RCMAP Rangeland Trends for Component Timeseries (1985-2023), v06

USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS
Dataset Availability
1985-01-01T00:00:00Z–2023-12-31T00:00:00Z
Dataset Provider
Earth Engine Snippet
ee.Image("USGS/NLCD_RELEASES/2023_REL/RCMAP/V6/TRENDS")
Tags
climate-change disturbance landsat-derived nlcd rangeland trends

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

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

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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');
Open in Code Editor