Dry forest ecosystems in the western United States face the pressure of uncharacteristically severe wildfires and widespread drought-induced mortality as a result of fire exclusion, past management practices, and climate change. Implementing forest treatments that incorporate individual tree, clump, and opening (ICO) patterns can help to increase forest resilience to these disturbances. We explore tradeoffs in meeting treatment goals while incorporating ICO concepts in a case study using RxGaming, a publicly available, open-source software tool. The RxGaming tool provides a framework for decision making via 1) visualization and assessment of current stand structure and 2) treatment simulations using an algorithm that incorporates ICO-based thinning methods and reflects user-defined objectives.

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.

AbstractInterruption 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.

Maintaining dense forest habitats for the threatened northern spotted owl (NSO) has proven challenging in seasonally dry, fire-dependent landscapes where low-density conditions were historically dominant and are generally more climate- and disturbance-resilient. To better inform the dual, sometimes-conflicting objectives of species conservation and forest resilience, we developed an approach to evaluate NSO habitat sustainability by: (1) quantifying the structure of high suitability habitat (HSH) associated with NSO using two remotely sensed platforms, (2) estimating current and historical HSH abundance, and (3) identifying HSH locations more likely to persist given current and future forest-zone climate projections and increasing risk of severe wildfire. Tall, closed-canopy conditions effectively comprised the key structural features of HSH, providing a means to map habitat through time. Both historical amounts and contemporary spatial patterns of HSH and other forest and non-forest conditions around occupied NSO sites indicated that HSH and forest resilience goals can be congruent at multiple scales. Independent lines of evidence suggest HSH historically composed similar to 18-24% of the dry and moist mixed-conifer landscape - considerably lower levels than current management goals in many areas. Projected shifts in climate and severe-fire likelihood suggest substantial spatial and temporal shifts where HSH will be sustainable into the future - mainly in currently moist as well as some cold forest types. These findings can inform the potential convergence and trade-offs of species conservation and disturbance resilience goals across local and regional landscapes, based on the inherent capacity of the landscape to support both goals under projected shifts in climate and wildfire.

Contemporary reference sites in California's Sierra Nevada represent areas where a frequent, low-intensity fire regime – an integral ecological process in temperate dry forests – has been reintroduced after several decades of fire suppression. Produced by an intact fire regime, forest structural patterns in these sites are likely more resilient to future disturbances and climate, and thus can provide reference conditions to guide management and ecological research. In this paper, we present a set of 119 delineated contemporary reference sites in the Sierra Nevada yellow pine and mixed-conifer zone along with a suite of key remote sensing-derived forest structure metrics representing conditions within these sites. We also provide a set of summary figures for individual reference sites and sites grouped by dominant climate class. We identified restored frequent-fire landscapes using a combination of fire history, burn severity, management history, and forest type datasets and we delineated individual polygons using catchment basins, fire perimeters, and imagery. Reference sites ranged in size from 101-966 ha with a mean size of 240 ha. Where available (for 59 sites), we used airborne lidar datasets to characterize a suite of key forest structure metrics within reference sites. Across all 119 sites, we provide a set of forest structure metrics produced by the California Forest Observatory. Reference sites were categorized based on their dominant climate class to assist users in identifying the most climatically relevant reference conditions for their project or study area. We encourage the use of the reference sites and associated forest structure datasets for guiding ecologically focused forest management and research in the Sierra Nevada.

We investigated the relative importance of daily fire weather, landscape position, climate, recent forest and fuels management, and fire history to explaining patterns of remotely-sensed burn severity - as measured by the Relativized Burn Ratio - in 150 fires occurring from 2001 to 2019, which burned conifer forests of northeastern Washington State, USA. Daily fire weather, annual precipitation anomalies, and species' fire resistance traits were important predictors of wildfire burn severity. In areas burned within the past two to three decades, prior fire decreased the severity of subsequent burns, particularly for the first 16 postfire years. In areas managed before a wildfire, thinning and prescribed burning treatments lowered burn severity relative to untreated controls. Prescribed burning was the most effective treatment at lowering subsequent burn severity, and prescribed burned areas were usually unburned or burned at low severity in subsequent wildfires. Patches that were harvested and planted <10 years before a wildfire burned with slightly higher severity. In areas managed within 5 years after an initial fire, postfire harvest and planting reduced prevalence of stand-replacing fire in reburns. However, overall, postfire management actions after a first wildfire only weakly influenced the severity of subsequent fires. The importance of fire-fire interactions to moderating burn severity establishes the importance of stabilizing feedbacks in active fire regimes, and our results demonstrate how silvicultural treatments can be combined with prescribed fire and wildfires to maintain resilient landscapes.

We evaluated the effects of postfire management on forest structure in mixed-conifer forests of northeastern Washington, USA. Postfire treatments were harvest-only, harvest combined with planting, planting-only, and postfire prescribed fire. We used aerial light detection and ranging (LiDAR) to measure vertical and horizontal components of postfire forest structure over a period of 2 to 32 years after fires. We compared treated areas to control areas with similar bioclimatic environments and past fire severity. We used niche overlap statistics to quantify distributions of individual forest structure components and PERMANOVA to assess forest structural response to the presence or absence of treatments, past fire severity, time since treatment, and bioclimatic setting. Harvest alone after fire decreased dominant tree height and reduced vertical canopy complexity and the cover of tall trees. Harvest combined with planting increased dominant tree height, vertical complexity, and cover in lower height strata. Planting and prescribed fires showed little difference in forest structure relative to untreated controls. Overall, the burn severity of the initial fire was the strongest influence on postfire structure, and many aspects of vertical and horizontal forest structure showed little difference with increasing time since fire.

Mountain ecosystems contain strong elevational gradients in climate and vegetation that shape species distributions and the structure of animal communities. Nevertheless, studies of habitat selection for individual species rarely account for such gradients that often result in species being managed uniformly across their range, which may not improve conservation as intended. Therefore, we characterized variation in nocturnal habitat selection by 18 GPS-tagged California spotted owls (Strix occidentalis occidentalis) along a 1400-m elevational gradient in the Sierra Nevada, California. We characterized three-dimensional forest structure with light detection and ranging data that we used in mixed-effects resource- and step-selection analyses of owl habitat selection. At lower elevations, owls selected stands with shorter trees, sites closer to hard edges between tall forests and open areas, sites with less diversity in forest seral types and sites with more ridge and southwest aspects. In contrast, owls at higher elevations selected the opposite. Within public forests that had taller trees and within their home range core (45% kernel density estimate of GPS points) areas, owls selected forests with less and more canopy cover at low and high elevations, respectively. Outside of their core areas, owls selected areas with fewer and more tall trees at low and high elevations, respectively. These findings may be explained by elevational gradients in prey distribution and variation in owl diet because owls consume more woodrats (Neotoma spp; earlier seral species) at lower elevations and more flying squirrels (Glaucomys sabrinus; older forest species) at higher elevations. Thus, at low elevations and in areas unlikely to support nesting, spotted owls could benefit from management that promotes woodrat habitat by encouraging oak regeneration and creating small brushy openings within forests with shorter (younger) trees. Conversely, at higher elevations, (1) enhancing flying squirrel habitat by promoting large trees and denser canopy on mesic sites and (2) managing for greater cover type diversity on southwest-facing slopes and ridgetops is more likely to improve foraging habitat quality for spotted owls. The patterns of owl selection over elevational gradients has not been explicitly considered in most habitat management plans but clearly would improve management throughout mountain ecosystems.

AbstractFollowing 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.

Context As the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by weather, fuels, topography, and management. Objectives Identify the biophysical and management drivers of severity patterns and their spatial variability across the 2013 Rim Fire, Sierra Nevada, California, USA. Methods Random forest models were developed separately for reburned and fire-excluded (> 80 year) areas within Yosemite National Park (NP) and Stanislaus National Forest (NF). Models included biophysical, past disturbance, and spatial autocorrelation (SA) predictors. Variable importance was assessed globally and locally. Variance partitioning was used to assess pure and shared variance among predictors. Results High spatial variability in the relative dominance of predictors existed across burn days and between land ownerships. Fire weather was a dominant top-down control during plume-dominated fire spread days. However, bottom-up controls from fuels and topography created local, fine-scale heterogeneity throughout. Reburn severity correlated with previous severity suggesting strong landscape memory, particularly in Yosemite NP. SA analysis showed broad-scale spatial dependencies and high shared variance among predictors. Conclusions Wildfires are inherently a multi-scaled process. Spatial structure in environmental variables create broad-scale patterns and dependencies among drivers leading to regions of similar fire behavior, while local bottom-up drivers generate fine-scaled heterogeneity. Identifying the conditions under which top-down factors overwhelm bottom-up controls can help managers monitor and manage wildfires to achieve both suppression and restoration goals. Restoration targeting both surface and ladder fuels can mediate future fire severity even under extreme weather conditions.

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 (R 2 = 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 (R 2 = 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.

One challenge that land managers face in the southern Sierra Nevada is how to balance conservation of fisher (Pekania pennanti) habitat with the reintroduction of fire. The fisher population in the southern Sierra Nevada is of high conservation priority, due to its small population size, genetic isolation, and the risk of habitat loss due to wildfire and fuel reduction activities. It is unknown whether contemporary forests altered by fire suppression can support fisher habitat following the reintroduction of fire. We examined whether patch-scale forest habitat conditions used by fishers in a landscape with limited recent fire (Dinkey study area) also exist in a landscape with restored fire (Yosemite study area). We developed random forest and logistic regression models using lidar-derived forest structure metrics to distinguish reproductive den presence (n = 261) from randomly-generated "available" points (n = 261) within an estimate of the female population home range. The full logistic regression model correctly classified (under cross-validation) 69.5% of observations and the random forest model correctly classified 74.3%. The parsimonious logistic regression model we selected had comparable accuracy to the full model (correctly classified 68.8% of observations) and included the following variables: cover > 2 m, 95th percentile height, and 25th percentile height. Partial dependence plots suggest thresholds at which predicted probability of reproductive den presence exceeds 50%: cover > 2 m greater than 60%, 95th percentile height of at least 32 m, and 25th percentile height between 4 m and 14 m. We found that comparable thresholds of forest cover and tree height exist in burned areas in Yosemite; 43.0% of burned pixels had similar lidar-derived forest structural characteristics to those predictive of reproductive dens in Dinkey. Areas with similar forest structures occurred within a range of fire severities and years since the most recent fire, and particularly in low-severity fire conditions (mean differenced normalized burn ratio [dNBR] value: 128.4). These results are promising for land managers who face the challenge of simultaneously reducing the risk of high-severity fire and conserving fisher habitat, however more research is needed to conclude whether suitable fisher habitat can exist in burned areas at all scales of selection and for all activities of the fisher population.

BACKGROUND:Major depressive disorder (MDD) is associated with poor recollection, but the neural mechanisms responsible for this deficit are unclear. Recollection is supported by interactions between the hippocampus and cortex that appear to be mediated by oscillatory activity in the theta band (4-7 Hz) and that are elicited during source memory retrieval. Therefore, we tested the hypothesis that evoked theta power during source memory retrieval would be reduced in MDD, as this would provide a physiological basis for deficient recollection in adults with depression.METHODS:Morlet wavelets were applied to event-related potentials collected from 24 unmedicated adults with MDD and 24 healthy control adults during the retrieval of source and semantic memories. Whole-scalp analyses focused on group differences in evoked theta power.RESULTS:There were no group differences in behavior. Nevertheless, from 400 to 799 ms, theta power was broadly reduced in adults with depression versus healthy adults. This reduction was observed during source and semantic retrieval. Parietal midline electrodes showed significantly reduced theta power during source-but not semantic-retrieval in adults with depression versus healthy adults in this interval. Furthermore, theta power over parietal midline sites from 400 to 799 ms was more strongly related to source memory accuracy in healthy adults versus adults with depression.CONCLUSIONS:Relative to healthy control adults, adults with depression showed reduced theta power during memory retrieval and a weaker relationship between parietal midline theta power and source memory accuracy. These findings indicate that abnormal theta signals may contribute to memory deficits in adults with MDD.

A century of fire suppression has left fire-dependent forests of the western United States increasingly vulnerable to wildfire, drought, and insects. Forest managers are trying to improve resilience using treatments such as mechanical thinning and prescribed fire; however, operational and resource constraints limit treatments to a fraction of the needed area each year. An alternative is to let wildfires burn under less-than-extreme fire weather where human lives and infrastructure are not at risk. We examined post-fire forest structure using airborne lidar data to determine whether a single wildland fire following an extended fire-free period could produce forest structures resembling fire-resilient historical conditions. We studied forest structures resulting from these "first-entry" fires in a forest with a history of timber management (2008 American River Complex Fires, Tahoe National Forest) and in a wilderness area (2009 Big Meadow Fire, Yosemite National Park). We compared the results of these first-entry fires with nearby reference areas that had experienced 2+ fires that burned predominately at low- and moderate-severity. We identified visible overstory trees from the lidar data and examined their patterns in terms of individuals, tree clumps, and openings. We found that moderate-severity fire effects in these first-entry fires produced similar patterns to the reference areas with area in openings at approximately 40% and trees predominately in small (2 to 4 trees) and medium (5-9 trees) clumps High-severity fire produced mortality likely to lead to large canopy openings that were historically uncharacteristic in these forests. As burn severity increased, the amount of the residual canopy area represented by taller trees ( > 16 m and especially > 32 m) decreased, which could result from fires preferentially killing taller trees or from locations with taller trees more commonly experiencing lower burn severities. Our study suggests that first-entry fires allowed to burn under less-than-extreme conditions can reproduce spatial patterns resembling historical conditions resilient to fires and drought but possibly at the disproportionate expense of larger trees.

Coexistence of ecologically similar species can be maintained by partitioning along one or more niche axes. Three-dimensional structural complexity is central to facilitating resource partitioning between many forest species, but is underrepresented in field-based studies. We examined resource selection by sympatric northern spotted owls (Strix occidentalis caurina), a threatened species under the US Endangered Species Act, and nonnative barred owls (S. varia) in western Oregon, USA to explore the relative importance of canopy heterogeneity, vertical complexity of forest, and abiotic features to resource selection and identify potential differences that may facilitate long-term coexistence. We predicted that within home range selection of understory densities, measured with airborne lidar, would differ between species based on proportional differences in arboreal and terrestrial prey taken by each owl species. We used discrete choice models and telemetry data from 41 spotted owls and 38 barred owls monitored during 2007–2009 and 2012–2015. Our results suggested that while both species used tall canopy areas more often than low canopy areas, spotted owls were more commonly found in areas with lower tree cover, more developed understory, and steeper slopes. This is the first evidence of fine-scale partitioning based on structural forest properties by northern spotted owls and barred owls.

Background: Satellite-based aboveground forest biomass maps commonly form the basis of forest biomass and carbon stock mapping and monitoring, but biomass maps likely vary in performance by region and as a function of spatial scale of aggregation. Assessing such variability is not possible with spatially-sparse vegetation plot networks. In the current study, our objective was to determine whether high-resolution lidar-based and moderate-resolution Landsat-base aboveground live forest biomass maps converged on similar predictions at stand- to landscape-levels (10 s to 100 s ha) and whether such differences depended on biophysical setting. Specifically, we examined deviations between lidar- and Landsat-based biomass mapping methods across scales and ecoregions using a measure of error (normalized root mean square deviation), a measure of the unsystematic deviations, or noise (Pearson correlation coefficient), and two measures related to systematic deviations, or biases (intercept and slope of a regression between the two sets of predictions). Results: Compared to forest inventory data (0.81-ha aggregate-level), lidar and Landsat-based mean biomass predictions exhibited similar performance, though lidar predictions exhibited less normalized root mean square deviation than Landsat when compared with the reference plot data. Across aggregate-levels, the intercepts and slopes of regression equations describing the relationships between lidar- and Landsat-based biomass predictions stabilized (i.e., little additional change with increasing area of aggregates) at aggregate-levels between 10 and 100 ha, suggesting a consistent relationship between the two maps at landscape-scales. Differences between lidar- and Landsat-based biomass maps varied as a function of forest canopy heterogeneity and composition, with systematic deviations (regression intercepts) increasing with mean canopy cover and hardwood proportion within forests and correlations decreasing with hardwood proportion. Conclusions: Deviations between lidar- and Landsat-based maps indicated that satellite-based approaches may represent general gradients in forest biomass. Ecoregion impacted deviations between lidar and Landsat biomass maps, highlighting the importance of biophysical setting in determining biomass map performance across aggregate scales. Therefore, regardless of the source of remote sensing (e.g., Landsat vs. lidar), factors affecting the measurement and prediction of forest biomass, such as species composition, need to be taken into account whether one is estimating biomass at the plot, stand, or landscape scale.

Restoration of western dry forests in the USA often focuses on reducing fuel loads. In the range of the spotted owl, these treatments may reduce canopy cover and tree density, which could reduce preferred habitat conditions for the owl and other sensitive species. In particular, high canopy cover (>= 70%) has been widely reported to be an important feature of spotted owl habitat, but averages of stand-level forest cover do not provide important information on foliage height and gap structure. To provide better quantification of canopy structure, we used airborne LiDAR imagery to identify canopy cover in different height strata and the size and frequency of gaps that were associated with owl nest sites, protected activity centers (PACs), and territories within four study areas and 316 owl territories. Although total canopy cover was high in nest stands and PAC areas, the cover in tall ( > 48 m) trees was the canopy structure most highly selected for, while cover in lower strata (2-16 m) was avoided compared to availability in the surrounding landscape. Tall tree cover gradually decreased and lower strata cover increased as distance increased from the nest. Large ( > 1000 m(2)) gaps were not found near nests, but otherwise there was no difference in gap frequencies and sizes between PACs and territories and the surrounding landscape. Using cluster analysis we classified canopy conditions into 5 structural classes and 4 levels of canopy cover to assess the relationship between total canopy cover and tree size within nest sites, PACs, and territories. High canopy cover (>= 70%) mostly occurs when large tree cover is high, indicating the two variables are often confounded. Our results suggest that the cover of tall trees may be a better predictor of owl habitat than total canopy cover because the latter can include cover in the 2-16 m strata - conditions that owls actually avoid. Management strategies designed to preserve and facilitate the growth of tall trees while reducing the cover and density of understory trees may improve forest resilience to drought and wildfire while also maintaining or promoting the characteristics of owl habitat.

The fate of live forest biomass is largely controlled by growth and disturbance processes, both natural and anthropogenic. Thus, biomass monitoring strategies must characterize both the biomass of the forests at a given point in time and the dynamic processes that change it. Here, we describe and test an empirical monitoring system designed to meet those needs. Our system uses a mix of field data, statistical modeling, remotely-sensed time-series imagery, and small-footprint lidar data to build and evaluate maps of forest biomass. It ascribes biomass change to specific change agents, and attempts to capture the impact of uncertainty in methodology. We find that: A common image framework for biomass estimation and for change detection allows for consistent comparison of both state and change processes controlling biomass dynamics. Regional estimates of total biomass agree well with those from plot data alone. The system tracks biomass densities up to 450-500 Mg ha(-1) with little bias, but begins underestimating true biomass as densities increase further. Scale considerations are important. Estimates at the 30 m grain size are noisy, but agreement at broad scales is good. Further investigation to determine the appropriate scales is underway. Uncertainty from methodological choices is evident, but much smaller than uncertainty based on choice of allometric equation used to estimate biomass from tree data. In this forest-dominated study area, growth and loss processes largely balance in most years, with loss processes dominated by human removal through harvest. In years with substantial fire activity, however, overall biomass loss greatly outpaces growth. Taken together, our methods represent a unique combination of elements foundational to an operational landscape-scale forest biomass monitoring program.