@article {JRN54949, title = {Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984{\textendash}2017}, journal = {Ecosphere}, volume = {9}, year = {2018}, month = {09/2018}, pages = {1-19}, chapter = {1}, 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.

}, keywords = {cloud computing, conservation, Google Earth Engine, grazing, land cover, landsat, machine learning, rangeland, remote sensing, Time series, wildfire}, doi = {doi.org/10.1002/ecs2.2430}, url = {files/bibliography/18-021.pdf}, author = {Matthew O. Jones and Brady W. Allred and David E. Naugle and J. Maestas and Donnelly, Patrick and Loretta Metz and Jason W. Karl and Smith, Rob and Brandon T Bestelmeyer and Chad S. Boyd and Kerby, Jay D. and McIver, James D.} }