Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017. Jones, M. O., Allred, B. W., Naugle, D. E., Maestas, J. D., Donnelly, P., Metz, L. J., Karl, J., Smith, R., Bestelmeyer, B., Boyd, C., Kerby, J. D., & McIver, J. D. Ecosphere, 9(9):e02430, 2018. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.2430![Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017 [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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 United States 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.
@article{jones_innovation_2018,
title = {Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for {U}.{S}. rangelands, 1984–2017},
volume = {9},
copyright = {© 2018 The Authors.},
issn = {2150-8925},
shorttitle = {Innovation in rangeland monitoring},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ecs2.2430},
doi = {10.1002/ecs2.2430},
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 United States 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.},
language = {en},
number = {9},
urldate = {2023-12-11},
journal = {Ecosphere},
author = {Jones, Matthew O. and Allred, Brady W. and Naugle, David E. and Maestas, Jeremy D. and Donnelly, Patrick and Metz, Loretta J. and Karl, Jason and Smith, Rob and Bestelmeyer, Brandon and Boyd, Chad and Kerby, Jay D. and McIver, James D.},
year = {2018},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.2430},
keywords = {Google Earth Engine, Landsat, cloud computing, conservation, grazing, land cover, machine learning, notion, rangeland, remote sensing, time series, wildfire},
pages = {e02430},
}
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