Authors: Arvind Bhuta*, U.S. Forest Service, Andrew Evans, Texas A&M, College of Geosciences, Pablo Juarbe-Martinez, Geological Society of America, GeoCorps America , Gabriel Seidman, Geological Society of America, GeoCorps America
Topics: Biogeography, Physical Geography, Spatial Analysis & Modeling
Keywords: National Forests, LANDFIRE, longleaf pine, ecosystems, Forest Service, potential vegetation, existing vegetation, biogeography
Session Type: Poster
Start / End Time: 3:05 PM / 4:45 PM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
We used the most recent LANDFIRE (LF) dataset for fine-scale ecological condition modeling of longleaf pine forests across the National Forests in the southeastern United States. LF provides federal agencies with a common set of vegetation and wildland fire/fuels information for strategic fire/resource management planning/analysis and are derived from Landsat imagery, plot data, and biophysical gradient modeling mapped across the entire country at 30 m resolution. It is updated every two years to reflect landscape-scale disturbance and change. We extracted vegetation (site potential, existing vegetation type, height, & cover), fire regime (fire regime groups, MFRI, severity, succession, and condition); fuel (fuel characteristic classifications, behavior models, & forest canopy data); disturbance (fuel & vegetation disturbance and transition models); and topography (aspect, slope, and elevation) from LF and conducted a decision tree classification analysis. We wished to evaluate the ecological conditions for the presence of longleaf pine across the forest matrix, determined the loss of longleaf pine when comparing existing vegetation to potential vegetation, and compared our data to a prior assessment of ecological condition for the National Forests in Alabama. We found that when compared to the previous model using SSURGO and field collected stand level data that LF data was unsuitable for fine scale modeling, but could be used at landscape scales to determine pre- and post-disturbance events, bridging gaps in stand data, and for the identification, analysis, and prioritization of future restoration efforts across longleaf pine ecosystems that comprise the National Forests in the southeast.