Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Detection of Environmental Degradation of Satkhira District, Bangladesh Through Remote Sensing Indices
Abstract Satkhira is one of the most vulnerable coastal districts of Bangladesh due to both natural disasters and anthropogenic causes, which faces continuous environmental degradation. This study aims to explore the environmental changes in the Upazilas of Satkhira district by employing several remote sensing indices using the Landsat images of the year 2007, 2010, 2013, and 2016. NDVI, NDWI, NDSI, NDBI, and NDBaI are used to extract the spatial information regarding the condition of vegetation, wetlands, soil salinity, built up area and bare lands in the Upazilas respectively. Temporal change of these variables has been monitored and compared among and within the Upazilas on the basis of the threshold values of the indices. Analysis of NDVI has revealed that there was a drastic change in vegetation from 2007 to 2010, which was because of the cyclones. Though NDVI of 2013 showed a positive increase from 2010, it cannot restore its previous state not even in 2016. Analysis of NDBI and NDBaI have revealed that built up area has been increased day by day; whereas, a decreasing trend has been seen in case of bare lands, as the bare lands are occupied either by built up area or by shrimp farming area. Increasing NDWI and NDSI justify the increasing shrimp farming trend in Satkhira. These variables indicate the changing nature of land use land cover and the vulnerability due to environmental degradation in Satkhira district, which reveals a need for immediate land use planning. This study will help the policy makers and land use manager to promote substantial and sustainable development plan for Satkhira district.
Detection of Environmental Degradation of Satkhira District, Bangladesh Through Remote Sensing Indices
Abstract Satkhira is one of the most vulnerable coastal districts of Bangladesh due to both natural disasters and anthropogenic causes, which faces continuous environmental degradation. This study aims to explore the environmental changes in the Upazilas of Satkhira district by employing several remote sensing indices using the Landsat images of the year 2007, 2010, 2013, and 2016. NDVI, NDWI, NDSI, NDBI, and NDBaI are used to extract the spatial information regarding the condition of vegetation, wetlands, soil salinity, built up area and bare lands in the Upazilas respectively. Temporal change of these variables has been monitored and compared among and within the Upazilas on the basis of the threshold values of the indices. Analysis of NDVI has revealed that there was a drastic change in vegetation from 2007 to 2010, which was because of the cyclones. Though NDVI of 2013 showed a positive increase from 2010, it cannot restore its previous state not even in 2016. Analysis of NDBI and NDBaI have revealed that built up area has been increased day by day; whereas, a decreasing trend has been seen in case of bare lands, as the bare lands are occupied either by built up area or by shrimp farming area. Increasing NDWI and NDSI justify the increasing shrimp farming trend in Satkhira. These variables indicate the changing nature of land use land cover and the vulnerability due to environmental degradation in Satkhira district, which reveals a need for immediate land use planning. This study will help the policy makers and land use manager to promote substantial and sustainable development plan for Satkhira district.
Detection of Environmental Degradation of Satkhira District, Bangladesh Through Remote Sensing Indices
Tauhid Ur Rahman, M. (Autor:in) / Ferdous, Jannatul (Autor:in)
13.05.2018
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Coastal Bangladesh , Environmental degradation , Shrimp farm , Remote sensing indices Engineering , Civil Engineering , Geotechnical Engineering & Applied Earth Sciences , Remote Sensing/Photogrammetry , Hydrology/Water Resources , Climate Change/Climate Change Impacts , Image Processing and Computer Vision
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