This dataset provides seedling density and site characteristics for 131 plots in Whiskeytown National Recreation area in California, USA. Site characteristics include modeled seed availability and terrain indices calculated using a 1 meter resolution digital elevation model (DEM).

This data release provides forest structure information including basal area and stem density by genus and mortality status for overstory and sapling trees in 149 plots in Whiskeytown National Recreation area in California, USA. The data includes forest structure measurements taken before and after the 2018 Carr Fire, a nearly 93,000 hectare wildfire.

This data release provides forest structure information including basal area and stem density by genus and mortality status for overstory and sapling trees in 149 plots in Whiskeytown National Recreation area in California, USA. The data includes forest structure measurements taken before and after the 2018 Carr Fire, a nearly 93,000 hectare wildfire.

Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Raw aerial photography, orthorectified imagery, point cloud data, and digital elevation models (DEMs) for Whiskeytown National Recreation Area (NRA) following the Carr Fire. Sites within the NRA include: Lower Crystal Creek, Tower House, Grizzly Gulch, Boulder Creek South Shore and Conifer, Brandy Creek Camp, Shasta Divide, Paige Bar (North, NEED Camp, East, and Southeast), Chinese Laundry, and Coggins Park. Imagery was collected with two sensors (Ricoh GR II and MicaSense RedEdge) on a quadcopter flown at 400 feet above ground level immediately following the Carr Fire (October 2018) and 8-9 months after the fire (May and June 2019). Due to access, not all sites were flown during both collection periods. U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center UAS data is available from Earth Explorer. To access: 1) Log in to https://earthexplorer.usgs.gov 2) Search for imagery by downloading the KMZ file below and selecting it within the KML tab in the Search Criteria (on Earth Explorer). 3) Specify a date range if searching for imagery from a specific collection period. 4) Click on Data Sets and select UAS - Raw/Orttho/Point Cloud/DEM (desired imagery format). 5) Click on Results to view and download imagery.

This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain.

This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain.

This dataset includes polygons with a minimum of 40 acres of woodlands per square mile as depicted in William H. Brewer�s 1873 map of woodland density and covers the conterminous United States. Each polygon has been labeled with the density category (1-5) depicted on the original map. This dataset was created by georeferencing a scanned version of the source map and by heads-up digitizing each woodland density polygon.