Multi-source geospatial dataset supporting random forest land cover classification in Alabama Wildlife Management Areas from 2008 to 2024

This dataset contains geospatial data compiled and generated to support supervised land cover classification and habitat monitoring across nine Wildlife Management Areas (WMAs) in Alabama. The dataset integrates Landsat 9 surface reflectance imagery acquired between May and July 2024, the 2021 National Land Cover Database (NLCD), NLCD-derived Tree Canopy Cover (TCC), a stacked predictor raster, and field-collected training and validation data. Landsat imagery was used to derive spectral bands, vegetation indices, and texture metrics that were combined into a multiband predictor raster stack used as input to a machine learning classification model. The NLCD and TCC layers were incorporated as additional environmental predictors and for comparison with model outputs. Field data were collected within WMAs to provide labeled training and independent validation samples representing major land cover classes. All raster datasets were reprojected to a common coordinate system, resampled to a consistent spatial resolution, and spatially aligned prior to stacking. The predictor stack represents the final set of variables used in model training and prediction. Wildlife Management Area boundary shapefiles were used to define the spatial extent of all analyses. This dataset supports reproducible land cover classification workflows and landscape-scale ecological analysis across protected lands in Alabama.