Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Brady W. Allred, Brandon T. Bestelmeyer, Chad S. Boyd, Christopher Brown, Kirk W. Davies, Michael C. Duniway, Lisa M. Ellsworth, Tyler A. Erickson, Samuel D. Fuhlendorf, Timothy V. Griffiths, Vincent Jansen, Matthew O. Jones, Jason Karl, Anna Knight, Jeremy D. Maestas, Jonathan J. Maynard, Sarah E. McCord, David E. Naugle, Heath D. Starns, Dirac TwidwellDaniel R. Uden

Research output: Contribution to journalArticlepeer-review

100 Scopus citations

Abstract

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision-making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on-the-ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal-explicit, pixel-level estimates of uncertainty. We evaluated the model with 5,780 on-the-ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel-level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on-the-ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision-making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Original languageEnglish
Pages (from-to)841-849
Number of pages9
JournalMethods in Ecology and Evolution
Volume12
Issue number5
DOIs
StatePublished - May 2021

Keywords

  • conservation
  • convolutional neural network
  • grassland
  • machine learning
  • monitoring
  • rangeland management
  • remote sensing
  • temporal convolutional network

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