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.

No abstract

Recently microbial fuel cells (MFCs) have been considered as an alternative power generation technique by utilizing organic wastes. In this study, an experiment was carried out to generate bioelectricity from co-digestion of organic waste (kitchen waste) and sewage sludge as a waste management option using microbial fuel cell (MFC) in anaerobic process. A total of five samples with different sludge-waste ratio were used with zinc (Zn) and cupper (Cu) as cell electrodes for the test. The trends of voltage generation were different for each sample in cells such as 350 mV, 263 mV, 416 mV maximum voltage were measured from sample I, II and III respectively. It was observed that the MFC with sewage sludge showed the higher values (around 960 mV) of voltages with time whereas 918 mV obtained with organic waste. Precisely comparing cases with varying the organic waste and sewage sludge ratio helps to find the best bioelectricity generation option. Using MFCs can be appeared as the solution of electricity scarcity along the world as an efficient and eco-friendly manner as well as organic solid waste and sewage sludge management.

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.