Data release for tracking rates of post-fire conifer regeneration distinct from deciduous vegetation recovery across the western U.S.
Dates
Publication Date
2020-10-13
Start Date
1984-03-01
End Date
2018-12-31
Citation
Vanderhoof, M.K., Hawbaker, T.J., Ku, A.M., Merriam, K.E., Berryman, E.M., and Cattau, M., 2020, Data release for tracking rates of post-fire conifer regeneration distinct from deciduous vegetation recovery across the western U.S.: U.S. Geological Survey data release, https://doi.org/10.5066/P9TD78FW.
Summary
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 [...]
Summary
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.
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Related External Resources
Type: Related Primary Publication
Vanderhoof, M.K., Hawbaker, T.J., Ku, A., Merriam, K., Berryman, E., and Cattau, M., 2020, Tracking rates of postfire conifer regeneration vs. deciduous vegetation recovery across the western United States: Ecological Applications, v. 31, no. 2, https://doi.org/10.1002/eap.2237.
The purpose of this study was to quantify variability in the rates of Normalized Difference Vegetation Index (NDVI or greenness) recovery across the western United States and to test for drivers that might explain this variability. Identifying areas across the western United States with less regeneration than expected or a decline in the rate of regeneration over time is essential for detecting reductions in forest resiliency, impacts of climate change and potential state-transitions in ecosystem types. We focused on fires in forests where (1) the pre-fire overstory was dominated by conifer species, (2) snow cover is consistently present in the winter months, and (3) we had a minimum of 10 years of post-fire Landsat satellite data.
