This data set provides global forest area, forest growing stock, and forest biomass data at 1-degree resolution for the period 1950-2010. The data set is based on a compilation of forest area and growing stock data reported in international assessments performed by FAO, MCPFE (now Forest Europe), and UNECE. Data of different assessments are to the extent possible harmonized to reflect both forest area and other wooded land, to be comparable between countries and assessments.

The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.

This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB.

This data set, NPP Multi-Biome: Summary Data from Intensive Studies at 125 Sites, 1936-2006, contains a single shapefile that provides site-level summary statistics from 125 sites in five biomes: boreal forest, grassland, temperate forest, tropical forest, and tundra. The spatial coverage is global and spans the time period from 1936 through 2006. Study periods, and both spatial and temporal resolution vary by site. Data include georeferenced location, elevation, mean annual precipitation, mean annual minimum and maximum air temperature, dominant soil type, ecoregion type, dominant plant species, general vegetation types, annual mean or peak living above- and below-ground biomass, average annual above- and below-ground Net Primary Productivity (NPP), and reference information. Additionally study sampling period and intervals, plot management, and long-term site management history are also provided.

This data set provides the results of time-series analyses of Landsat imagery for 55 selected forested sites across the conterminous U.S.A. The output is a pair of disturbance data products for each site, one showing the first year of disturbance in the time series, the other showing the last year of disturbance. Each data pixel is labeled as either a static land class (persistent non-forest, persistent forest, or persistent water) or with the year of change for disturbed forest pixels. The time period analyzed is approximately 1984-2009.These forest disturbance data are distributed as a single band GeoTiff, with appropriate projection information defined within the file. The analyses were performed in three phases: 5 sites during the Prototype/Focal phase; 23 sites in Phase I; and 27 sites in Phase II. The spatial resolution of the Prototype/Focal and Phase I data is 28.5 meters. The spatial resolution of the Phase II data is 30 meters. The temporal resolution is nominally biennial. The mapped area for each forested site is approximately 185 km x 185 km. There are a total of 110 GeoTiff files - a first year and a last year disturbance file for each of the 55 sites.

This dataset contains model predictions of soil erosion and soil organic carbon (SOC) redistribution caused by agricultural practices such as tillage erosion. Soil erosion diminishes agricultural productivity by driving the loss of SOC. This model addresses a growing need to predict soil organic carbon transport, loss, and deposition. The model was applied to three sites containing paired prairie grassland and field plots in Iowa, and predicts SOC redistribution between 1859 to 2019. The model was developed by incorporating a SOC mixing model with a landscape evolution model that simulates tillage erosion.

This data set provides a soil map with estimates of soil carbon (C) in g C/m2 for 20-cm layers from the surface to one meter depth for the conterminous United States.STATSGO v.1 (State Soil Geographic Database, Soil Survey Staff, 1994) data were used to estimate by 20-cm intervals to a 1-m depth the mean soil carbon for each of the STATSGO-delineated soil map units. These map units are the polygons represented in the provided Shapefile data product.

This dataset contains annual estimates of carbon stocks, fluxes, and productivity over forested land in 11 states of the western USA (Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming). The estimates were produced from multiple simulations using the Community Land Model (v4.5) with different climate forcing data and prescribed harvest rates. Business as usual (BAU) scenarios were run to ensure that the simulations represented present-day stand ages. The estimates span two modeled time periods, 1979-2014 and 2015-2099, at 1/24-degree (4 km x 4 km) resolution. Variables included are gross primary production (GPP), net ecosystem exchange (NEE), net ecosystem productivity (NEP), net primary production (NPP), autotrophic respiration (RA), heterotrophic respiration (RH), transpiration factor, aboveground live tree carbon, carbon loss from fire, allocation to stem carbon, and burned area fraction over forested areas of the western USA.

This data set contains standardized gridded observation data, terrestrial biospheric model output, and inverse model simulations that were compiled but not used in the North American Carbon Program (NACP) Regional Synthesis activities, thus the supplemental designation. The data set provides six (6) observation data packages (9 variables - MODIS LAI, MODIS FPAR, MODIS NDVI, MODIS EVI, FIA forest biomass, forest area, GPP Anomaly, NEE Anomaly, Reco Anomaly; 8 data files), output results from three terrestrial biosphere models (TBM) (14 variables; 214 files), and simulations from one inverse model (IM) (one variable; 1 file). To produce this data set, the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) original data files were resampled to 1-degree spatial resolution for North American region (except for FIA Forest Biomass which was resampled to 0.5-degree resolution), interpolated into monthly or yearly temporal resolution, and reformatted into Climate and Forecast (CF) convention compatible netCDF format.

This data set provides forest age map products at 1-km resolution for Canada and the United States (U.S.A.). These continental forest age maps were compiled from forest inventory data, historical fire data, optical satellite data, and the images from the NASA Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project. These input data products have various sources and creation dates as described in the source paper by Pan et al. (2011). Canadian maps were produced with data available through 2004 and U.S.A. maps with data available through 2006. A supplementary map of the standard deviations for age estimates was developed for quantifying uncertainty.Note that the Pan et al. (2011) paper is included as a companion file with this data set and was the source of descriptions in the guide.Forest age, implicitly reflecting the past disturbance legacy, is a simple and direct surrogate for the time since disturbance and may be used in various forest carbon analyses that concern the impact of disturbances. By combining geographic information about forest age with estimated carbon dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status.