existants bas & eacute;s sur le climat, la v & eacute;g & eacute;tation et les conditions topographiques, ont & eacute;t & eacute; utilis & eacute;s comme intrants avec des variables issues de quatre sc & eacute;narios climatiques futurs, afin d'examiner la distribution spatiotemporelle de la probabilit & eacute; de br & ucirc;lage au 21e si & egrave;cle. Les changements ont & eacute;t & eacute; calcul & eacute;s et analys & eacute;s pour tous les pixels des & eacute;cozones o & ugrave; la for & ecirc;t est dominante au Canada et pour 160 communaut & eacute;s & agrave; proximit & eacute; de la for & ecirc;t, en comparant les projections aux valeurs actuelles de probabilit & eacute;s de br & ucirc;lage. D'ici 2100, la probabilit & eacute; m & eacute;diane globale projet & eacute;e de br & ucirc;lage augmentera de 17 % selon le sc & eacute;nario, passant & agrave; une augmentation de 4 & agrave; 60 % selon l'& eacute;cozone. La probabilit & eacute; de br & ucirc;lage augmentera & eacute;galement pour la majorit & eacute; des communaut & eacute;s & agrave; proximit & eacute; de la for & ecirc;t, bien que l'ampleur de l'augmentation soit tr & egrave;s variable. Les r & eacute;sultats de cette & eacute;tude montrent la distribution spatiotemporelle des changements de la probabilit & eacute; de br & ucirc;lage dans le cadre de sc & eacute;narios climatiques futurs et fournissent des informations pratiques pour ceux qui souhaitent mettre en place des techniques d'att & eacute;nuation (par exemple pour du br & ucirc;lage dirig & eacute;, des & eacute;claircies foresti & egrave;res, la mise en place d'espaces d'intervention ou de pare-feu) pour r & eacute;duire les impacts des futurs feux.

Multispectral sensors mounted to unoccupied aerial vehicles (UAVs) can be leveraged to quantify microphytobenthos (MPB) biomass in intertidal mudflats, providing data products with cm-scale pixels. However, no standard protocol currently exists for calibrating UAV-acquired spectral information to sediment MPB content. Here, we present a new protocol for calibrating data from a UAV-mounted multispectral sensor to sediment MPB biomass as measured by photopigment content. To do so, we developed a methodology for acquiring and analyzing UAV imagery and sediment photopigment field data. We then implemented the protocol in the Fraser River Estuary, Canada to build a statistically valid calibration equation, testing the effectiveness of several spectral indices and photopigment measurements. Calibrated spectral index values can provide a very accurate measurement of MPB biomass, able to achieve 90 % correlation between the normalized difference vegetation index (NDVI) and sediment chlorophyl-a (chl-a) concentration. This high performance was achieved by closely pairing georeferenced sediment samples to corresponding multispectral imagery and minimizing the lag between sediment sample collection and UAV imagery acquisition. This protocol can facilitate the use of calibrated UAV-acquired multispectral imagery for investigating ecologically-important fine-scale spatial heterogeneity of MPB biomass.

Climate change is altering northern vegetation structure and below-ground carbon storage. Expanding forest and shrub cover has decreased soil organic carbon (SOC) storage in some parts of the forest-tundra ecotone. In this study, we linked measurements of SOC with terrain and vegetation structure derived from drone imagery across treelines underlain by continuous permafrost in the Northwest Territories, Canada. We classified sites into three treeline types representing differences in vegetation productivity and topography. Between treeline types, we observed differences in C:N ratios and organic matter depth related to the rate of soil carbon turnover and SOC storage. Overall, SOC showed small positive relationships with tree stem density and average canopy height. We did not find evidence that expanding tree- and shrublines would result in losses of SOC storage in our study area. Instead, topography and landscape drainage patterns, rather than vegetation structure may be more important predictors of SOC storage. We used medium resolution satellite data to extend predictions of treeline type across our study area. The majority of predicted treelines (82%) showed positive relationships between vegetation height and SOC storage. Our findings highlight the value of integrating vegetation structure and landscape features in understanding carbon dynamics in the forest-tundra ecotone.

The integration of airborne laser scanning (ALS) technology into forest inventory practices has significantly improved forest management by providing accurate predictions of forest structural attributes. However, ALS offers limited insight into the spectral properties of tree crowns, hindering the accurate prediction of various physiological attributes and the identification of tree species. The fusion of multitemporal spectral information with ALS data has been proposed as an important step towards addressing this limitation. While previous studies have explored combining ALS with optical data for forest species mapping, the fusion process often requires feature generation and selection, which restrict the scalability and effectiveness of these approaches. There remains a need for an approach that effectively leverages both the structural information of ALS and spectral dynamics of optical imagery in a fully data-driven manner. We propose a novel dual-stream deep learning approach that fuses ALS point-cloud data with multitemporal Sentinel-2 (S2) imagery to predict the proportions of seven species and two genera across a 630,000 ha Canadian boreal forest, capturing both structural and spectral features within 20 m grid cells. The results showed an R2 of 0.58 and an RMSE of 0.14 for all proportional values, with an 8% increase in accuracy for the detection of broadleaf species when using seasonal multispectral images, compared to using ALS data alone. Additionally, lower R2 values (0.49-0.57) were observed only when the S2 imagery was used. When identifying the leading species from the model predictions, a weighted F1 score of 0.62 and an overall accuracy of 0.65 were achieved for the seven species and two genera. This research highlights the potential of deep learning and data fusion to advance forest inventory practices by offering a scalable and reproducible method for detailed mapping of species proportions.