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

Forests provide essential ecosystem services, including carbon storage, water regulation, and habitat provision. Due to these important functions, forests are consequently subject to extensive monitoring and sustainable management efforts, which require consistent reporting across management units and jurisdictions to ultimately balance conservation and resource use. Stand-replacing disturbances, such as harvesting and wildfire, significantly alter forest landscapes by removing vegetation and reshaping their spatial and structural patterns. Following disturbance, forest development can be characterized using indicators that capture different facets of its composition (extent), configuration (spatial pattern and fragmentation), and structure (biomass and canopy properties). Using annual, wall-to-wall, satellite-derived data from 1984 to 2022, we tracked the post-disturbance dynamics of forest composition, configuration, and structure across Canada’s forested ecosystems. Our results showed that during our nearly four-decade study period, forests generally returned to pre-disturbance conditions largely resembling baseline conditions. Forest composition and configuration returned more rapidly to baseline, while structural metrics, such as aboveground biomass and canopy cover density, recovered more slowly, with rates strongly influenced by disturbance type and geographic location. Thirty years after harvesting, managed forests reached 95% of baseline aboveground biomass, whereas burned areas reached 42% of baseline. These contrasting outcomes reflect the diversity of growing conditions across Canada’s forests, where variability in productivity, seasonality, and other factors influence the time required for forest vertical structural development. In managed forests, post-harvest conditions closely matched pre-disturbance composition and structure, and configuration metrics showed no significant differences after three decades. In contrast, wildfire-dominated northern forests did not consistently reach baseline levels within the period covered but were trending toward them by the end of our analysis period. We provide a spatially explicit assessment of post-disturbance forest dynamics considering multiple metrics across Canada using multi-decadal, satellite-based observations and a landscape-scale analysis framework. This approach supports sustainability assessments and provides a basis for multifaceted monitoring of forest conditions in response to natural and anthropogenic disturbances.

Canada's terrestrial ecosystems are critical to the global carbon cycle and are responding to unprecedented climate change and wildfire disturbance. However, our understanding of Canada's historical (~1920-present) carbon cycle is incomplete. There are also no published physically coherent (i.e., those that respect conservation laws) wall-to-wall estimates of all major carbon pools and fluxes for Canada. Existing assessments vary in spatial scale and methodology, yielding notable differences in the magnitude of Canada's land carbon sink. Moreover, inversions and data-driven estimates do not disentangle the relative influence of disturbance, CO2 fertilization, or climate change on Canada's carbon cycle. Here, we synthesize information from the site to Canada-wide scale with a land surface model and the most comprehensive wildfire and wood harvest estimates available to provide the first physically coherent wall-to-wall estimates of all major carbon pools and fluxes for Canada. Using factorial model runs, we show that Canada's terrestrial ecosystems have been a carbon sink since the mid-20th-century, due to wildfire and timber harvest before 1940. Since the early 2000s, wildfire disturbance has been driving Canadian forests towards becoming a carbon source. Continued increases in wildfire activity will further weaken, and may ultimately reverse, Canada's role as a carbon sink.

Satellite data from Landsat and Sentinel-2 are widely used to model forest structural attributes at the pixel level, supporting applications in forest management, habitat assessment, carbon cycle analysis, and climate impact mitigation. However, many management and modeling frameworks rely on stand-level forest inventories derived from ocular interpretation of aerial imagery. To address this information need, a Satellite-Based Forest Inventory (SBFI) was developed for Canada’s forested ecosystems (∼650 Mha), providing spatially exhaustive and nationally consistent stand-level attributes derived from a multi-decadal series of satellite data. A key SBFI attribute is total treed aboveground biomass (AGB). We present a new dataset that further partitions this total SBFI AGB into species-specific biomass components for stemwood, bark, branches, and foliage, and also includes estimates of AGB for non-treed vegetation pools. This level of detail supports applications requiring biomass accounting beyond total AGB, including carbon reporting, productivity assessment, wildfire fuel characterization, and modeling approaches that depend on biomass components. We outline the development, modeling framework, and structure of this expanded biomass component database. This remote-sensing-informed, stand-level product delivers national coverage of species-specific component AGB across more than 25 million forest stands and the dataset is openly available to support analyses from local to national scales.

Satellite time series enable the consistent and scalable characterization of land cover changes and vegetation dynamics across extensive areas. The Composite2Change (C2C) protocol applies temporal spectral trend analysis to identify land change events, producing metrics that describe the timing, magnitude, duration, rate, direction, and recovery dynamics of disturbances. These metrics summarize spectral trajectories and support mapping and modelling applications. While the methods have been published and their value demonstrated, their replication across regions and sensors has been limited due to high storage and computational demands, coupled with the lack of available implementation. To address these barriers, we implemented C2C as a reproducible cloud-native framework within Google Earth Engine (C2C-GEE). C2C-GEE enables users to generate change products and metrics that describe the spectral and temporal signatures of land cover change and recovery. The framework supports the transparent and systematic generation of standardized change metrics for large-area mapping and modelling applications worldwide.

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.

Forest inventory practices in Canada have evolved over time with changes in forest management priorities, advances in technology, fluctuations in the marketplace, societal expectations, and generational shifts in the workforce. Provincial and territorial governments in Canada are vested with forest management responsibilities and each jurisdiction has adopted forest inventory approaches that reflect jurisdictional information needs and contexts. Typically, these inventories are strategic in nature and spatially explicit, providing stand-level forest attribute information derived from a two-phase approach involving manual air photo interpretation and stratified ground plot sampling. Airborne laser scanning (ALS; also known as light detection and ranging or lidar) has emerged as a transformative data source for forest inventories and is now considered operational, with the resulting outputs commonly referred to as enhanced forest inventories (EFI). Herein we review and synthesize how EFIs are influencing forest inventory practice in Canada. We characterize the spatial coverage and characteristics of ALS data acquired for forest inventory purposes, summarize the current status of EFI implementation within Canada’s provinces and territories, identify emerging trends associated with these EFIs, and consider these EFIs in the broader global context. We also highlight common research gaps towards the development of a nationally and globally relevant research agenda to support the greater integration of remotely sensed data into forest inventory programs in Canada and beyond.

In Canada, satellite-derived data are available as National Terrestrial Ecosystem Monitoring System (NTEMS) products, which can be used as auxiliary information to improve sample-based National Forest Inventory (NFI) estimates. This study explored statistical approaches of using these satellite-derived data to improve vegetated tree (VT) cover estimate in the Atlantic Maritime Ecozone. First, a model-assisted regression estimator with beta regression (MA beta ) was evaluated using simulated population datasets. Then, the efficiency of the MA beta estimator was compared to a design-based ratio estimator (DB) in two scenarios: one with temporally matched survey and auxiliary data, and another with temporally unmatched survey and auxiliary data. The assisting model was also used to generate model-based bootstrap aggregate annual estimates (Mb BAE ) of VT cover proportion to explore temporal trends and assess sensitivity to disturbances during 2007–2017. Results showed that the MA beta estimator provided nearly unbiased estimates with a coverage rate of about 95% (when n ≥ 100). The MA beta estimates were more precise than the DB estimates, especially when survey and auxiliary data were temporally matched (RE = 3.04 with g-weights, RE = 3.25 without g-weights). Moreover, the MB BAE -based annual estimates of VT cover proportion indicated that stand-replacing disturbances drive VT cover dynamics in the ecozone. The results suggest that incorporating remote sensing based, independently derived, wall-to-wall data products can improve the accuracy and precision of Canada's NFI, aiding in monitoring forest resources at various scales.

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.

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.

Spatially explicit fire and harvest data are useful for driving land surface model (LSM) simulations of the carbon cycle. From 1985-present, numerous Canadian disturbance datasets exist. However, before the launch of Landsat-4 (1984), few are available. We create spatially explicit LSM disturbance drivers for Canada for 1740-2018. We catalog and harmonize spatial and aspatial datasets and develop a novel algorithm that reconstructs disturbance far back in time using stand age. Based on possible historical scenarios, we reconstruct 283-394 Mha of fire and 3.42 Mha of harvest in total Canada-wide from 1740-1918. After 1918, when spatial records are available, we supplement them by reconstructing 25.79-60.30 Mha of fire and 24.75 Mha of harvest. After 1984, we exclusively use spatially explicit records. We verify the algorithm by comparing the inputs and resultant drivers and examine diagnostic metrics to disentangle the contribution of spatial, aspatial, and stand-age data. The resulting drivers primarily capture stand-replacing disturbance on forested land. Our forcings and algorithm will improve the representation of disturbance-mediated impacts on Canada's terrestrial carbon cycle and possibly in other regions.

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

The area burned by wildfires in Canada in 2023 is unprecedented in historical records. To help ensure the safety of communities and support the mobilization of firefighting resources, rapid detection of areas affected by wildfires is required. Satellite data are ideally suited to provide near real-time wildfire information over large areas. At the same time, clouds, smoke, and haze can obscure the collection of observations from sensors typically used for mapping purposes. Established methods using coarse spatial resolution satellites (e.g., MODIS, VIIRS) rely upon the combination of daily revisit to enable the rapid and reliable detection of large active fires, in full or in part, and the application of modeling (including spatial buffering) to infer additional, yet still obscured, areas. While timely, these initial maps of wildfire-impacted areas do not capture small fires (those smaller than 200 ha) and, importantly, are not intended to differentiate unburned areas within fire perimeters. To address these limitations, we used data from Sentinel-2A and -2B, and Landsat-8 and -9, which form a virtual constellation of four satellites to revisit and map burned area in Canada's forested ecosystems for the 2023 fire season. Availing upon the high temporal data density and using the Tracking Intra- and Inter-year Change algorithm (TIIC), an aggregate seasonal mapping of wildfires resulted in a total area affected by wildfires in 2023 of 12.74 Mha. Within this total area, 9.51 Mha of treed land cover was impacted. Shrubs and wetlands comprised most of the remaining non-treed area that was burned. Using a 2022 map of aboveground treed biomass (AGB), approximately 0.649 Pg of AGB was impacted by 2023 wildfires, representing an 11-fold increase in AGB impacts relative to a long-term annual average of treed AGB loss. Differences between the estimate of total burned area reported herein and the total burned area indicated by the Natural Resources Canada (NRCan) Fire M3 hotspot fire perimeters (18.64 Mha) were analyzed. Overall, estimates of burned area differed by 5.9 Mha, including over 1.13 Mha of water identified as burned within the NRCan perimeters. Differences in land cover and AGB impacts between the two products were also investigated and quantified. TIIC enables the near-continuous capture of areas impacted by fire through the fire season, allowing for within-year refinement of total burned area, rapid interrogation of land cover types impacted, and estimation of associated biomass consequences.

Mapping tree species and changes in species distribution over time enables monitoring of successional dynamics and linking of realized species distributions to regional, climatic, and disturbance processes. Such knowledge is valuable for informing forest management activities regarding the role of climate change and various types of forest disturbances on tree species distributions and successional processes. In this study, we used time-series Landsat imagery to produce annual maps of dominant tree species (n = 37 tree species classes) from 1984 to 2022 at a 30-m spatial resolution for the 650 Mha of Canada's forested ecosystems. The classification approach was based on spectral, geographic, climatic, and topographic descriptive metrics and was independently calibrated and validated with data from Canada's National Forest Inventory. Preliminary results were informed by disturbance events and post-processed to ensure consistency of the annual species maps. Assessment of the resulting annual species maps using independent validation data resulted in an overall accuracy of 86.1 % +/- 0.14 % (95 %-confidence interval). Over the study period, Canada's treed area increased from 335 to 359 Mha. The area dominated by conifers increased in absolute terms but decreased in relative terms (from 86 % to 84.9 %), especially in western ecozones where wildfire is the main agent of disturbance. The area dominated by black spruce (Picea mariana), the most common tree species in Canada, remained stable, but its relative area decreased by 4.6 %. The area dominated by balsam fir (Abies balsamea, 1.5 %), trembling aspen (Populus tremuloides, 0.9 %), jack pine (0.5 %), and sugar maple (Acer saccharum, 0.4 %) increased. In burned areas, the prevalence of black spruce (74.7 %) and jack pine (Pinus banksiana, 14.6 %) was greater than their prevalence in Canada's forested ecosystems overall (58.7 % and 3.8 %, respectively), while trembling aspen (2.4 % vs. 10 %) and subalpine fir (1.1 % vs. 4.6 %; Abies lasiocarpa) were underrepresented in burned areas. The area dominated by black spruce was underrepresented in harvested areas (46.8 % vs. 58.7 %) whereas balsam fir (6.9 % vs. 3 %), Engelmann spruce (5.5 % vs. 2 %; Picea engelmannii), red spruce (1.8 % vs. 0.4 %; Picea rubens), lodgepole pine (11.2 % vs. 5.6 %; Pinus contorta), Douglas-fir (2.7 % vs. 1.3 %; Pseudotsuga menziesii), and western hemlock (4.8 % vs. 2 %; Tsuga heterophylla) were found to be overrepresented. Both post-fire and post-harvest dynamics indicated a general trend of regeneration to the same pre-disturbance tree species, with post-harvest landscapes exhibiting a greater variety of different dominant tree species. These results provide valuable information for sustainable forest management and can inform conservation strategies by helping to better understand the shortand long-term effects of forest disturbances on species presence and distribution.

The northern forest-tundra ecotone is one of the fastest warming regions of the globe. Models of vegetation change generally predict a northward advance of boreal forests and corresponding retreat of the tundra. Previous satellite remote sensing analyses in this region have focused on mapping vegetation greenness and tree cover derived from optical multi -spectral sensors. Changes in vegetation structure relating to height and biomass are less frequently investigated due to limited availability of lidar data over space and time in comparison with optical platforms. As such, there is an opportunity to combine lidar and optical remote sensing products for continuous mapping of vegetation structure at high -latitudes, with an emphasis on the forest-tundra transition. In this study, we used lidar data from the Ice, Cloud and land Elevation Satellite (ICESat-2) to classify canopy presence/absence, and predict canopy height across 120 million hectares of the Canadian forest-tundra ecotone at 30 m spatial resolution. Spatially continuous predictors derived from the Landsat satellite archive (2012-2021) and the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) Digital Elevation Model were used to extrapolate 98th percentile canopy height from the ICESat-2 Land and Vegetation Height (ATL08) product using Random Forests models developed in R (version 4.2.2). Model accuracy was assessed using data from the Land, Vegetation and Ice Sensor (LVIS), a large-footprint airborne lidar system. The overall accuracy of the canopy presence classification was 89%, and canopy presence was detected with 88% accuracy. Models of vegetation height showed an overall R 2 of 0.54 and RMSE of 2.09 m. Finally, we used these methods to map the limit of continuous 3 m forest across Canada and compared our model outputs with forest cover from the MODIS and Landsat Vegetation Continuous Fields datasets. This work demonstrates the challenges and potential for mapping horizontal and vertical vegetation structure within sparse, high latitude forests using both lidar and optical remote sensing data.

Ecosystem dynamics and ecological disturbances manifest as breakpoints in long-term multispectral remote sensing time series. Typically, these breakpoints are captured using univariate methods applied individually to each band, with subsequent integration of the results. However, multivariate analysis provides a promising way to fully incorporate the multispectral bands into breakpoints detection methods, but it has been rarely applied in monitoring ecosystem dynamics and detecting ecological disturbances. In this research, we developed a multivariate algorithm, named breakpoints-Detection algoRithm using MultivAriate Time series (DRMAT). DRMAT can fully use multispectral bands simultaneously with the consideration of the inter-correlation among bands. It decomposes a multivariate time series into trend, seasonality, and noise, iteratively segmenting the detrended/ de-seasonalized signals. We quantitatively evaluated DRMAT using both simulated multivariate data and randomly sampled real-world data, including subtle land cover changes caused by forest disturbances (depletions) and recovery (return of vegetation), as well as subtle changes over a broad range of land cover types. We also qualitatively assessed DRMAT in mapping real-world disturbances. For simulated data with prescribed breakpoints in both trend and seasonality, DRMAT detected breakpoints in trend with an F1 score of 85.5 % and in seasonality with an F1 score of 91.7 %. For real-world data in forested land cover, DRMAT unveiled both disturbances and subsequent recovery with an F1 score of 95.1 % for disturbances and 77.1 % for recovery. It detected disturbances in broader land cover types with an F1 score of 84.0 %. We demonstrated that using allband data was more accurate than using selected bands in breakpoint detection. The inclusion of vegetation indices as model inputs did not improve accuracy unless the original input bands lacked the specific band information in the vegetation indices. As a multivariate approach, DRMAT leverages the full information in the multispectral data and avoids the necessity of integrating results derived from individual bands.

Tree architecture reflects a hierarchical growth pattern shaped by the interplay between genetics and the environment. Environmental variation leads to unique resource availability, resulting in each tree developing distinct structural features, akin to the uniqueness of a human fingerprint. In this study, we propose a nondestructive method for quantifying this architectural uniqueness using terrestrial laser scanning for tree identification. While tree identification is commonly based on their precise geospatial location, this information may not always be available. Instead, we hypothesized that a tree's stem profile (diameters along the stem) and branching arrangement (locations of branch origins on the stem surface) could distinguish individuals within a population. The experimental setup included 65 Scots pine (Pinus sylvestris L.) trees in a managed boreal forest stand, scanned with terrestrial laser scanning in September 2021 (T1) and November 2022 (T2). We investigated whether individual trees could be identified based on architectural similarities between their point cloud reconstructions from T1 and T2. In total, 52 trees (80.0%) were identified based on their architectural characteristics. The results supported our hypothesis, showing that identifying >= 10 branch origins from independent reconstructions was sufficient to establish architectural uniqueness, resulting in 100% identification accuracy (n = 20 trees). These findings suggest that the complex three-dimensional tree architecture can be condensed into a two-dimensional pattern of points representing branch arrangement, which we term the "tree fingerprint." These architectural characteristics, which can be reconstructed from the lower half of the tree, are well suited for acquisition via ground-based sensing techniques such as terrestrial or mobile laser scanning. If point cloud data capable of characterizing individual branches is acquired during forest operations, the proposed methodology can facilitate tree identification for applications such as wood tracking, even without geospatial coordinates.

Space-based laser altimetry has revolutionized our capacity to characterize terrestrial ecosystems through the direct observation of vegetation structure and the terrain beneath it. Data from NASA's ICESat-2 mission provide the first comprehensive look at canopy structure for boreal forests from space-based lidar. The objective of this research was to create ICESat-2 aboveground biomass density (AGBD) models for the global entirety of boreal forests at a 30 m spatial resolution and apply those models to ICESat-2 data from the 2019–2021 period. Although limited in dense canopy, ICESat-2 is the only space-based laser altimeter capable of mapping vegetation in northern latitudes. Along each ICESat-2 orbit track, ground and vegetation height is captured with additional modeling required to characterize biomass. By implementing a similar methodology of estimating AGBD as GEDI, ICESat-2 AGBD estimates can complement GEDI's estimates for a full global accounting of aboveground carbon. Using a suite of field measurements with contemporaneous airborne lidar data over boreal forests, ICESat-2 photons were simulated over many field sites and the impact of two methods of computing relative height (RH) metrics on AGBD at a 30 m along-track spatial resolution were tested; with and without ground photons. AGBD models were developed specifically for ICESat-2 segments having land cover as either Evergreen Needleleaf or Deciduous Broadleaf Trees, whereas a generalized boreal-wide AGBD model was developed for ICESat-2 segments whose land cover was neither. Applying our AGBD models to a set of over 19 million ICESat-2 observations yielded a 30 m along-track AGBD product for the pan-boreal. The ability demonstrated herein to calculate ICESat-2 biomass estimates at a 30 m spatial resolution provides the scientific underpinning for a full, spatially explicit, global accounting of aboveground biomass.

Wildfire is the dominant stand-replacing disturbance regime in Canadian boreal forests. An accurate quantification of postfire changes in forest structure and aboveground biomass density (AGBD) provides a means to understand the magnitudes of ecosystem changes through wildfires and related linkages with global climate. While multispectral remote sensing has been extensively utilized for burn severity assessment, its capacity for postfire forest structure and AGBD change monitoring has been more limited to date. This study evaluates the interactions among burn severity, forest structure, and fire-return intervals for two representative sites in the western Canadian boreal forest. We adopted burn severity measurements from Landsat to characterize the heterogeneity of wildfire effects, while vertical forest structure information from Lidar was utilized to inform on realized forest changes and carbon fluxes associated with fire. Dominant trees in biomass-rich stands showed higher tolerance to low- and moderate-severity wildfires, while understory vegetation in these same stands showed a severity-invariant response to wildfires indicated by high vegetation mortality regardless of burn severity levels. Compared to a site without previous burn, canopy height and AGBD experienced lower magnitudes of change after subsequent wildfires, explained by a negative feedback between high frequency wildfires and biomass loss (Delta Canopy Height(single wildfire)=3.03m;Delta Canopy Height(successive wildfire)=2.47m; Delta AGBD(single wildfire)=8.40Mg/ha;Delta AGBDsuccessive wildfir( $successive wildfire) = 6.69 Mg/ha). This study provides new insights into forest recovery dynamics following fire disturbance, which is particularly relevant given increased fire frequency and intensity in boreal ecosystems resulting from climate change.

Satellite data are increasingly used to provide information to support forest monitoring and reporting at varying levels of detail and for a range of attributes and spatial extents. Forests are dynamic environments and benefit from regular assessments to capture status and changes both locally and over large areas. Satellite data can provide products relevant to forest science and management on a regular basis (e.g. annually) for land cover, disturbance (i.e. date, extent, severity, and type), forest recovery (e.g. quantification of return of trees following disturbance), and forest structure (e.g. volume, biomass, canopy cover, stand height), with products generated over large areas in a systematic, transparent, and repeatable fashion. While pixel-based outcomes are typical based upon satellite data inputs, many end users continue to require polygon-based forest inventory information. To meet this information need and have a spatial context for forest inventory attributes such as tree species assemblages, we present a new work-flow to produce a novel spatially explicit, stand-level satellite-based forest inventory (SBFI) in Canada applying image segmentation approaches to generate spatially unique forest stands (polygons), which are the fundamental spatial unit of management-level inventories. Thus, SBFI offers spatial context to aggregate and generalize other pixel-based forest data sets. Canada has developed a National Terrestrial Ecosystem Monitoring System (NTEMS) that utilizes medium spatial resolution imagery, chiefly from Landsat, to annually characterize Canada's forests at a pixel level from 1984 until present. These NTEMS datasets are used to populate SBFI polygons with information regarding status (e.g. current land cover type, dominant tree species, or total biomass) as well as information on dynamics (e.g. has this polygon been subject to change, when, by what, and if so, how is the forest recovering). Here, we outline the information drivers for forest monitoring, present a set of products aimed at meeting these information needs, and follow to demonstrate the SBFI concept over the 650-Mha extent of Canada's forest-dominated ecosystems. In so doing, the entirety of Canada's forest ecosystems (managed and unmanaged) were mapped using the same data, attributes, and temporal representation. Moreover, the use of polygons allows for the generation of attributes such as tree species composition, and total biomass and wood volume in a stand-scale format familiar to landscape managers and suitable for strategic planning. The data, methods, and outcomes presented here are portable to other regions and input data sources, and the national SBFI outcomes for Canada are available via open access.