USFS Landscape Change Monitoring System (LCMS)

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Time Lapse of Annual Vegetation Loss and Gain

Bridger-TetonNF-Blackrock-District

Landscape change, Bridger-Teton, Blackrock District

click to display
time lapse controls
  • Water
  • Non Tree
  • Stable Tree
  • Loss Tree
  • Gain Tree

Knowing where, when, and what factors create change across landscapes is critical to making sound land management decisions. To support land managers and scientists with this need, a group of leading remote sensing scientists and application specialists in the US Forest Service, US Geological Survey, NASA, and numerous universities have collaborated to develop and produce a Landscape Change Monitoring System (LCMS).

LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. LCMS provides a “best available” map of landscape change that leverages advances in time series-based change detection techniques, Landsat data availability, cloud-based computing power, and big data analysis methods.


LCMS Products:

LCMS produces annual maps depicting change (vegetation loss and vegetation gain), land cover, and land use from 1985 to present that can be used to assist with a wide range of land management applications. With the help of Regional and National Forest staffs we have identified many applications of LCMS data, including forest planning and revision, updating existing vegetation maps, assessing landscape conditions, supporting post-fire recovery, and meeting some broad-scale monitoring requirements.

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LCMS Data Availability:

In preparation for nationwide production, prototype LCMS products have been produced over several study areas: the Flathead, Bridger-Teton, Manti-La Sal, and Chugach National Forests, as well as the USFS Intermountain Region (Southern Idaho, Nevada, Utah and Western Wyoming). In addition, an early prototype of only the vegetation loss product over all lands of the conterminous United States is available. The full suite of LCMS products will be available nationwide in early 2021 and these products will be updated annually thereafter.

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LCMS Data Access:

LCMS data are available for viewing and download through the LCMS Data Explorer (http://lcms.forestry.oregonstate.edu/) and also through the links in the table below.

The LCMS Data Explorer is a web-based application that provides users the ability to view, analyze, summarize and download LCMS data. A user-friendly set of tools allows users to upload an area of interest and perform pixel- and area-based summaries with a charting feature displaying the results of all LCMS outputs. A tutorial is available in the Viewer Support menu.

For questions or more information on LCMS please contact the LCMS helpdesk sm.fs.lcms@usda.gov.


  LCMS Product Category
LCMS Study Area Change Land Cover Land Use
Conterminous United States
Detailed Product Description
Download (Loss Annual)

3.6 GB


Download (Loss Summary)

2.6 GB

Not Available Not Available
USFS Intermountain Region
Detailed Product Description
Download (All)

941.3 MB

Download (All)

8.6 GB

Download (All)

2.7 GB

Chugach National Forest-Kenai Peninsula
Detailed Product Description
Download (All)

126.7 MB

Download (All)

471.1 MB

Download (All)

284.1 MB

Flathead National Forest
Detailed Product Description
Download (All)

81.2 MB

Download (All)

112.0 MB

Download (All)

70.5 MB

Bridger-Teton National Forest
Detailed Product Description
Download (All)

145.9 MB

Download (All)

573.2 MB

Download (All)

401.4 MB

Manti-La Sal National Forest
Detailed Product Description
Download (All)

22.8 MB

Download (All)

95.5 MB

Download (All)

60.8 MB

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Documentation and References:

  • Project Overview and Awareness Materials:
  • Online Resources:
  • References:
    • (1) Breiman, L. 2001, Random forests. Machine Learning, 45, pp.5–32.
    • (2) Cohen, W.B., Yang, Z., Kennedy, R.E. 2010, Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation, Remote Sensing of Environment, 114, pp. 2911-2924.
    • (3) Cohen, W.B., Yang, Z., Healey, S.P., Kennedy, R.E., Gorelick, N. 2018, A LandTrendr multispectral ensemble for forest disturbance detection, Remote Sensing of Environment, 205, pp. 131-140.
    • (4) Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sensing of Environment, 202, pp. 18–27.
    • (5) Healey, S.P., Cohen, W.B., Yang, Z., Brewer, C. K., Brooks, E.B., Gorelick, N., Hernandez, A.J., Huang, C., Hughes, M.J., Kennedy, R.E., Loveland, T.R., Moisen, G.G., Schroeder, T.A., Stehman, S.V., Vogelmann, J.E., Woodcock, C.E., Yang, L., Zhu, Z. 2018, Mapping forest change using stacked generalization: an ensemble approach, Remote Sensing of Environment, 204, pp. 717-728.
    • (6) Hughes, M., Kaylor, S., Hayes, D. 2017, Patch-based forest change detection from Landsat time series, Forests, 8, p. 166.
    • (7) Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., Healey, S. 2018, Implementation of the LandTrendr Algorithm on Google Earth Engine, Remote Sensing, 10, p. 691.
    • (8) Zhu, Z., Woodcock, C.E. 2012, Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment, 118, pp. 83–94.
     

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