Rocky Mountain Research Station

Emerging remote sensing technologies are revolutionizing our ability to monitor biodiversity and ecosystem function, particularly across fire-prone and drought-stressed landscapes. Leveraging 12 years of GPS telemetry from over 3,000 individuals across three mammalian trophic groups—herbivorous mule deer (Odocoileus hemionus), omnivorous black bears (Ursus americanus), and carnivorous cougars (Puma concolor)—and integrating satellite-derived vegetation indices, burn severity maps, and habitat suitability models, we quantify how fire and drought drive changes in the spatial distribution, quality, and demographic outcomes of large mammal habitats in the American West.Our first study explores the role of wildland fires, including both high-severity wildfires and prescribed fires, in shaping large mammal habitat quality and selection. We integrated GPS and remote sensing data to quantify how fire characteristics (extent, severity, age, vegetation recovery) influence resource use, showing substantial habitat overlap with burned landscapes. Herbivores and omnivores showed improved or neutral habitat quality in low-severity or small burns, driven by increased forage and cover, but consistently avoided large, severe wildfires, which diminished habitat quality for up to 30 years post-fire. Machine learning algorithms helped distinguish the nuanced effects of fire at broad scales, revealing pathways to optimize fire management for biodiversity outcomes.Our second analysis addresses the mounting threat of extreme drought. We examined how annual drought conditions, quantified with high-resolution remote sensing of vegetation productivity and biomass, cause widespread contractions in highly suitable habitats for all three species. Severe droughts reduced suitable habitat by 10–18%, with the greatest impacts observed for upper trophic levels. Critically, we linked broad-scale predictions to demographic shifts, finding that mule deer fawn recruitment dropped by over 34% during severe drought years. These findings underscore the necessity of integrating multi-source remote sensing products, ecological theory, and in-situ fitness data to scale biodiversity monitoring from individual movement to population viability, thereby informing adaptive management under the Kunming-Montreal Biodiversity Framework and SDG targets.Our work demonstrates that next-generation Earth observation combined with advanced ecological modeling is essential to anticipate, monitor, and mitigate the synergistic impacts of fire and drought on wildlife and ecosystem resilience at landscape scales.

This archive contains research data collected and/or funded by Forest Service Research and Development (FS R&D), U.S. Department of Agriculture. It is a resource for accessing both short and long-term FS R&D research data, which includes Experimental Forest and Range data. It is a way to both preserve and share the quality science of our researchers.

The Convention on Biological Diversity's Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals to protect and restore biodiversity. It includes a monitoring framework that mandates countries to track progress toward these goals using indicators that summarize biodiversity trends. Calculating indicators is challenging for countries because of fragmented biodiversity monitoring efforts, technical barriers, a lack of available data and tools, and capacity bottlenecks. The BON in a Box platform for biodiversity monitoring and indicator calculation, developed by the Group on Earth Observations Biodiversity Observation Network, was created to address these challenges by providing open, transparent, and reproducible analysis pipelines that convert data into essential biodiversity variables and indicators. These pipelines are built by experts and contributed by the community, follow FAIR principles, and help scientists apply their research to coordinate biodiversity monitoring efforts, build capacity to track progress toward the GBF, and affect policy change.

This archive contains research data collected and/or funded by Forest Service Research and Development (FS R&D), U.S. Department of Agriculture. It is a resource for accessing both short and long-term FS R&D research data, which includes Experimental Forest and Range data. It is a way to both preserve and share the quality science of our researchers.

The extension of NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission beyond the cryosphere to include the study of vegetation and the land surface has significantly advanced global Earth observation. This review synthesizes findings from 293 peer-reviewed articles and provides a comprehensive assessment of the mission’s contributions to terrestrial ecosystem science. We begin by outlining the mission’s objectives, instrumentation, and core data products—particularly ATL03 (geolocated photons) and ATL08 (terrain and canopy heights)—which have supported a wide array of applications in forestry, terrain analysis, and environmental monitoring. Our analysis shows that most studies focus on temperate and tropical broadleaf forests, with a strong emphasis on estimating canopy height, terrain elevation, and forest structure. Terrain metrics derived from ICESat-2 products typically achieve high accuracy, while vegetation variables, such as canopy height, cover, leaf area index (LAI), and aboveground biomass, demonstrate moderate to strong accuracies across biomes. While parametric models remain the most used approach, machine learning methods are expanding, and more than one third of the reviewed literature incorporates synergistic analyses with other satellite missions. Despite its versatility, ICESat-2 remains underutilized in several key domains—including nonforest vegetation, fire ecology, wildlife habitat assessment, urban monitoring, and disturbance detection—mainly due to the scarcity of standardized algorithms and validated reference datasets. However, the growing availability of open source processing tools presents a significant opportunity for expanding the user base and fostering innovation in these emerging areas. Future advancements, such as the forthcoming ATL18 gridded canopy height and terrain products, alongside synergies with missions like the European Space Agency (ESA)’s BIOMASS and the NASA–Indian Space Research Organisation (ISRO) Synthetic Aperture Radar (NISAR), promise to enhance multisensor integration and ecosystem monitoring. Overall, ICESat-2 continues to evolve as a powerful resource for characterizing vegetation structure, ecosystem dynamics, and land surface processes on a global scale.

Background Soil and stream nitrate (NO3−) concentrations often increase after severe fire from elevated nitrogen (N) mineralization and reduced plant uptake. However, it is unclear how long these effects persist and contribute to stream N export. Aims We examined the contribution of soil N supply and vegetation N demand to 19-fold higher stream NO3− export that has persisted since the 2002 Hayman Fire in Colorado, USA. Methods We compared soil N pools, inorganic N production, subsurface (0–100 cm) NO3− concentrations, vegetation cover, productivity and N demand 17 years post-fire. We sampled along burned and unburned hillslopes to evaluate whether near-stream vegetation and soils attenuated N loss during downslope transport. Key results Mineral soil, leachate and groundwater NO3− concentrations were higher in burned than unburned hillslopes, despite similar mineralization rates. Burned uplands showed 62% lower productivity and 28% lower N demand relative to unburned forests. Riparian recovery exceeded uplands but remained incomplete relative to unburned conditions. Burned uplands acted as N sources, with slight reductions in soil NO3− in downslope riparian soils. Conclusions Sustained NO3− export was driven by reduced vegetation N demand and subsurface transport, not increased mineralization. Implications Revegetation of severely burned uplands and riparian zones may enhance long-term N retention.