Hot spot ($${G}_{i}^{{*}}$$) model for forest vulnerability assessment: a remote sensing-based geo-statistical investigation of the Sundarbans mangrove forest, Bangladesh

Abstract

The 5th category super-cyclone Sidr disrupts the world's largest mangrove forest Sundarbans on November 15, 2007. It seriously shatters about 1900 km2 that 31% of the total area of the Sundarbans. That makes a great threat to the mangrove ecosystem and biodiversity, which convey to forest vulnerability monitoring of Sundarbans. This research emphasizes on mangrove forest monitoring with vulnerability assessment using Landsat-5 and Landsat-8 remote sensing data based on geo-statistical hot spot ( $${G}_{i}^{{*}}$$ ) model, normalized difference vegetation index (NDVI) and forest discrimination index (FDI). However, the analysis works with statistical algorithm Gi(d) and G(d) in terms of geo-statistical nearest neighborhood spatial autocorrelation analysis. Hot spot ( $${G}_{i}^{{*}}$$ ) model used to explore the hot and cold confidence zone, which provided the mangrove vulnerability confidence level. The simulated, ~ 14.1% extreme safe zone is increased from 2001 to 2015 and extremely vulnerable zone also increased 4.1% at the same time, although 4.3% stable zone also decreased in that time. Even, high-density mangrove area was decreased in 2009, and the low-density mangrove area increased due to cyclone Sidr. In addition, FDI denotes the mangrove density, and NDVI provides vegetation health condition and represents the mangrove variability scenario with geospatial location those signify to detect the threatening condition of mangrove population and density. Furthermore, this study’s methods and results will provide the base for further long-term studies on this world’s largest mangrove forest and would have an implication for the mangrove monitoring and disaster risk reduction strategies.