Landscape Change Monitoring System (LCMS)

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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 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.

The full suite of LCMS products are now available for the Conterminous United States, Southeastern Alaska, Puerto Rico - US Virgin Islands, and Hawaii. It will be updated annually.

LCMS data are available for viewing and download through the LCMS Data Explorer (https://apps.fs.usda.gov/lcms-viewer/) 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.

These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the appropriate citation from this table:

Appropriate use includes regional to national assessments of vegetation cover, land cover, or land use change trends, total extent of vegetation cover, land cover, or land use change, and aggregated summaries of vegetation cover, land cover, or land use change. This product is the initial output from the modeling process. No post-processing (such as applying a minimum mapping unit or manually burning in known features such as roads) has been performed.

• Project Overview and Awareness Materials:
  • PowerPoint: LCMS Overview
  • Paper: Project Brief
  • Poster: Awareness Poster
• Online Resources:
  • LCMS Data Explorer
  • USFS Rocky Mountain Research Station, LCMS Science Team page
• 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.
   

• Take the LCMS User Survey!

The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.

These data were collected using funding from the U.S. Government and can be used without additional permissions or fees.