TreeMap 2020 CONUS: A tree-level model of the forests of the conterminous United States circa 2020

TreeMap 2020 CONUS provides a tree-level model of the forests of the conterminous United States. Throughout the conterminous United States (CONUS), for each forested pixel in 30×30 meter (m) gridded landscape data for circa 2020, we identified and assigned the most similar Forest Inventory and Analysis (FIA) plot. We used a Random Forest machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE) and Daymet (Daymet). Predictor variables consisted of percent forest cover, vegetation height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), climatic variables (precipitation, shortwave radiation, snow water equivalent, maximum temperature, minimum temperature, vapor pressure, and vapor pressure deficit), and disturbance history (time since disturbance and disturbance type). The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30×30 m spatial resolution for the conterminous U.S., corresponding to landscape conditions circa 2020. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster of plot identifiers can be linked to the FIA databases available through the FIA DataMart to map hundreds of attributes available there, or to the comma-separated file included in this data publication to access a more limited set of tree-level attributes. The data files included in this publication also contain attributes for each tree in the plots that were assigned, including the FIA plot PLT_CN for the plot on which the tree was measured (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, the corresponding number of trees per acre it represents due to the study design, the status (live or dead), species, diameter, height, actual height (where broken), crown ratio and a code for cause of death where applicable. Previous versions of TreeMap have been validated for characteristics including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, and snag hazard category. Previous versions of TreeMap are being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources.<br>Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. The TreeMap 2014 dataset (Riley et al. 2019) was the first of its kind to provide such detailed forest structure data across the forests of the conterminous United States. Prior to the TreeMap 2014 imputed forest data, assessments relied largely on forest inventory at fixed plot locations at sparse densities. The TreeMap 2016 dataset (Riley et al. 2021, Riley et al. 2022) updated the 2014 version to include disturbance as a response variable, which improved accuracy in disturbed areas. The TreeMap 2020 CONUS dataset featured here updates the TreeMap 2016 dataset to landscape conditions circa 2020 and updates the methods by 1) using a different suite of climate variables in the imputation and 2) improving species composition assignments to prevent plots being imputed to areas where their existing vegetation type was not present, an issue which affected a small number of pixels in previous TreeMap versions.<br>See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). For more information about these data, see Riley et al. (2022).<p></p>