River bank erosion is one of the frequent but the most unpredictable disasters that occur every year in Bangladesh. In this paper, Landsat TM-5 and Landsat-8 imageries from 1989 and 2015 were used to detect changes of...River bank erosion is one of the frequent but the most unpredictable disasters that occur every year in Bangladesh. In this paper, Landsat TM-5 and Landsat-8 imageries from 1989 and 2015 were used to detect changes of present land use, river erosion and bar deposition in Chowhali Upazila, Sirajganj district of Bangladesh. This study reveals that human settlement, forest, seasonal crops and agriculture features decrease, while river coverage increases dramatically. About 1340 hectare areas have been eroded, while 630 hectares are deposited as channel bar in the study area over the last 26 years. Finally, an accuracy assessment is conducted between the test data and each land use feature. The overall classification accuracy was 97% and 98% in 1989 and 2015 respectively. Moreover, 98% accuracy is found in erosion while 97% is found in bar deposition areas.展开更多
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 o...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.展开更多
文摘River bank erosion is one of the frequent but the most unpredictable disasters that occur every year in Bangladesh. In this paper, Landsat TM-5 and Landsat-8 imageries from 1989 and 2015 were used to detect changes of present land use, river erosion and bar deposition in Chowhali Upazila, Sirajganj district of Bangladesh. This study reveals that human settlement, forest, seasonal crops and agriculture features decrease, while river coverage increases dramatically. About 1340 hectare areas have been eroded, while 630 hectares are deposited as channel bar in the study area over the last 26 years. Finally, an accuracy assessment is conducted between the test data and each land use feature. The overall classification accuracy was 97% and 98% in 1989 and 2015 respectively. Moreover, 98% accuracy is found in erosion while 97% is found in bar deposition areas.
文摘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.
