15 datasets found

Calculates fire regime deviations based on Safford et al.'s methodology, using historic presettlement averages for comparison.

Quantifies fire regime deviations using advanced statistical testing, accounting for historical variability across 17 western U.S. states.

Highlights statistically significant fire regime deviations, masking areas with p-values ≤ 2.5% or ≥ 97.5%.

Probability of human-caused wildfire ignitions across the western United States.

Probability of naturally caused wildfire ignitions across the southeastern United States.

Probability of human-caused wildfire ignitions across the southeastern United States.

Probability of naturally caused wildfire ignitions across the western United States.

Combined probability of wildfire ignitions from both human and natural (lightning) causes across western landscapes.

Combined likelihood of wildfire ignitions from human and natural (lightning) causes in southeastern ecosystems.

Tracks the total number of fires per pixel from 1950 to 2023, categorized by severity.

Tracks the total number of fires per pixel from 1950 to 2023, categorized by severity.

This project is a collection of the test dataset for reproducing the analysis from the VibrantVS model manuscript. The test dataset contains paired "NAIP", aerial "lidar CHM",...

LDSim's GPU data layer models vegetation patterns, fire return intervals, and ecological departures at fine scales (~50 acres) to inform site-level restoration and management...

/subbasinResults.zip • zone.tif = geotiff raster depicting zones (unique combinations of Subbasins and biophysical class (BPC); used as the landscape partition for quantifying...

LDSim's BPU data layer categorizes site productivity into biophysical classes, mapping historical tree biomass and ecological departures for regional analysis.