Spatial and Temporal Patterns of Mangrove Forest Change in the Mekong Region over Four Decades Based on a Remote Sensing Data-Driven Approach

Mangrove forests are critical coastal ecosystems that store carbon, support marine life, and serve as natural barriers, protecting shorelines from erosion and reducing the impact of storms by absorbing wave energy. However, the rise of human activities and sea levels has led to their destruction over the past decades. It is important to know how the areas of mangrove forests change and adapt every year to plan for their restoration and protection and to support future trends like using carbon credits to help developing countries generate income. This study aims to map and monitor mangrove forest area changes over four decades in the Mekong region, comprising Myanmar, Thailand, Cambodia, and Vietnam, from 1984 to 2023 using a time series of Landsat data together with random forest (RF) classification. This analysis implemented multiple approaches, including creating stabilized Landsat imagery composites from the LandTrendr algorithm, Otsu edge detection, Minimum Mapping Unit (MMU), and RF classifier. The study found the map accuracy based on the RF model classifier achieved an overall accuracy between 86.2% and 88.8%, providing reliable data for analysis. Country-level analysis revealed increasing mangrove forest cover in Thailand (12.9%) and Vietnam (28.4%) since 1984. Conversely, mangrove areas in Cambodia and Myanmar have decreased significantly from 1984 to 2023 by about 14.6% and 22.7%, respectively. These findings have significant implications for resource allocation, investment strategies, and the development of carbon credits to support mangrove conservation efforts. This comprehensive dataset offers valuable insights for stakeholders involved in mangrove management and restoration in the Mekong region. By understanding the spatial-temporal distribution patterns of mangrove forest change, decision-makers can make informed decisions to safeguard these critical ecosystems for future generations.