Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches-Reference-Cited by-同舟云学术

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

Published:2021-11 Issue:11 Volume:12 Page:
ISSN:2150-8925
Container-title:Ecosphere
language:en
Short-container-title:Ecosphere

Author:

Kitzes Justin¹^ORCID,Blake Rachael²^ORCID,Bombaci Sara³,Chapman Melissa⁴,Duran Sandra M.⁵,Huang Tao⁶,Joseph Maxwell B.⁷^ORCID,Lapp Samuel¹,Marconi Sergio⁸^ORCID,Oestreich William K.⁹^ORCID,Rhinehart Tessa A.¹^ORCID,Schweiger Anna K.¹⁰^ORCID,Song Yiluan¹¹^ORCID,Surasinghe Thilina¹²,Yang Di¹³^ORCID,Yule Kelsey¹⁴^ORCID

Affiliation:

1. Department of Biological Sciences University of Pittsburgh Pittsburgh Pennsylvania USA

2. National Socio‐Environmental Synthesis Center Annapolis Maryland USA

3. Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA

4. Department of Environmental Science, Policy, and Management University of California Berkeley Berkeley California USA

5. Department of Ecology & Evolutionary Biology The University of Arizona Tucson Arizona USA

6. Human‐Environment Systems Boise State University Boise Idaho USA

7. Earth Lab Cooperative Institute for Research in Environmental Sciences (CIRES) University of Colorado Boulder Boulder Colorado USA

8. Department of Wildlife Ecology and Conservation University of Florida Gainesville Florida USA

9. Hopkins Marine Station Stanford University Stanford California USA

10. Department of Geography University of Zurich Zurich Switzerland

11. Environmental Studies Department University of California Santa Cruz California USA

12. Department of Biological Sciences Bridgewater State University Bridgewater Massachusetts USA

13. Wyoming Geographic Information Science Center (WyGISC) University of Wyoming Laramie Wyoming USA

14. National Ecological Observatory Network Biorepository Arizona State University Tempe Arizona USA

Funder

Division of Biological Infrastructure

University of Pittsburgh

National Science Foundation

Mascaro Center for Sustainable Innovation, University of Pittsburgh

Gordon and Betty Moore Foundation

Universität Zürich

Publisher

Wiley

Subject

Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.3795

Reference161 articles.

1. Automated classification of bird and amphibian calls using machine learning: A comparison of methods

2. Reliable Automation of Bat Call Identification for Eastern New South Wales, Australia, Using Classification Trees and AnaScheme Software

3. Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

4. Real-time bioacoustics monitoring and automated species identification

5. Regional-scale mapping of tree cover, height and main phenological tree types using airborne laser scanning data

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