Rangeland Condition Monitoring Assessment and Projection (RCMAP)¶

The Rangeland Condition Monitoring Assessment and Projection (RCMAP) dataset quantifies the percent cover of rangeland components across western North America 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, tree, and shrub height; in addition to the temporal trends of each component.

Several enhancements were made to the RCMAP process relative to prior generations:

1. The training database was expanded by incorporating new observations from the BLM Analysis Inventory and Monitoring (AIM) dataset, the LANDFIRE public reference database, and additional Landsat scale observations collected by RCMAP
2. New high-resolution training sites were added in the Northern Rockies, Pacific Northwest, and Northern Great Plains
3. High-resolution sites were subjected to a new screening approach which removes areas of topographic shadows and other likely noise from the training pool
4. The change detection methodology was removed to create more temporally dynamic predictions
5. Additional independent variables were added to the Landsat scale models including time-since most recent fire and 3 by 3 focal variance of all predictor variables
6. The project's study area was expanded to include the grasslands biome as defined by the U.S. Fish and Wildlife Service Central Grassland Roadmap, which expanded the study area by 1,830,783 km²

RCMAP data are useful to natural resource decision making including understanding drought impact, invasive species, fire risk, and livestock management.

Available Datasets¶

The RCMAP v7 product suite includes both cover data and time-series trends analysis. The following table shows the current availability status:

Dataset Details¶

Rangeland Components¶

RCMAP Time-Series Trends Analysis¶

The RCMAP product suite assesses temporal patterns in each component using two approaches: 1. linear trends and 2. a breaks and stable states method with a temporal moving window based on structural change at the pixel level.

Linear trend products include Theil-Sen slope, which features improved estimation of trend given outliers compared to the least squares method. The slope represents the average percent cover change per year over the time-series.

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. The break point trends analysis suite produces the following statistics:

1. Break Point Presence/Absence - Presence/absence of structural breaks in each component each year
2. Break Point Count - Number of structural breaks observed in the time-series
3. Slope of Segment - Slope of structural breaks segment (% change/year × 100)
4. Total Change Intensity Index - Derivative index highlighting total change across primary components
5. Year of Most Recent Break - Year of most recent break in the time-series for each component
6. Slope of Linear Model - Theil-Sen slope of linear trends model (% change/year × 100)

Data Access¶

Component products can be downloaded at the MRLC website and are available in an interactive viewer: MRLC Rangeland Viewer. Additionally, data are available as WMS/WCS services through the MRLC Data Services Page.

Citation¶

Rigge, M.B., Bunde, B., and Postma, K., 2025, Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across Western North America from 1985-2024
U.S. Geological Survey data release, https://doi.org/10.5066/P13QF8HT

Earth Engine Snippet¶

var rcmap_cover = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES/COVER");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/RCMAP-COVER

Break Point Presence Data (1985-2024)¶

var bp_annual_herbaceous = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/ANNUAL_HERBACEOUS");
var bp_bare_ground = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/BARE_GROUND");
var bp_herbaceous = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/HERBACEOUS");
var bp_litter = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/LITTER");
var bp_non_sagebrush_shrub = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/NON_SAGEBRUSH_SHRUB");
var bp_perennial_herbaceous = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/PERENNIAL_HERBACEOUS");
var bp_sagebrush = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/SAGEBRUSH");
var bp_shrub = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/SHRUB");
var bp_shrub_height = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/SHRUB_HEIGHT");
var bp_tree = ee.ImageCollection("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAKPOINT_PRESENCE_ANNUAL/TREE");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/RCMAP-BREAK-POINT

Break Point Count Data¶

var breakpoint_count = ee.Image("projects/sat-io/open-datasets/USGS/RCMAP/V7/TIME_SERIES_TRENDS/BREAK_POINT_COUNT");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/RCMAP-BREAK-POINT-COUNT

License¶

This work is marked with CC0 1.0 Universal. You are free to copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

Created by: Matthew B. Rigge, Brett Bunde, Kory Postma, and collaborators at USGS Earth Resources Observation and Science (EROS) Center

Curated in GEE by: Samapriya Roy

Keywords: Rangeland, USGS, Google Earth Engine, Landsat, Vegetation mapping, Fractional cover, Time series, Natural resources, Land management, Western North America

Last updated: 2025-06-03

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