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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #354049
Title: Innovation in rangeland monitoring: Annual, 30m, plant functional type percent cover maps for US rangelands, 1984–2017

Author
item JONES, MATTHEW - University Of Montana
item ALLRED, BRADY - University Of Montana
item NAUGLE, DAVID - University Of Montana
item MAESTAS, JEREMY - Natural Resources Conservation Service (NRCS, USDA)
item DONNELLY, PATRICK - Us Fish And Wildlife Service
item METZ, LORETTA - Natural Resources Conservation Service (NRCS, USDA)
item KARL, JASON - University Of Idaho
item SMITH, ROB - University Of Montana
item Bestelmeyer, Brandon
item Boyd, Chad
item KERBY, JAY - Nature Conservancy
item MCIVER, JAMES - Oregon State University

Submitted to: Ecosphere
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/31/2018
Publication Date: 9/1/2018
Citation: Jones, M., Allred, B.W., Naugle, D.E., Maestas, J.D., Donnelly, P., Metz, L., Karl, J., Smith, R., Bestelmeyer, B.T., Boyd, C.S., Kerby, J.D., McIver, J.D. 2018. Innovation in rangeland monitoring: Annual, 30m, plant functional type percent cover maps for US rangelands, 1984–2017. Ecosphere. 9(9):e02430. https://doi.org/10.1002/ecs2.2430.
DOI: https://doi.org/10.1002/ecs2.2430

Interpretive Summary: New computational procedures were merged with historical remote sensing and field data to provide the first moderate resolution, annual, percent cover maps of plant functional types across rangeland ecosystems. We predicted per pixel percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western U.S. from 1984 to 2017.  Results were validated using three independent collections of plot level measurements and resulting maps display land cover variation in response to changes in climate, disturbance, and management.  The maps, which will be updated annually at the end of each year, provide exciting opportunities to expand and improve rangeland conservation, monitoring, and management.  The data open new doors for scientific investigation at an unprecedented blend of temporal fidelity, spatial resolution, and geographic scale.

Technical Abstract: Innovations in machine learning and cloud-based computing were merged with historical remote sensing and field data to provide the first moderate resolution, annual, percent cover maps of plant functional types across rangeland ecosystems to effectively and efficiently respond to pressing challenges facing conservation of biodiversity and ecosystem services.  We utilized the historical Landsat satellite record, gridded meteorology, abiotic land surface data, and over 30,000 field plots within a Random Forests model to predict per pixel percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western U.S. from 1984 to 2017.  Results were validated using three independent collections of plot level measurements and resulting maps display land cover variation in response to changes in climate, disturbance, and management.  The maps, which will be updated annually at the end of each year, provide exciting opportunities to expand and improve rangeland conservation, monitoring, and management.  The data open new doors for scientific investigation at an unprecedented blend of temporal fidelity, spatial resolution, and geographic scale.