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This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R2 = 0.8, RMSE = 115 Mg ha−1, Bias = 2 Mg ha−1). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R2 = 0.8, RMSE = 152 Mg ha−1, Bias = 9 Mg ha−1), including higher AGB values (>400 Mg ha−1) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps.

Interruption of frequent burning in dry forests across western North America and the continued impacts of anthropogenic climate change have resulted in increases in fire size and severity compared to historical fire regimes. Recent legislation, funding, and planning have emphasized increased implementation of mechanical thinning and prescribed burning treatments to decrease the risk of undesirable ecological and social outcomes due to fire. As wildfires and treatments continue to interact, managers require consistent approaches to evaluate treatment effectiveness at moderating burn severity. In this study, we present a repeatable, remote sensing–based, analytical framework for conducting fire‐scale assessments of treatment effectiveness that informs local management while also supporting cross‐fire comparisons. We demonstrate this framework on the 2021 Bootleg Fire in Oregon and the 2021 Schneider Springs Fire in Washington. Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. The application of our framework showed that, in both fires, recent prescribed burning treatments were the most effective at reducing burn severity relative to untreated controls. In contrast, thinning‐only treatments only produced low/moderate‐severity effects under the more moderate fire weather conditions in the Schneider Springs Fire. Our framework offers a robust approach for evaluating treatment effects on burn severity at the scale of individual fires, which can be scaled up to assess treatment effectiveness across multiple fires. As climate change brings increased uncertainty to dry forest ecosystems of western North America, our framework can support more strategic management actions to reduce wildfire risk and foster resilience.

Following a wildfire, regeneration to forest can take decades to centuries and is no longer assured in many western U.S. environments given escalating wildfire severity and warming trends. After large fire years, managers prioritize where to allocate scarce planting resources, often with limited information on the factors that drive successful forest establishment. Where occurring, long‐term effects of postfire salvage operations can increase uncertainty of establishment. Here, we collected field data on postfire regeneration patterns within 13‐ to 28‐yr‐old burned patches in eastern Washington State. Across 248 plots, we sampled tree stems <4 m height using a factorial design that considered (1) fire severity, moderate vs. high severity; (2) salvage harvesting, salvaged vs. no management; and (3) potential vegetation type (PVT), sample resides in a dry, moist, or cold mixed‐conifer forest environment. We found that regeneration was abundant throughout the study region, with a median of 4414 (IQR 19,618) stems/ha across all plots. Only 15% of plots fell below minimum timber production stocking standards (350 trees/ha), and <2% of plots were unstocked. Densities were generally highest in high‐severity patches and following salvage harvesting, although high variability among plots and across sites led to variable significance for these factors. Post hoc analyses suggested that mild postfire weather conditions may have reduced water stress on tree establishment and early growth, contributing to overall high stem densities. Douglas fir was the most abundant species, particularly in moderate‐severity patches, followed by ponderosa pine, lodgepole pine, western larch, and Engelmann spruce. Generalized additive models (GAMs) revealed species‐level climatic tolerances and seed dispersal limits that portend future challenges to regeneration with expected future climate warming and increased fire activity. Postfire regeneration will occur on sites with adequate seed sources within their climatic tolerances.

Wildfire activity has accelerated with climate change, sparking concerns about uncharacteristic impacts on mature and old‐growth forests containing large trees. Recent assessments have documented fire‐induced losses of large‐tree habitats in the US Pacific Northwest, but key uncertainties remain regarding contemporary versus historical fire effects in different forest composition types, specific impacts on large trees within closed versus open canopies, and the role of fuel reduction treatments. Focusing on the 2021 Schneider Springs Fire, which encompassed 43,000 ha in the eastern Cascade Range of Washington and burned during a period of severe drought, this study addresses three interrelated questions: (1) Are burn severity distributions consistent with historical fire regimes in dry, moist, and cold forest types? (2) How does burn severity vary among forest structure classes, particularly large trees with open versus closed canopies? (3) How do fuel reduction treatments influence forest structure and burn severity inside and outside of treated areas? Within each forest type, burn severity proportions were similar to historical estimates, with lower overall severity in dry forests than in moist and cold forests. However, across all forest types combined, high‐severity fire affected 30% (4500 ha) of large‐tree locations with tree diameters >50 cm. In each forest type, burn severity was lower in locations with large‐open structure (<50% canopy cover) than in locations with large‐closed structure (>50% canopy cover). Burn severity also was lower inside than outside treated sites in all structure classes, and untreated large‐closed forests tended to burn at lower severity closer to treatments. These results highlight the susceptibility of dense, late‐successional forests to contemporary fires, even in events with widespread potentially beneficial effects consistent with historical fire regimes. These results also illustrate the effectiveness of treatments that shift large‐closed to large‐open structures and suggest that treatments may help mitigate fire effects in adjacent large‐closed forests. Long‐term monitoring and adaptive management will be essential for conserving critical wildlife habitats and fostering ecosystem resilience to climate change, wildfires, and other disturbances.

Maps of species composition are important for assessing a wide range of ecosystem functions in forested landscapes, including processes shaping community structure at broader (e.g., climate) and finer (e.g., disturbance) scales. Incorporating recently available remotely sensed datasets has the potential to improve species composition mapping by providing information to help predict species presence and relative abundance. Using USDA Forest Service Forest Inventory and Analysis plot data and the gradient nearest neighbor imputation modeling approach in eastern Washington, USA, we developed tree species composition and structure maps based on climate, topography, and two sources of remote sensing: height from digital aerial photogrammetry (DAP) of pushbroom aerial photography and Sentinel-2 multispectral satellite imagery. We tested the accuracy of these maps based on their capacity to predict species occurrence and proportional basal area for 10 coniferous tree species. In this study region, climate, topography, and location explained much of the species occurrence patterns, while both DAP and Sentinel-2 data were also important in predicting species proportional basal area. Overall accuracies for the best species occurrence model were 68–92% and R 2 for the proportional basal area was 0.08–0.55. Comparisons of model accuracy with and without remote sensing indicated that adding some combination of DAP metrics and/or Sentinel-2 imagery increased R 2 for the proportional basal area by 0.25–0.45, but had minor and sometimes negative effects on model skill and accuracy for species occurrence. Thus, species ranges appear most strongly constrained by environmental gradients, but abundance depends on forest structure, which is often determined by both environment and disturbance history. For example, proportional basal area responses to moisture limitation and canopy height varied by species, likely contributing to regional patterns of species dominance. However, local-scale examples indicated that remotely sensed forest structures representing recent disturbance patterns likely impacted tree community composition. Overall, our results suggest that characterizing geospatial patterns in tree communities across large landscapes may require not only environmental factors like climate and topography, but also information on forest structure provided by remote sensing.

We review science-based adaptation strategies for western North American (wNA) forests that include restoring active fire regimes and fostering resilient structure and composition of forested landscapes. As part of the review, we address common questions associated with climate adaptation and realignment treatments that run counter to a broad consensus in the literature. These include the following: (1) Are the effects of fire exclusion overstated? If so, are treatments unwarranted and even counterproductive? (2) Is forest thinning alone sufficient to mitigate wildfire hazard? (3) Can forest thinning and prescribed burning solve the problem? (4) Should active forest management, including forest thinning, be concentrated in the wildland urban interface (WUI)? (5) Can wildfires on their own do the work of fuel treatments? (6) Is the primary objective of fuel reduction treatments to assist in future firefighting response and containment? (7) Do fuel treatments work under extreme fire weather? (8) Is the scale of the problem too great? Can we ever catch up? (9) Will planting more trees mitigate climate change in wNA forests? And (10) is post-fire management needed or even ecologically justified? Based on our review of the scientific evidence, a range of proactive management actions are justified and necessary to keep pace with changing climatic and wildfire regimes and declining forest heterogeneity after severe wildfires. Science-based adaptation options include the use of managed wildfire, prescribed burning, and coupled mechanical thinning and prescribed burning as is consistent with land management allocations and forest conditions. Although some current models of fire management in wNA are averse to short-term risks and uncertainties, the long-term environmental, social, and cultural consequences of wildfire management primarily grounded in fire suppression are well documented, highlighting an urgency to invest in intentional forest management and restoration of active fire regimes.

For over 20 years, forest fuel reduction has been the dominant management action in western US forests. These same actions have also been associated with the restoration of highly altered frequent-fire forests. Perhaps the vital element in the compatibility of these treatments is that both need to incorporate the salient characteristics that frequent fire produced—variability in vegetation structure and composition across landscapes and the inability to support large patches of high-severity fire. These characteristics can be achieved with both fire and mechanical treatments. The possible key to convergence of fuel reduction and forest restoration strategies is integrated planning that permits treatment design flexibility and a longer-term focus on fire reintroduction for maintenance. With changing climate conditions, long-term forest conservation will probably need to be focused on keeping tree density low enough (i.e., in the lower range of historic variation) for forest conditions to adapt to emerging disturbance patterns and novel ecological processes.

Abstract LiDAR individual tree detection (ITD) is a promising tool for measuring forests at a scale that is meaningful ecologically and useful for forest managers. However, most ITD research evaluates methods over small homogeneous areas, while many forest managers work over large, complex landscapes. We investigated how ITD results varied across diverse structural conditions in California’s Sierra Nevada mixed-conifer forests and what this taught us about when and how to apply ITD. Our results suggest that it is advantageous to use ITD when it improves analysis interpretability, when measuring horizontal patterns, or when field data are unavailable. In the latter case, it is best to focus on measures dominated by large trees, like basal area and biomass. Thinking of ITD results as “tree-approximate objects” including one dominant tree and up to a few subordinate tree respects LiDAR’s strengths and limitations; we illustrate how this concept keeps analyses consistent across varying structural conditions.

CONTEXT: More than a century of forest and fire management of Inland Pacific landscapes has transformed their successional and disturbance dynamics. Regional connectivity of many terrestrial and aquatic habitats is fragmented, flows of some ecological and physical processes have been altered in space and time, and the frequency, size and intensity of many disturbances that configure these habitats have been altered. Current efforts to address these impacts yield a small footprint in comparison to wildfires and insect outbreaks. Moreover, many current projects emphasize thinning and fuels reduction within individual forest stands, while overlooking large-scale habitat connectivity and disturbance flow issues. METHODS: We provide a framework for landscape restoration, offering seven principles. We discuss their implication for management, and illustrate their application with examples. RESULTS: Historical forests were spatially heterogeneous at multiple scales. Heterogeneity was the result of variability and interactions among native ecological patterns and processes, including successional and disturbance processes regulated by climatic and topographic drivers. Native flora and fauna were adapted to these conditions, which conferred a measure of resilience to variability in climate and recurrent contagious disturbances. CONCLUSIONS: To restore key characteristics of this resilience to current landscapes, planning and management are needed at ecoregion, local landscape, successional patch, and tree neighborhood scales. Restoration that works effectively across ownerships and allocations will require active thinking about landscapes as socio-ecological systems that provide services to people within the finite capacities of ecosystems. We focus attention on landscape-level prescriptions as foundational to restoration planning and execution.

Abstract Forest-restoration efforts are increasing in the western United States in response to realized and expected changes in climate and disturbance regimes. Managers are challenged to find practical and defensible targets to shift forest composition, structure, and spatial pattern to a more resistant and resilient state. The Northeast Washington Forest Vision 2020 project on the Colville National Forest presented an opportunity to map and use previously uncaptured mesic stand-level historical reference conditions to a large restoration project. We reconstructed historical forest conditions in 12 plots across a range of plant-association groups and mapped five restoration treatment units after implementation. We evaluated treatment effectiveness both in terms of meeting the prescriptions’ stated objectives and by similarity to observed reference conditions using metrics of density, species composition, clump-size patterns, and open-space patterns. We found that dry plant associations were historically dominated by distributed clumps of large shade-intolerant trees, whereas cold mesic plant associations were structured as a gap-matrix spatial pattern. Treatments were effective at meeting prescribed density and species-composition targets, but generally resulted in stands that were overly uniform or clumped compared to historical reference conditions.