Annual maps of forest and evergreen forest in the contiguous United States during 2015–2017 from analyses of PALSAR-2 and Landsat images

Abstract

Annual forest maps at a high spatial resolution are necessary for forest management and conservation. Large uncertainties remain in existing forest maps because of different forest definitions, satellite datasets, in situ training datasets, and mapping algorithms. In this study, we generated annual maps of forest and evergreen forest at a 30 $\mathrm{m}$ resolution in the contiguous United States (CONUS) during 2015–2017 by integrating microwave data (Phased Array type L-band Synthetic Aperture Radar – PALSAR-2) and optical data (Landsat) using knowledge-based algorithms. The resultant PALSAR-2/Landsat-based forest maps (PL-Forest) were compared with five major forest datasets from the CONUS: (1) the Landsat tree canopy cover from the Global Forest Watch dataset (GFW-Forest), (2) the Landsat Vegetation Continuous Field dataset (Landsat VCF-Forest), (3) the National Land Cover Database 2016 (NLCD-Forest), (4) the Japan Aerospace Exploration Agency forest maps (JAXA-Forest), and (5) the Forest Inventory and Analysis (FIA) data from the U.S. Department of Agriculture (USDA) Forest Service (FIA-Forest). The forest structure data (tree canopy height and canopy coverage) derived from the lidar observations of the Geoscience Laser Altimetry System (GLAS) on board NASA's Ice, Cloud, and land Elevation Satellite (ICESat-1) were used to assess the five forest cover datasets derived from satellite images. Using the forest definition of the Food and Agricultural Organization (FAO) of the United Nations, more forest pixels from the PL-Forest maps meet the FAO's forest definition than the GFW-Forest, Landsat VCF-Forest, and JAXA-Forest datasets. Forest area estimates from PL-Forest were close to those from the FIA-Forest statistics, higher than GFW-Forest and NLCD-Forest, and lower than Landsat VCF-Forest, which highlights the potential of using both the PL-Forest and FIA-Forest datasets to support the FAO's Global Forest Resources Assessment. Furthermore, the PALSAR-2/Landsat-based annual evergreen forest maps (PL-Evergreen Forest) showed reasonable consistency with the NLCD product. The comparison of the most widely used forest datasets offered insights to employ appropriate products for relevant research and management activities across local to regional and national scales. The datasets generated in this study are available at https://doi.org/10.6084/m9.figshare.21270261 (Wang, 2024). The improved annual maps of forest and evergreen forest at 30 $\mathrm{m}$ over the CONUS can be used to support forest management, conservation, and resource assessments.