Understory biomass plays an important role in forests, and explicit characterizations of live and dead understory vegetation are critical for wildland fuel characterization and to link understory vegetation to ecosystem processes. Current methods to accurately model understory fuel complexity in 3D rely on expensive and often inaccessible technologies. Structure-from-motion close-range photogrammetry, in which ordinary photographs or video stills are overlaid to generate point clouds, is promising as an alternative method to generate 3D models of fuels at a fraction of the cost of more traditional field surveys. In this study, we compared the performance of close-range photogrammetry with field sampling surveys to assess the utility of this alternative technique for quantifying understory fuel structure. Using a commercially available GoPro camera, we generated 3D point cloud models from video-derived image stills of 138 sampling plots across two western ponderosa pine and two southeastern slash pine sites. We directly compared structural metrics derived from the photogrammetry to those derived from field sampling, then evaluated predictive models of biomass calibrated by means of destructive sampling. Photogrammetry-derived measures of occupied volume and fuel height showed strong agreements with field sampling (Pearson’s R = 0.81 and 0.86, respectively). While we found weak relationships between photogrammetry metrics and biomass 0 to 10 cm in height, occupied volume and a novel metric to characterize the vertical profile of vegetation produced the strongest relationships with biomass above the litter layer (i.e., >10 cm) across different fuel types (R² = 0.55–0.76). The application of this technique has the potential to provide managers with an accessible option for inexpensive data collection and can lay the groundwork for the rapid collection of input datasets to train landscape-scale fuel models.

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.

Southern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such as NASA’s GEDI spaceborne LiDAR, enable more precise mapping of canopy cover. Although GEDI provides accurate data, its limited spatial coverage restricts large-scale assessments. To address this, we combined GEDI with Synthetic Aperture Radar (SAR), and optical imagery (Sentinel-1 GRD and Landsat–Sentinel Harmonized (HLS)) data to create a comprehensive canopy cover map for Florida. Using a random forest algorithm, our model achieved an R² of 0.69, RMSD of 0.17, and MD of 0.001, based on out-of-bag samples for internal validation. Geographic coordinates and the red spectral channel emerged as the most influential predictors. External validation with airborne laser scanning (ALS) data across three sites yielded an R² of 0.70, RMSD of 0.29, and MD of −0.22, confirming the model’s accuracy and robustness in unseen areas. Statewide analysis showed lower canopy cover in southern versus northern Florida, with wetland forests exhibiting higher cover than upland sites. This study demonstrates the potential of integrating multiple remote sensing datasets to produce accurate vegetation maps, supporting forest management and sustainability efforts in Florida.

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). 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.