Title:

Field and plot data for 2017-2018 wildland fires in the southwestern United States collected for Rapid Assessment of Vegetation Condition after Wildfire (RAVG) models: burn severity and stand characteristics

Author(s):

Reiner, Alicia L.; Baker, Craig R.; Wahlberg, Maximillian M.

Publication Year:

2022

How to Cite:

These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Reiner, Alicia L.; Baker, Craig R.; Wahlberg, Maximillian M. 2022. Field and plot data for 2017-2018 wildland fires in the southwestern United States collected for Rapid Assessment of Vegetation Condition after Wildfire (RAVG) models: burn severity and stand characteristics. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2022-0018

Abstract:

These data were collected to develop southwest U.S. regionally-specific fire effects models for use in the Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program at the USDA Forest Service Geospatial Technology and Applications Center (GTAC). The data presented here are the training data associated with the response variables in the model. These data were collected in both Arizona and New Mexico from March 2018 through June 2019 using two different methods: field plots and photographic interpretation. These data include plot latitude and longitude along with post-processed burn severity and stand characteristics variables derived from plot data. Data also have potential predictor variables such topographic, ecological, and satellite-imagery-derived variables attributed to the plot locations. The Composite Burn Index (CBI) is the variable in these data used to document burn severity. The main variables which describe forest structure change with fire are the change in basal area and the change in canopy cover, which were both derived by processing plot tree measurements through the Forest Vegetation Simulator (FVS), as well as from Photographic Interpretation (PI) data, which involves an analyst assigning canopy cover yes/no to a grid of points overlaid on pre- and post-fire aerial photos.

Keywords:

fire effects; composite burn index; CBI; burn severity; basal area; canopy cover; RAVG; satellite indices; biota; environment; Ecology, Ecosystems, & Environment; Fire; Fire ecology; Fire effects on environment; Inventory, Monitoring, & Analysis; Natural Resource Management & Use; Forest management; Arizona; New Mexico; southwest United States

Related publications:

• Reiner, Alicia L.; Baker, Craig R.; Wahlberg, Maximillian M. 2022. Geospatial data for 2017-2018 wildland fires in the southwestern United States used for region-specific Rapid Assessment of Vegetation Condition after Wildfire (RAVG) models: burned area boundaries and burn indices derived from Landsat and Sentinel-2 satellite imagery. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2022-0019
• Reiner, Alicia L.; Baker, Craig R.; Wahlberg, Maxillian M.; Rau, Benjamin M.; Birch, Joseph D. 2022. Region-specific remote-sensing models for predicting burn severity, basal area change, and canopy cover change following fire in the southwestern United States. Fire. 5: 137. https://doi.org/10.3390/fire5050137

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