No abstract

No abstract

No abstract

Mangrove vegetation plays a vital role in habitat and nursing ground for different organisms and prevents coastal erosion caused by wave and tide action. In recent years the mangrove vegetation in Chattogram coast, Bangladesh, has been interrupted by other infrastructural development, which has a destructing effect on the surrounding environment. Land surface temperature analysis of an area helps learn about different environmental conditions, weather, and climate. It is also essential to monitor the rising temperature and global warming, the biggest threat to humanity. NDBI and NDVI are the efficient process for monitoring vegetation and build up areas of a geographical location. This study focused on those analyses to understand the importance of mangrove vegetation in the Salimpur area and surrounding coastal areas of Chattogram by studying the relationship between NDVI and NDBI, NDVI and LST, NDBI, and LST. The outcome indicates that a higher vegetation index results in lower land surface temperature during different periods, negatively correlated. This study also found a strong positive correlation between buildup index (NDBI) and land surface temperature (LST), which means Land Surface temperature was found higher in Buildup areas. The vegetation areas are greatly affected by the buildup areas. The correlation between buildup areas and vegetation areas was strongly negative, which means an increase of NDBI decreases NDVI, and a decrease of NDBI increases NDVI.

Waterlogging is considered a severe environmental hazard in the Sylhet city corporation (SCC) area of northeast Bangladesh adversely affects the local socio-economic development. The integrated geospatial approach presents a worthy alternative to the conventional mapping of waterlogged surface areas. The remote sensing-based water index technique was used for delineating pre-monsoon and post-monsoon waterlogged areas of SCC in 2020 and 2021. Seasonal deviations of the waterlogging were determined by overlaying the waterlogged sites emanated from the pre-monsoon and post-monsoon periods under the GIS environment. Results show that waterlogged areas for pre-monsoon and post-monsoon seasons in 2020 are 2.25 sq. km and 4.12 sq. km, respectively, whereas 2.22 sq. km and 3.90 sq. km in 2021. Hence, the seasonal variations in waterlogged areas in 2020 and 2021 are 1.87 sq. km and 1.68 sq. km, respectively. This analysis also demonstrates that waterlogging in SCC is utterly driven by the high seasonal variations that mainly depend on the amount of rainfall besides the local flood-prone geologic settings and climate change.