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

Author:

Allred Brady W.12ORCID,Bestelmeyer Brandon T.3,Boyd Chad S.4,Brown Christopher5,Davies Kirk W.4ORCID,Duniway Michael C.6ORCID,Ellsworth Lisa M.7,Erickson Tyler A.5ORCID,Fuhlendorf Samuel D.8,Griffiths Timothy V.9,Jansen Vincent10,Jones Matthew O.2,Karl Jason10,Knight Anna6ORCID,Maestas Jeremy D.11,Maynard Jonathan J.12,McCord Sarah E.3,Naugle David E.1,Starns Heath D.13ORCID,Twidwell Dirac14,Uden Daniel R.1415ORCID

Affiliation:

1. W.A. Franke College of Forestry and Conservation University of Montana Missoula MT USA

2. Numerical Terradynamic Simulation Group University of Montana Missoula MT USA

3. Jornada Experimental RangeUSDA Agricultural Research ServiceLas Cruces NM USA

4. Eastern Oregon Agricultural Research Center USDA Agricultural Research Service Burns OR USA

5. Google, Inc Mountain View CA USA

6. U.S. Geological SurveySouthwest Biological Science Center Moab UT USA

7. Fisheries and Wildlife Department Oregon State University Corvallis OR USA

8. Natural Resource Ecology and Management Oklahoma State University Stillwater OK USA

9. USDA Natural Resources Conservation ServiceLandscape Initiatives Team Bozeman MT USA

10. Department of Forest, Rangeland, and Fire Sciences University of Idaho Moscow ID USA

11. USDA Natural Resources Conservation ServiceWest National Technology Support Center Portland OR USA

12. Sustainability Innovation Lab at Colorado University of Colorado at Boulder Boulder CO USA

13. Texas AgriLife Research Texas A&M University Sonora TX USA

14. Department of Agronomy and Horticulture University of Nebraska–Lincoln Lincoln NE USA

15. School of Natural Resources University of Nebraska–Lincoln Lincoln NE USA

Publisher

Wiley

Subject

Ecological Modelling,Ecology, Evolution, Behavior and Systematics

Reference27 articles.

1. AI Platform. (2020). Retrieved fromhttps://cloud.google.com/ai‐platform

2. Woody Plant Encroachment: Causes and Consequences

3. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling;Bai S.;arXiv,2018

4. Applications for deep learning in ecology

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