This paper represents an advanced assessment of a satellite imagery-based vegetation transition detection method for Bangladesh's southern district of Barguna. Bangladesh has been identified as the most vulnerable country to the effects of climate change due to deteriorating vegetative cover and urbanization pushing forces. This paper aims to use GIS and remote sensing techniques to estimate vegetation change and determine the conversion pattern in the coastal district of Barguna, Bangladesh, from 1989 to 2020. The major methodology used in this research is NDVI differencing and supervised image classification to validate the conversion over time. With a combination of satellite imagery bands, NDVI uses multi-spectral remote sensing techniques to discover vegetation index, water, empty spaces, and forests. To categorize the canopy into distinct kinds, we use NDVI threshold values of 0, 0.1, 0.15, and 0.2. Our findings reveal significant geographical variations in bare regions, sparse, moderate, and thick vegetation types. We discovered that over the last 31 years, a total of 412.90 km 2 (49.25 percent) of land has been deforested, with the transition rate being particularly high in dense vegetation. This research of vegetation cover change can help predict the recurrence of natural disasters, give humanitarian assistance, and enable innovative protection tactics.

Coastal Bangladesh has experienced large scale changes in erosion and deposition in the Meghna Estuary and the big islands due to the Ganges–Brahmaputra-Meghna stream background. Also, the coastal area is prone to natural disasters almost in every year which creates a change in the ground water level, increases the surface water infiltration, soil salinity, and flood level. Considering these facts of the coastal area of Bangladesh, watershed delineation can contribute to proper planning and management of watershed to mitigate the surface and groundwater problems. Therefore, in this paper GIS and remote sensing techniques were used to identify the exact water course using spatial data to know the current watershed condition of the South Ganges Delta Region of Bangladesh. Here, Hydrology Toolset was utilized to analyze and identify correct watershed flow direction, network density, and confluence thresholds using digital elevation model (DEM) of the study area. The well-known D8 algorithm deployed to calculate the stream flow from each cell to its downslope neighbor and 100–1500 thresholds to determine the flow directions and transform the streams into line features for watershed network density measurement. The results showed that the length and density of the networks were proportional to the threshold. In consequence, the density of the stream network increased dramatically with the soaring of thresholds. Therefore, the results also revealed that when the convergence threshold set to 900, the extracted stream network appeared the closest to the exact water flow in the research area. It showed various sharp flows of the stream network, their length and density, as well as the convergence threshold. The findings of this study can help to quantify the watershed basin and river flow watercourses that can contribute to plan and manage future flood forecasting method of the study region.

The study focused on existing condition of drinking water and evaluates the performance of pond sand filters (PSFs) as a safe drinking water source of three selected Union of Dacope Upazila, Khulna in south-west coastal part of Bangladesh. This study exposed that the un-treated pond water is the main drinking water sources (54 %) in the study area. Other options for drinking water are PSF (43 %) and rain-water harvesting (RWH) systems (3%). The people who are using pond water directly as drinking water are suffered (67%) from various water-borne diseases in different times of the year. Most of the PSF users satisfied with the existing system of PSF. In most cases 85% the beneficiary’s willingness to pay for maintaining of PSFs. The water qualities were tested for the raw and treated water at various steps of the treatment process. The laboratory analysis showed that turbidity, pH, nitrate (NO3), ammonia (NH3), total dissolved solid (TDS), elec-trical conductivity (EC) and phosphate (PO4) of the PSFs water meet the Bangladesh standard, but the microbial contaminations are failed to meet the Bangladesh standard. In the raw water from three PSFs, the fecal coliform ranges are 64,122 and 136 CFU/100ml. After the treatment the fecal coliform ranges were 9, 19 and 38 CFU/100ml respectively. The outcome of the study provided information to ensure safe and adequate quantity of drinking water system in a disaster-prone coastal area of Bangladesh. It seems that if the PSF installs more in this region and takes action in regular monitoring and proper management, therefore, it will be one of the most sustainable drinking water sources for this coastal region.