Machine learning-based examination of recent mangrove forest changes in the western Irrawaddy River Delta, Southeast Asia

Yuan XIONG, Zhijun DAI, Chuqi LONG, Xixing LIANG, Yaying LOU, Xuefei MEI, Binh An NGUYEN, Jinping CHENG

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

Mangrove forests serve as a significant carbon sink and provide effective shoreline protection against devastating winds. However, these forests are facing an unprecedented decline in tropical and subtropical large deltas due to human activities and climate change. This study focuses on quantifying the dynamic changes of mangrove forests in the Western Irrawaddy River Delta (WIDR), which is part of Myanmar's largest delta in Southeast Asia, Remote sensing images from 1988 to 2022 were utilized alongside a machine learning approach to analyze these changes. The findings demonstrate a significant decline of mangrove forests within the WIRD, with a reduction in area by 45.35% over the past 35 years. Additionally, the patches of mangrove forests have become increasingly fragmented. Losses predominantly occurred in the inland regions, while gains were observed along the seaward edges, suggesting progradation towards the sea, which compensated for a net loss of 2,812.32 ha during the study period. The intensification of human activities, specifically deforestation and aquaculture pond utilization, appears to be the leading cause of catastrophic internal degradation within the WIRD's mangrove forests. Contrary to the influence of local sea level rise or variations in suspended sediment discharge into the WIRD, this study suggests that the persistent retreat of mangrove forest fringes is controlled by large waves generated by the southwest monsoon. Estuarine barriers situated in the WIRD act as buffers, dissipating wave energy and facilitating seaward growth. of mangrove forests. Our study reveals a significant pattern in the WIRD, where landward mangrove forest loss coincided with seaward mangrove forest gain. This work underscores the importance of addressing landward mangrove forest loss and recognizing the emergence of seaward gains in the WIRD, providing valuable insights for fostering successful and sustainable mangrove management and protection initiatives. Copyright © 2023 Elsevier B.V. All rights reserved.

Original languageEnglish
Article number107601
JournalCatena
Volume234
Early online dateOct 2023
DOIs
Publication statusPublished - Jan 2024

Citation

Xiong, Y., Dai, Z., Long, C., Liang, X., Lou, Y., Mei, X., Nguyen, B. A. & Cheng, J. (2023). Machine learning-based examination of recent mangrove forest changes in the western Irrawaddy River Delta, Southeast Asia. Catena, 234, Article 107601. https://doi.org/10.1016/j.catena.2023.107601

Keywords

  • Dynamics
  • Mangrove forest
  • Gain and loss
  • The western Irrawaddy River Delta
  • Anthropogenic activities
  • Southeast Asia

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