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Detection and Mapping of Seagrass Meadows at Ritchie’s Archipelago using Sentinel 2A Satellite Imagery


Affiliations
1 Indian Institute of Remote Sensing, Dehradun 248 001, India
2 Department of Endangered Species Management, Wildlife Institute of India, Dehradun 248 002, India
3 Marine and Atmospheric Science Department, Indian Institute of Remote Sensing, Dehradun 248 001, India
4 Department of Habitat Ecology, Wildlife Institute of India, Dehradun 248 002, India
 

This study presents an attempt to utilize seagrass data acquired from field surveys to compare classification models for mapping seagrasses using Sentinel -2A satellite data. Out of three models tested , viz. Random Forest, Support Vector Machine and K-Nearest Neighbor; Random Forest classification model proved most effective in the given scenario with 0.99 model accuracy. Seagrasses present as deep as 21 m were detected post water column correction, presenting the capability of Sentinel-2A satellite in detecting submerged benthic habitat.

Keywords

Depth Invariant Index, Ritchie’s Archipelago, Seagrass, Sentinel-2A.
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  • Detection and Mapping of Seagrass Meadows at Ritchie’s Archipelago using Sentinel 2A Satellite Imagery

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Authors

Sharad Bayyana
Indian Institute of Remote Sensing, Dehradun 248 001, India
Satish Pawar
Indian Institute of Remote Sensing, Dehradun 248 001, India
Swapnali Gole
Department of Endangered Species Management, Wildlife Institute of India, Dehradun 248 002, India
Sohini Dudhat
Department of Endangered Species Management, Wildlife Institute of India, Dehradun 248 002, India
Anant Pande
Department of Endangered Species Management, Wildlife Institute of India, Dehradun 248 002, India
Debashis Mitra
Marine and Atmospheric Science Department, Indian Institute of Remote Sensing, Dehradun 248 001, India
Jeyaraj Antony Johnson
Department of Habitat Ecology, Wildlife Institute of India, Dehradun 248 002, India
Kuppusamy Sivakumar
Department of Endangered Species Management, Wildlife Institute of India, Dehradun 248 002, India

Abstract


This study presents an attempt to utilize seagrass data acquired from field surveys to compare classification models for mapping seagrasses using Sentinel -2A satellite data. Out of three models tested , viz. Random Forest, Support Vector Machine and K-Nearest Neighbor; Random Forest classification model proved most effective in the given scenario with 0.99 model accuracy. Seagrasses present as deep as 21 m were detected post water column correction, presenting the capability of Sentinel-2A satellite in detecting submerged benthic habitat.

Keywords


Depth Invariant Index, Ritchie’s Archipelago, Seagrass, Sentinel-2A.

References





DOI: https://doi.org/10.18520/cs%2Fv118%2Fi8%2F1275-1282