Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region

A third of the Sundarbans mangrove forest, the world’s largest contiguous mangrove, shows long-term degradation despite overall stability in its extent. Using a novel Continuous Change Detection and Classification (CCDC) method on 30 years of Landsat data, researchers found that 25% of the area experienced negative greening trends—likely due to die-back in Heritiera fomes—and that 47.6% of areas damaged by Cyclone Sidr in 2007 had not recovered by mid-2018. While the Sundarbans demonstrates resilience to cyclones, climate-driven degradation may erode this over time. The CCDC method, though computationally intensive, offers a powerful tool for global mangrove monitoring by leveraging every available satellite observation, bypassing cloud cover limitations.

Awty-Carroll, K., Bunting, P., Hardy, A., & Bell, G. (2019). Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region. Remote Sensing, 11(23), 2833. https://doi.org/10.3390/rs11232833