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<p>Myanmar, recognized as a hotspot for global mangrove deforestation, has experienced rapid loss of these critical ecosystems due to extensive human activity and natural disturbances. However, limited research has addressed the long-term, detailed dynamics of mangrove disturbances in Myanmar. To bridge this gap, this study applied the continuous change detection and classification (CCDC) algorithm on Landsat images within the Google Earth Engine (GEE) platform to analyze mangrove disturbances (disturbance frequency, maximum disturbance years, and maximum disturbance degrees) and driving forces in Myanmar over 30 a (1990–2020). Our findings reveal that (1) the CCDC algorithm effectively detects mangrove disturbances with an overall recognition accuracy of 85.50%. (2) Approximately 80% of the disturbed mangroves in Myanmar experienced disturbances fewer than three times, with areas experiencing severe disturbance being the smallest. The maximum disturbance areas of mangroves in Myanmar showed an overall increasing trend from 1990, followed by a decline and then a renewed increase. (3) The expansion of rice cultivation disturbed many mangroves in Ayeyarwady and Rakhine, and Cyclone Nargis also disturbed mangroves in Ayeyarwady. These findings on mangrove disturbance’s spatial and temporal patterns offer essential insights for informed management and conservation strategies for Myanmar’s mangrove resources.</p>

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Mangrove disturbance analysis over 30 years in Myanmar based on continuous change detection and classification algorithm

  • 期刊论文

  • 发表日期：

    2025年6月18日

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  • Yifei Xia,
  • Kai Liu,
  • Yuanhui Zhu,
  • Xin Wen,
  • Jingjing Cao

摘要

Myanmar, recognized as a hotspot for global mangrove deforestation, has experienced rapid loss of these critical ecosystems due to extensive human activity and natural disturbances. However, limited research has addressed the long-term, detailed dynamics of mangrove disturbances in Myanmar. To bridge this gap, this study applied the continuous change detection and classification (CCDC) algorithm on Landsat images within the Google Earth Engine (GEE) platform to analyze mangrove disturbances (disturbance frequency, maximum disturbance years, and maximum disturbance degrees) and driving forces in Myanmar over 30 a (1990–2020). Our findings reveal that (1) the CCDC algorithm effectively detects mangrove disturbances with an overall recognition accuracy of 85.50%. (2) Approximately 80% of the disturbed mangroves in Myanmar experienced disturbances fewer than three times, with areas experiencing severe disturbance being the smallest. The maximum disturbance areas of mangroves in Myanmar showed an overall increasing trend from 1990, followed by a decline and then a renewed increase. (3) The expansion of rice cultivation disturbed many mangroves in Ayeyarwady and Rakhine, and Cyclone Nargis also disturbed mangroves in Ayeyarwady. These findings on mangrove disturbance’s spatial and temporal patterns offer essential insights for informed management and conservation strategies for Myanmar’s mangrove resources.