Post-fire vegetation status and condition have multiple implications. They are indicative of burn severity and the lasting impacts of fire the land; they also help inform post-fire debris flow modeling and related risk analyses, hydrology and water quality assessments, and vulnerability to invasive species. Monitoring vegetation recovery over time enables continuous re-evaluation of various post-fire hazards, thereby facilitating informed and timely responses to post-fire risks by land managers at the local level. Structure metrics were derived from spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar data and used to map pre- and post-fire structure. Pre- and post-fire Landsat or Sentinel satellite data were obtained from the Monitoring Trends in Burn Severity (MTBS; https://www.mtbs.gov/) program. GEDI data were intersected with each satellite band and XGBoost models were built using band values as independent variables and GEDI vegetation structure values as dependent values. The models were used to generate spatially continuous maps of structure, providing vegetation structural estimates throughout the fire perimeter and beyond.

Post-fire vegetation status and condition have multiple implications. They are indicative of burn severity and the lasting impacts of fire the land; they also help inform post-fire debris flow modeling and related risk analyses, hydrology and water quality assessments, and vulnerability to invasive species. Monitoring vegetation recovery over time enables continuous re-evaluation of various post-fire hazards, thereby facilitating informed and timely responses to post-fire risks by land managers at the local level. Structure metrics were derived from spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar data and used to map pre- and post-fire structure. Pre- and post-fire Landsat or Sentinel satellite data were obtained from the Monitoring Trends in Burn Severity (MTBS; https://www.mtbs.gov/) program. GEDI data were intersected with each satellite band and XGBoost models were built using band values as independent variables and GEDI vegetation structure values as dependent values. The models were used to generate spatially continuous maps of structure, providing vegetation structural estimates throughout the fire perimeter and beyond.

Post-fire vegetation status and condition have multiple implications. They are indicative of burn severity and the lasting impacts of fire the land; they also help inform post-fire debris flow modeling and related risk analyses, hydrology and water quality assessments, and vulnerability to invasive species. Monitoring vegetation recovery over time enables continuous re-evaluation of various post-fire hazards, thereby facilitating informed and timely responses to post-fire risks by land managers at the local level. Structure metrics were derived from spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar data and used to map pre- and post-fire structure. Pre- and post-fire Landsat or Sentinel satellite data were obtained from the Monitoring Trends in Burn Severity (MTBS; https://www.mtbs.gov/) program. GEDI data were intersected with each satellite band and XGBoost models were built using band values as independent variables and GEDI vegetation structure values as dependent values. The models were used to generate spatially continuous maps of structure, providing vegetation structural estimates throughout the fire perimeter and beyond.

Post-fire vegetation status and condition have multiple implications. They are indicative of burn severity and the lasting impacts of fire the land; they also help inform post-fire debris flow modeling and related risk analyses, hydrology and water quality assessments, and vulnerability to invasive species. Monitoring vegetation recovery over time enables continuous re-evaluation of various post-fire hazards, thereby facilitating informed and timely responses to post-fire risks by land managers at the local level. Structure metrics were derived from spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar data and used to map pre- and post-fire structure. Pre- and post-fire Landsat or Sentinel satellite data were obtained from the Monitoring Trends in Burn Severity (MTBS; https://www.mtbs.gov/) program. GEDI data were intersected with each satellite band and XGBoost models were built using band values as independent variables and GEDI vegetation structure values as dependent values. The models were used to generate spatially continuous maps of structure, providing vegetation structural estimates throughout the fire perimeter and beyond.

Post-fire vegetation status and condition have multiple implications. They are indicative of burn severity and the lasting impacts of fire the land; they also help inform post-fire debris flow modeling and related risk analyses, hydrology and water quality assessments, and vulnerability to invasive species. Monitoring vegetation recovery over time enables continuous re-evaluation of various post-fire hazards, thereby facilitating informed and timely responses to post-fire risks by land managers at the local level. Structure metrics were derived from spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar data and used to map pre- and post-fire structure. Pre- and post-fire Landsat or Sentinel satellite data were obtained from the Monitoring Trends in Burn Severity (MTBS; https://www.mtbs.gov/) program. GEDI data were intersected with each satellite band and XGBoost models were built using band values as independent variables and GEDI vegetation structure values as dependent values. The models were used to generate spatially continuous maps of structure, providing vegetation structural estimates throughout the fire perimeter and beyond.

Post-fire vegetation status and condition have multiple implications. They are indicative of burn severity and the lasting impacts of fire the land; they also help inform post-fire debris flow modeling and related risk analyses, hydrology and water quality assessments, and vulnerability to invasive species. Monitoring vegetation recovery over time enables continuous re-evaluation of various post-fire hazards, thereby facilitating informed and timely responses to post-fire risks by land managers at the local level. Structure metrics were derived from spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar data and used to map pre- and post-fire structure. Pre- and post-fire Landsat or Sentinel satellite data were obtained from the Monitoring Trends in Burn Severity (MTBS; https://www.mtbs.gov/) program. GEDI data were intersected with each satellite band and XGBoost models were built using band values as independent variables and GEDI vegetation structure values as dependent values. The models were used to generate spatially continuous maps of structure, providing vegetation structural estimates throughout the fire perimeter and beyond.

Land management treatments in sagebrush steppe are an important opportunity to break the annual-grass fire cycle, provided they offer long-lasting resistance to annual-grass invasion and do not burn. However, for BLM areas seeded as part of the Emergency Stabilization and Rehabilitation (ESR) program, one of the largest programs for land management treatments, about 1/4 have at least partially reburned over the last 30 years, according to a recent study. Reburning of treatments can cause a loss of investment if fire-intolerant perennials do not recover and/or significant invasions occur, in which case the risks of wildfire are compounded by increased potential for ecological degradation. Alternatively, recovery of fire-tolerant perennials occurs naturally or due to treatments would represent a significant return on prior investment and the occurrence of fire would thus pose reduced ecological hazard risks. Fire risks are highly variable across sagebrush landscapes, owing to variability in fuel loading, ignition potential, and fire transmission. Information is needed on predicting future risks related to reburning - including post-fire hazards related to ecological degradation - for past land management investments to a) identify protection measures that could be applied now, and b) help design and positioning of future treatment investments to minimize their risk of reburning in ways that cause ecological degradation. This dataset was compiled in order to predict reburn risk to areas that had previously burned and were retreated.

Post-fire vegetation status and condition have multiple implications. They are indicative of burn severity and the lasting impacts of fire the land; they also help inform post-fire debris flow modeling and related risk analyses, hydrology and water quality assessments, and vulnerability to invasive species. Monitoring vegetation recovery over time enables continuous re-evaluation of various post-fire hazards, thereby facilitating informed and timely responses to post-fire risks by land managers at the local level. Structure metrics were derived from spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar data and used to map pre- and post-fire structure. Pre- and post-fire Landsat or Sentinel satellite data were obtained from the Monitoring Trends in Burn Severity (MTBS; https://www.mtbs.gov/) program. GEDI data were intersected with each satellite band and XGBoost models were built using band values as independent variables and GEDI vegetation structure values as dependent values. The models were used to generate spatially continuous maps of structure, providing vegetation structural estimates throughout the fire perimeter and beyond.

A raster identifying previously burned areas as being 1) recovered (to sagebrush-dominant ecosystem), 2) recovering, or 3) transitioned to annual grass-dominated.

A raster identifying previously burned areas as being 1) recovered (to sagebrush-dominant ecosystem), 2) recovering, or 3) transitioned to annual grass-dominated.