Sentinel-2 based estimates of rangeland fractional cover and canopy gap class for the western United States

  • Brady W. Allred
  • , Sarah E. McCord
  • , Timothy J. Assal
  • , Brandon T. Bestelmeyer
  • , Chad S. Boyd
  • , Alexander C. Brooks
  • , Samantha M. Cady
  • , Michael C. Duniway
  • , Samuel D. Fuhlendorf
  • , Shane A. Green
  • , Georgia R. Harrison
  • , Eric R. Jensen
  • , Emily J. Kachergis
  • , Anna C. Knight
  • , Chloe M. Mattilio
  • , Brian A. Mealor
  • , David E. Naugle
  • , Dylan O’Leary
  • , Peter J. Olsoy
  • , Erika S. Peirce
  • Jason R. Reinhardt, Robert K. Shriver, Joseph T. Smith, Jason D. Tack, Ashley M. Tanner, Evan P. Tanner, Dirac Twidwell, Nicholas P. Webb, Scott L. Morford

Research output: Contribution to journalArticlepeer-review

Abstract

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75 × 75 km tiles covering the 17 western states of the United States.

Original languageEnglish
Article number1889
JournalScientific data
Volume12
Issue number1
DOIs
StatePublished - Nov 28 2025

Keywords

  • United States
  • Ecosystem
  • Remote Sensing Technology
  • Satellite Imagery
  • Environmental Monitoring

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