- Dataset Availability
- 1985-01-01T00:00:00Z–2023-12-31T00:00:00Z
- Dataset Provider
- United States Geological Survey and Bureau of Land Management
- Earth Engine Snippet
-
ee.Image("USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS") - Tags
Description
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. 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. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was further improved by incorporating additional datasets. Next, the Landsat compositing approach was improved to better capture the range of conditions from across each year and through time. These composites are based on Collection 2 Landsat data with improved geolocation accuracy and dynamic range. Finally, the Canadian portion of the sagebrush biome was included, which expanded the study area by 29,199 km2.
Processing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded at Multi-Resolution Land Characteristics Consortium.
The temporal patterns were assessed in each RCMAP component with two approaches, 1) linear trends and 2) a breaks and stable states method with an 8-year temporal moving window based on structural change at the pixel level. Linear trend products include slope and p-value calculated from least squares linear regression. The slope represents the average percent cover change per year over the times-series and the p-value reflects the confidence of change in each pixel. The structural change method partitions the time-series into segments of similar slope values, with statistically significant breakpoints indicating perturbations to the prior trajectory. The break point trends analysis suite relies on structural break methods, resulting in the identification of the number and timing of breaks in the time-series, and the significance of each segment. The following statistics were produced: 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. This index reflects the total amount of change occurring across components in that pixel. The linear and structural change methods generally agreed on patterns of change, but the latter found breaks more often, with at least one break point in most pixels. The structural change model provides more robust statistics on the significant minority of pixels with non-monotonic trends, while detrending some interannual signal potentially superfluous from a long-term perspective.
Bands
Resolution
30 meters
Bands
| Name | Units | Min | Max | Scale | Description |
|---|---|---|---|---|---|
annual_herbaceous_break_point |
count | 0 | 3 | Number of structural breaks observed in the annual herbaceous time series |
|
bare_ground_break_point |
count | 0 | 3 | Number of structural breaks observed in the bare ground time series |
|
herbaceous_break_point |
count | 0 | 3 | Number of structural breaks observed in the herbaceous time series |
|
litter_break_point |
count | 0 | 3 | Number of structural breaks observed in the litter time series |
|
sagebrush_break_point |
count | 0 | 3 | Number of structural breaks observed in the sagebrush time series |
|
shrub_break_point |
count | 0 | 3 | Number of structural breaks observed in the shrub time series |
|
shrub_height_break_point |
count | 0 | 3 | Number of structural breaks observed in the shrub height time series |
|
non_sagebrush_shrub_break_point |
count | 0 | 3 | Number of structural breaks observed in the non sagebrush shrub time series |
|
perennial_herbaceous_break_point |
count | 0 | 3 | Number of structural breaks observed in the perennial herbaceous time series |
|
tree_break_point |
count | 0 | 3 | Number of structural breaks observed in the tree time series |
|
annual_herbaceous_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for annual herbaceous time series |
bare_ground_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for bare ground time series |
herbaceous_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for herbaceous time series |
litter_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for litter time series |
sagebrush_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for sagebrush time series |
shrub_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for shrub time series |
shrub_height_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for shrub height time series |
non_sagebrush_shrub_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for non sagebrush shrub time series |
perennial_herbaceous_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for perennial herbaceous time series |
tree_linear_model_pvalue |
P-value | 0 | 100 | 0.01 | P-value of linear trends model for tree time series |
annual_herbaceous_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for annual herbaceous time series |
bare_ground_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for bare ground time series |
herbaceous_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for herbaceous time series |
litter_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for litter time series |
sagebrush_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for sagebrush time series |
shrub_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for shrub time series |
shrub_height_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for shrub height time series |
non_sagebrush_shrub_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for non sagebrush shrub time series |
perennial_herbaceous_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for perennial herbaceous time series |
tree_linear_model_slope |
% change/y | -383 | 351 | 0.01 | Slope of linear trends model for tree time series |
annual_herbaceous_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of annual herbaceous time series |
|
bare_ground_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of bare ground time series |
|
herbaceous_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of herbaceous time series |
|
litter_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of litter time series |
|
sagebrush_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of sagebrush time series |
|
shrub_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of shrub time series |
|
shrub_height_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of shrub height time series |
|
non_sagebrush_shrub_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of non sagebrush shrub time series |
|
perennial_herbaceous_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of perennial herbaceous time series |
|
tree_most_recent_break_point |
y | 1985 | 2023 | Year of most recent break in the time-series for each component of tree time series |
|
total_change_intensity_index |
Dimensionless | 0 | 100 | Total Change Intensity is a derivative index designed to highlight the total amount of change across primary components (shrub, bare ground, litter, and herbaceous). Change indicates the slope values from the structural change analysis. Values are constructed so that 100 means the maximum observed change across all components and 0 means no change. |
Terms of Use
Terms of Use
This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. This is an Open Access article that has been identified as being free of known restrictions under copyright law, including all related and neighboring rights (https://creativecommons.org/publicdomain/mark/1.0/). You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
Citations
Rigge, M.B., Bunde, B., Postma, K., and Shi, H., 2024, Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2023: U.S. Geological Survey data release, doi:10.5066/P9SJXUI1.
Rigge, M., H. Shi, C. Homer, P. Danielson, and B. Granneman. 2019. Long-term trajectories of fractional component change in the Northern Great Basin, USA. Ecosphere 10(6):e02762. doi:10.1002/ecs2.2762
Rigge, M., C. Homer, L. Cleeves, D. K. Meyer, B. Bunde, H. Shi, G. Xian, S. Schell, and M. Bobo. 2020. Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data. Remote Sensing 12. doi:10.3390/rs12030412
Rigge, M., C. Homer, H. Shi, D. Meyer, B. Bunde, B. Granneman, K. Postma, P. Danielson, A. Case, and G. Xian. 2021. Rangeland Fractional Components Across the Western United States from 1985 to 2018. Remote Sensing 13:813. doi:10.3390/rs13040813
DOIs
Explore with Earth Engine
Code Editor (JavaScript)
// Import the NLCD RCMAP TRENDS image.
var dataset = ee.Image('USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS');
var trends = dataset.select('annual_herbaceous_break_point');
var vis = {
min: [0],
max: [5],
'palette': [
'000000', 'f9e8b7', 'f7e3ac', 'f0dfa3', 'eedf9c', 'eada91', 'e8d687',
'e0d281', 'ddd077', 'd6cc6d', 'd3c667', 'd0c55e', 'cfc555', 'c6bd4f',
'c4ba46', 'bdb83a', 'bbb534', 'b7b02c', 'b0ad1f', 'adac17', 'aaaa0a',
'a3a700', '9fa700', '9aa700', '92a700', '8fa700', '87a700', '85a700',
'82aa00', '7aaa00', '77aa00', '70aa00', '6caa00', '67aa00', '5fa700',
'57a700', '52a700', '4fa700', '4aa700', '42a700', '3ca700', '37a700',
'37a300', '36a000', '369f00', '349d00', '339900', '339900', '2f9200',
'2d9100', '2d8f00', '2c8a00', '2c8800', '2c8500', '2c8400', '2b8200',
'297d00', '297a00', '297900', '277700', '247400', '247000', '29700f',
'2c6d1c', '2d6d24', '336d2d', '366c39', '376c44', '396a4a', '396a55',
'3a6a5f', '3a696a', '396774', '3a6782', '39668a', '376292', '34629f',
'2f62ac', '2c5fb7', '245ec4', '1e5ed0', '115cdd', '005ae0', '0057dd',
'0152d6', '0151d0', '014fcc', '014ac4', '0147bd', '0144b8', '0142b0',
'0141ac', '013da7', '013aa0', '01399d', '013693', '013491', '012f8a',
'012d85', '012c82', '01297a'
]
};
// Display the image on the map.
Map.setCenter(-114, 38, 6);
Map.addLayer(trends, vis, 'annual herbaceous breakpoint in integer');