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Location Folder Show Tree ROOT _ScienceBase Catalog __Earth Resources Observation and Science Center (EROS) Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2023 Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2024 Evapotranspiration and Water Use Mapping Exotic annual grass (EAG) phenology estimates in the western U.S. rangelands based on 30-m HLS NDVI (ver. 2.0, April 2024) Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA, 2016 - 2023 (ver. 4.0, July 2024) More... Filters Date Range Past year (2) Past month (1) Past week (0) Past day (0) Custom range... To   Search Types Map Service (1) OGC WFS Layer (1) OGC WMS Layer (1) OGC WMS Service (1) Contacts Audra (Contractor) J Griebel U.S. Geological Survey Jesslyn Brown Earth Resources Observation and Science (EROS) Center Matthew B Rigge Jon Dewitz Danny Howard Tag Types Label (1) View Results as: JSON ATOM CSV Browse Folder

Rangeland Condition Monitoring Assessment and Projection (RCMAP) The USGS RCMAP (Rangeland Condition Monitoring Assessment and Projection) project has worked with BLM scientists and land managers to develop actionable remote-sensing based vegetation classifications. RCMAP quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2024. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, shrub height, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. The mapping area included eight regions which were subsequently... Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: RCMAP, back-in-time, climate change, grassland change, projections, All tags... rangeland, rangeland management, shrubland change, shrublands, time series, trends, vegetation change, Fewer tags USGS Land Cover The USGS Land Cover project has combined concepts and methodology from the legacy LCMAP and NLCD projects, along with modern deep learning convolutional neural networks, to produce promising prototypes of next generation land cover products. The new land cover algorithm will serve as the new baseline for USGS land cover production. Annual NLCD is a U.S. Geological Survey (USGS) science initiative implemented at the Earth Resources Observation and Science (EROS) Center that harnesses the remotely sensed Landsat data record to provide state-of-the-art land surface change information needed by scientists, resource managers, and decision-makers. Annual NLCD uses a modernized, integrated approach to map, monitor, synthesize,...