Mature and Old-growth Forest Probability Maps for the Conterminous United States

This dataset contains maps of succession-related forest classes across the conterminous US: mature-forest, old-growth-forest, other-forest, and non-forest. The three forest classes have also been combined into an all-forest class. The first map type is a predicted, class presence probability map at 1-ha spatial resolution (the native mapping resolution of this study). The second map type is an aggregated set of class proportion maps at 5-km (25-km2) resolution, representing the proportions of total land area and the proportions of forest land area, for the mature-forest, old-growth-forest, and all-forest classes. To derive these data, a spatial Bayesian modeling framework was developed that integrated U.S. Forest Inventory and Analysis (FIA) plot-level mature and old-growth (MOG) definitions with a stack of data layers derived from both active and passive satellite remote sensing platforms. To reduce spatial heterogeneity in predictor response relationships, CONUS was segmented into 29 strata based on forest composition and U.S. Environmental Protection Agency Level II and III ecoregions, while trying to maintain at least 100 plots in each class within any given stratum. Quadratic discriminant analysis (QDA) models were calibrated and applied independently in each stratum, with the FIA plot MOG labels as the response variable and the remote sensing data as predictors. The maps represent the state of U.S. forests circa 2022, the year that best matches the range of dates used for the remote sensing data, although the inventory data span slightly a wider and earlier range of years of data collection. The data are provided in cloud optimized GeoTIFF (COG) format.