摘要
Environmental degradation is a burning issue in Bangladesh. The degradation process is extremely acute in the north-western part of Bangladesh due to many environmental and anthropogenic reasons. However, initiative of research work on this issue is very crucial and urgent for regional and local planning and management. In this paper, Nachole and Niamotpur Upazilas were considered as the study area to identify and quantify environmental degradation using an integrated Geographic Information System (GIS) and Remote Sensing (RS) technique. The results of the study reveal that the area is one of the most vulnerable areas in terms of land degradation that already affected local agriculture, biodiversity, water supply and overall socio-economic livelihoods. From the modelling results, about 66,301 hectares (90%) of land are vulnerable to land degradation, of which 24,736, 40,309 and 256 hectares of land were classified as severely, highly, and moderately vulnerable areas respectively. The overall image classification accuracy for all the resultant images was 95.40% while kappa coefficient was 0.94.
Environmental degradation is a burning issue in Bangladesh. The degradation process is extremely acute in the north-western part of Bangladesh due to many environmental and anthropogenic reasons. However, initiative of research work on this issue is very crucial and urgent for regional and local planning and management. In this paper, Nachole and Niamotpur Upazilas were considered as the study area to identify and quantify environmental degradation using an integrated Geographic Information System (GIS) and Remote Sensing (RS) technique. The results of the study reveal that the area is one of the most vulnerable areas in terms of land degradation that already affected local agriculture, biodiversity, water supply and overall socio-economic livelihoods. From the modelling results, about 66,301 hectares (90%) of land are vulnerable to land degradation, of which 24,736, 40,309 and 256 hectares of land were classified as severely, highly, and moderately vulnerable areas respectively. The overall image classification accuracy for all the resultant images was 95.40% while kappa coefficient was 0.94.
