Characterising the distribution of mangroves along the southern coast of Vietnam using multi-spectral indices and a deep learning model.

Autor: Tran TV; School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia. Electronic address: thuong.tran@monash.edu., Reef R; School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia. Electronic address: ruth.reef@monash.edu., Zhu X; School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia. Electronic address: xuan.zhu@monash.edu., Gunn A; School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia. Electronic address: a.gunn@monash.edu.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 May 01; Vol. 923, pp. 171367. Date of Electronic Publication: 2024 Mar 01.
DOI: 10.1016/j.scitotenv.2024.171367
Abstrakt: Mangroves are an ecologically and economically valuable ecosystem that provides a range of ecological services, including habitat for a diverse range of plant and animal species, protection of coastlines from erosion and storms, carbon sequestration, and improvement of water quality. Despite their significant ecological role, in many areas, including in Vietnam, large scale losses have occurred, although restoration efforts have been underway. Understanding the scale of the loss and the efficacy of restoration requires high resolution temporal monitoring of mangrove cover on large scales. We have produced a time series of 10-m-resolution mangrove cover maps using the Multispectral Instrument on the Sentinel 2 satellites and with this tool measured the changes in mangrove distribution on the Vietnamese Southern Coast (VSC). We extracted the annual mangrove cover ranging from 2016 to 2023 using a deep learning model with a U-Net architecture based on 17 spectral indices. Additionally, a comparison of misclassification by the model with global products was conducted, indicating that the U-Net architecture demonstrated superior performance when compared to experiments including multispectral bands of Sentinel-2 and time-series of Sentinel-1 data, as shown by the highest performing spectral indices. The generated performance metrics, including overall accuracy, precision, recall, and F1-score were above 90 % for entire years. Water indices were investigated as the most important variables for mangrove extraction. Our study revealed some misclassifications by global products such as World Cover and Global Mangrove Watch and highlighted the significance of our study for local analysis. While we did observe a loss of 34,778 ha (42.2 %) of mangrove area in the region, 47,688 ha (57.8 %) of new mangrove area appeared, resulting in a net gain of 12,910 ha (15.65 %) over the eight-year period of the study. The majority of new mangrove areas were concentrated in Ca Mau peninsulas and within estuaries undergoing recovery programs and natural recovery processes. Mangrove loss occurred in regions where industrial development, wind farm projects, reclaimed land, and shrimp pond expansion is occurring. Our study provides a theoretical framework as well as up-to-date data for mapping and monitoring mangrove cover change that can be readily applied at other sites.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Thuong V. Tran reports financial support as allowance was provided by Monash University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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