Data introduction:
Pasture ecosystems in the western United States respond dynamically to climate change, fires, and other anthropogenic disturbances. The Rangeland Status, Monitoring, Assessment, and Forecasting (RCMAP) product aims to capture this response by quantifying the percentage coverage, associated errors, and trends of rangeland components in the western United States using Landsat imagery from 1985-2020.
Using Landsat images from 1985-2020, RCMAP quantified the coverage of pasture components in the western United States (according to Rigge et al., 2020). The RCMAP time series consists of eight parts: annual herbs, bare ground, herbs, litter, non-sea buckthorn shrubs, perennial herbs, sea buckthorn and shrubs, and the temporal trends for each part. The four main components (bare ground, shrubs, litter, and grass), when added to the canopy cover, are designed to sum to 100% per pixel. Minor components Annual and perennial herbs are subsets of primary component herbs, while non-sea buckthorn shrubs and sea buckthorn are subsets of shrubs. Minor components cannot have greater coverage than their respective major components. One year, 2012, was excluded from the time series due to a lack of high-quality images.
MRLC developed an automated method to identify changes in spectral conditions between each year and base maps in the Landsat archive around 2016. A regression tree model is trained on the unchanged portion of the time series for each year. The post-processing model corrects the burnt trajectory and removes noise and illogical changes in the predictions. The current generation of RCMAPs has been improved with more training data, region-scale Landsat composites, and more robust change detection. MRLC assessed the temporal patterns of each component with a linear model and a structural change approach, which used an 8-year time-moving window to identify breakpoints in the time series. Linear and structural variation methods are largely consistent in the overall pattern of variation, but the latter finds breakpoints more often, with most pixels having at least one breakpoint. The data provide spatiotemporal information about the occurrence of fractures, but more critically, attribute these change events to specific components. Detailed and specific information on the spatial, temporal and thematic (i.e. multi-component) of rangeland conditions facilitates the understanding of major patterns of change at local, regional and continental levels.
Dataset Availability
1985-01-01T00:00:00Z - 2020-12-31T00:00:00
Dataset Provider
United States Geological Survey and Bureau of Land Management
Earth Engine Snippet
ee.ImageCollection("USGS/NLCD_RELEASES/2019_REL/RCMAP/V4/COVER")
Band:
Resolution
30 meters
Bands Table
| Name | Description | Min | Max | Units |
|---|---|---|---|---|
| rangeland_annual_herbaceous | Percent of the pixel covered by annual herbaceous vegetation. | 0 | 100 | % |
| rangeland_bare_ground | Percent of the pixel covered by bare ground. | 0 | 100 | % |
| rangeland_non_sagebrush_shrub | Percent of the pixel covered by non-sagebrush shrubs. | 0 | 100 | % |
| rangeland_herbaceous | Percent of the pixel covered by herbaceous vegetation. | 0 | 100 | % |
| rangeland_litter | Percent of the pixel covered by litter. | 0 | 100 | % |
| rangeland_sagebrush | Percent of the pixel covered by sagebrush (*Artemisia* spp). | 0 | 100 | % |
| rangeland_shrub | Percent of the pixel covered by shrub. | 0 | 100 | % |
| rangeland_perennial_herbaceous | Percent of the pixel covered by perennial herbaceous vegetation. | 0 | 100 | % |
Literature citations:
-
Rigge, M.B., Bunde, B., Shi, H., Postma, K., 2021, Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2020 (ver. 2.0, October 2021): U.S. Geological Survey data release. doi:10.5066/P95IQ4BT
Code:
// 加载 NLCD RCMAP 数据集
var dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2019_REL/RCMAP/V4/COVER');
//时间筛选，并选出第一景影像
var nlcd2016 = dataset.filter(ee.Filter.eq('system:index', '2016')).first();
//打印16年影像的第一景结果
print('Bands:', nlcd2016.bandNames());
// 一年生草本植物覆盖的像素的百分比。
var landcover = nlcd2016.select('rangeland_annual_herbaceous');
var vis = {
'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",
]
};
// 加载在地图上，也就是植被覆盖的百分比%
Map.setCenter(-114, 38, 6);
Map.addLayer(landcover, vis, 'Rangeland Annual Herbaceous %');
Partially enlarged display:
result:
