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Mapping the national seagrass extent in Seychelles using PlanetScope NICFI data

Abstract:
Seagrasses provide ecosystem services worth USD 2.28 trillion annually. However, their direct threats and our incomplete knowledge hamper our capabilities to protect and manage them. This study aims to evaluate if the NICFI Satellite Data Program basemaps could map Seychelles’ extensive seagrass meadows, directly supporting the country’s ambitions to protect this ecosystem. The Seychelles archipelago was divided into three geographical regions. Half-yearly basemaps from 2015 to 2020 were combined using an interval mean of the 10th percentile and median before land and deep water masking. Additional features were produced using the Depth Invariant Index, Normalised Differences, and segmentation. With 80% of the reference data, an initial Random Forest followed by a variable importance analysis was performed. Only the top ten contributing features were retained for a second classification, which was validated with the remaining 20%. The best overall accuracies across the three regions ranged between 69.7% and 75.7%. The biggest challenges for the NICFI basemaps are its four-band spectral resolution and uncertainties owing to sampling bias. As part of a nationwide seagrass extent and blue carbon mapping project, the estimates herein will be combined with ancillary satellite data and contribute to a full national estimate in a near-future report. However, the numbers reported showcase the broader potential for using NICFI basemaps for seagrass mapping at scale.
Publication status:
Published
Peer review status:
Peer reviewed

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Files:
Publisher copy:
10.3390/rs15184500

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Role:
Author
ORCID:
0000-0003-4897-064X
et al.


Publisher:
MDPI
Journal:
Remote Sensing More from this journal
Volume:
15
Issue:
18
Article number:
4500
Publication date:
2023-09-13
Acceptance date:
2023-09-06
DOI:
EISSN:
2072-4292


Language:
English
Keywords:
Pubs id:
1526441
Local pid:
pubs:1526441
Deposit date:
2023-09-13

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