Long-Term Monitoring of Mangrove Resilience in the Sundarbans after Cyclone Sidr and Aila using Landsat-Derived Vegetation Indices

Abstract

The present work aims at assessing vegetation patterns and of the recovery process over the long term (2006 to 2025) in the Sundarbans mangroves based on the NDVI and SAVI. Landsat 5 TM and Landsat 8 OLI surface reflectance images were processed in Google Earth Engine to derive seasonal composites for the dry season (December–February). A supervised classification method was used to delineate five land-cover classes, namely water bodies, bare soil, sparse, intermediate, and dense vegetation. Accuracy assessment was carried out by visual interpretation of the sample points by using Google Earth Pro where overall accuracy was in the 88–93% over the entire study period. In 2006, dense vegetation was the most dominant (~68%) and sparse and intermediate other categories had low frequency and water bodies covered 21% of plots. For post-Sidr in 2008, nearly all plants showed more severe damage (76-79%). Post-Aila (2010) data suggested continuous intermediate (46%) and sparse (25%) vegetation cover but with negligible closed canopy. During 2015, the dense vegetation recovered to 60%, and dynamic changes among dense, intermediate, and sparse vegetation areas emerged, and the area of dense vegetation was up to 67% in 2025 indicating that the long-term restoration exhibits space heterogeneity. NDVI was effective for monitoring the overall trend of large scale canopy, while SAVI was able to capture very small scale regeneration and understory growth. The findings show the impressive resilience of the Sundarbans and the significance of such key ecological processes as canopy recovery and succession, and the need for more adaptive management to improve mangrove resilience in cyclone-prone coastal areas