Ghosh, Shuvankar, Proisy, Christophe, Muthusankar, Gowrappan, Hassenrück, Christiane ORCID: https://orcid.org/0000-0003-1909-1726, Helfer, Véronique ORCID: https://orcid.org/0000-0001-5923-5291, Mathevet, Raphaël, Andrieu, Julien, Balachandran, Natesan and Narendran, Rajendran (2022) Multiscale Diagnosis of Mangrove Status in Data-Poor Context Using Very High Spatial Resolution Satellite Images: A Case Study in Pichavaram Mangrove Forest, Tamil Nadu, India.. Remote Sensing, 14 (10). p. 2317. DOI https://doi.org/10.3390/rs14102317.

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Abstract

Highlighting spatiotemporal changes occurring within mangrove habitats at the finest possible scale could contribute fundamental knowledge and data for local sustainable management. This study presents the current situation of the Pichavaram mangrove area, a coastal region of Southeast India prone to both cyclones and reduced freshwater inflow. Based on the supervised classification and visual inspection of very high spatial resolution (VHSR) satellite images provided with a pixel size of <4 m, we generated time-series maps to analyze the changes that occurred in both the natural and planted mangroves between 2003 and 2019. We achieved a high mapping accuracy (>85%), which confirmed the potential of classification techniques applied to VHSR images in capturing changes in mangroves on a very fine scale. Our diagnosis reveals variable expansion rates in plantations made by the local authorities. We also report an ongoing mangrove dieback and confirm progressive shoreline erosion along the coastline. Despite a lack of field data, VHSR images allowed for the multiscale diagnosis of the ecosystem situation, thus constituting the first fine-scale assessment of the fragile Pichavaram mangrove area upon which the coastal community is dependent.

Document Type:	Article
Programme Area:	PA Not Applicable
Research affiliation:	Biogeochemistry and Geology > Tropical Marine Microbiology Ecology > Mangrove Ecology
Refereed:	Yes
Open Access Journal?:	Yes
DOI:	https://doi.org/10.3390/rs14102317
Date Deposited:	23 May 2022 15:00
Last Modified:	26 Mar 2024 13:31
URI:	http://cris.leibniz-zmt.de/id/eprint/4923

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