Arctic warming can influence tundra ecosystem function with consequences for climate feedbacks, wildlife and human communities. Yet ecological change across the Arctic tundra biome remains poorly quantified due to field measurement limitations and reliance on coarse-resolution satellite data. Here, we assess decadal changes in Arctic tundra greenness using time series from the 30 m resolution Landsat satellites. From 1985 to 2016 tundra greenness increased (greening) at ~37.3% of sampling sites and decreased (browning) at ~4.7% of sampling sites. Greening occurred most often at warm sampling sites with increased summer air temperature, soil temperature, and soil moisture, while browning occurred most often at cold sampling sites that cooled and dried. Tundra greenness was positively correlated with graminoid, shrub, and ecosystem productivity measured at field sites. Our results support the hypothesis that summer warming stimulated plant productivity across much, but not all, of the Arctic tundra biome during recent decades.

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI’s latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth’s forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat.

Large-extent maps of three-dimensional vegetation structure are important for understanding the hydrologic cycle, climate, carbon fluxes, and habitat. We aggregated over 7 billion lidar shots from the Global Ecosystem Dynamics Investigation (GEDI) to produce analysis-ready, gridded rasters of 36 vegetation structure metrics at three spatial resolutions (1, 6, and 12 km). We used 8 statistics to grid shots in every pixel, specifically the mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, Shannon’s Diversity Index, and shot count. We quantified uncertainty of the mean by randomly selecting 100 subsets of shots (i.e. bootstrapping) within each pixel. We also assessed the accuracy of several gridded metrics using fine spatial resolution airborne laser scanning data. The gridded metrics are generally more accurate at mid latitudes due to higher shot density and lower density of vegetation. Statistics associated with the central or maximum tendency of a metric are more accurate than statistics related to variability of metric values within the pixel.

Drivers of forest wildfire severity include fuels, topography and weather. However, because only fuels can be actively managed, quantifying their effects on severity has become an urgent research priority. Here we employed GEDI spaceborne lidar to consistently assess how pre-fire forest fuel structure affected wildfire severity across 42 California wildfires between 2019–2021. Using a spatial-hierarchical modeling framework, we found a positive concave-down relationship between GEDI-derived fuel structure and wildfire severity, marked by increasing severity with greater fuel loads until a decline in severity in the tallest and most voluminous forest canopies. Critically, indicators of canopy fuel volumes (like biomass and height) became decoupled from severity patterns in extreme topographic and weather conditions (slopes >20°; winds > 9.3 m/s). On the other hand, vertical continuity metrics like layering and ladder fuels more consistently predicted severity in extreme conditions – especially ladder fuels, where sparse understories were uniformly associated with lower severity levels. These results confirm that GEDI-derived fuel estimates can overcome limitations of optical imagery and airborne lidar for quantifying the interactive drivers of wildfire severity. Furthermore, these findings have direct implications for designing treatment interventions that target ladder fuels versus entire canopies and for delineating wildfire risk across topographic and weather conditions.