The U.S. Geological Survey (USGS), in association with the Multi-Resolution Land Characteristics (MRLC) Consortium, is pleased to announce the completion and release of the latest epoch of the National Land Cover Database (NLCD) for the conterminous U.S.—NLCD 2021. The MRLC, a consortium of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications, have been providing the scientific community with detailed land cover products for more than 30 years. Over that time, NLCD has been one of the most widely used geospatial datasets in the U.S., serving as a basis for understanding the Nation’s landscapes in thousands of studies and applications, trusted by scientists, land managers, students, city planners, and many more as a definitive source of U.S. land cover.
RCMAP 1985-2021 Fractional Component Time-Series Now Available
Published Dec 01, 2022
The U.S. Geological Survey (USGS), in collaboration with the MRLC consortium and Bureau of Land Management (BLM), is pleased to announce the availability of a new generation of Rangeland, Condition, Monitoring, Assessment, and Projection (RCMAP) fractional component data spanning a 1985-2021 time-series. The new time-series includes yearly cover predictions for 9 components: shrub, sagebrush, non-sagebrush shrub, herbaceous, annual herbaceous, perennial herbaceous, litter, bare ground, and new to this version, tree cover. Trends statistics of the new time-series will be available by Jan 2023. Data are available for download and on the rangelands viewer. The new generation of data update previously released 1985-2020 RCMAP data and are not designed to be backwards compatible (1985-2020 cover predictions are different between versions). While users are encouraged to use the newest generation of data, the previous versions of the time-series data are archived.
RCMAP has released temporal trends for the 1985-2020 fractional component cover time-series. Temporal patterns in each RCMAP component were evaluated with two approaches, 1) linear trends and 2) a structural change method using a temporal moving window to detect “breaks” and “stable states” in pixel values. The structural change method partitions the time-series into segments of similar slope values, with statistically significant break-points indicating perturbations to the prior trajectory. We provide the following statistics: 1) for each component, each year, the presence/absence of breaks, 2) the slope, p-value, and standard error of the segment occurring in each year, 3) the overall model R2 (quality of model fit to the temporal profile), and 4) an index, Total Change Intensity, reflecting the total amount of change occurring across components in that pixel.