Mapping tropical forest degradation with deep learning and Planet NICFI data
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Originalsprog | Engelsk |
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Artikelnummer | 113798 |
Tidsskrift | Remote Sensing of Environment |
Vol/bind | 298 |
Antal sider | 18 |
ISSN | 0034-4257 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
The authors are grateful to the Grantham and High Tide Foundations for their generous gift to UCLA and grants to CTrees for bringing new science and technology to solve environmental problems. This work was partially conducted at the Jet Propulsion Laboratory, California Institute of Technology under a contract ( 80NM0018F0590 ) the National Aeronautics and Space Administration (NASA). R.D. was partially supported by the São Paulo Research Foundation (FAPESP) grant 2019/21662-8 . D.B. was supported by the Brazilian National Council for Scientific and Technological Development (CNPq). P.C.B. and M.P. were supported by the University of Manchester through SEED (School of Environment Education and Development) New Horizons Research & Scholarship Stimulation Fund. L.O.A. was supported by the FAPESP grants: 2020/15230-5 and 2020/08916 , FAPEAM grant 01.02.016301.000289/2021-33 and the National Council for Scientific and Technological Development (CNPq), Brazil, productivity grant 314473/2020-3 . R.F. is supported by the research grant DeReEco ( 34306 ) from Villum Fonden .
Funding Information:
The authors are grateful to the Grantham and High Tide Foundations for their generous gift to UCLA and grants to CTrees for bringing new science and technology to solve environmental problems. This work was partially conducted at the Jet Propulsion Laboratory, California Institute of Technology under a contract (80NM0018F0590) the National Aeronautics and Space Administration (NASA). R.D. was partially supported by the São Paulo Research Foundation (FAPESP) grant 2019/21662-8. D.B. was supported by the Brazilian National Council for Scientific and Technological Development (CNPq). P.C.B. and M.P. were supported by the University of Manchester through SEED (School of Environment Education and Development) New Horizons Research & Scholarship Stimulation Fund. L.O.A. was supported by the FAPESP grants: 2020/15230-5 and 2020/08916, FAPEAM grant 01.02.016301.000289/2021-33 and the National Council for Scientific and Technological Development (CNPq), Brazil, productivity grant 314473/2020-3. R.F. is supported by the research grant DeReEco (34306) from Villum Fonden.
Publisher Copyright:
© 2023 The Authors
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