Articles | Volume 15, issue 9
https://doi.org/10.5194/essd-15-3991-2023
https://doi.org/10.5194/essd-15-3991-2023
Data description paper
 | 
08 Sep 2023
Data description paper |  | 08 Sep 2023

High-resolution global map of closed-canopy coconut palm

Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-463', Anonymous Referee #1, 23 Apr 2023
    • AC1: 'Reply on RC1', Adrià Descals, 10 Jul 2023
  • RC2: 'Comment on essd-2022-463', Anonymous Referee #2, 12 Jun 2023
    • AC2: 'Reply on RC2', Adrià Descals, 10 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Adrià Descals on behalf of the Authors (10 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (19 Jul 2023) by Nophea Sasaki
RR by Anonymous Referee #2 (27 Jul 2023)
RR by Anonymous Referee #1 (28 Jul 2023)
ED: Publish as is (29 Jul 2023) by Nophea Sasaki
AR by Adrià Descals on behalf of the Authors (30 Jul 2023)  Author's response   Manuscript 
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Short summary
The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
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