Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, Bangladesh
Agricultural transformation is one of the important factors of rural planning and sustainable land management. There are natural and man-made reasons of this transformation, which brings both positive and negative impacts on the physical environment, food security, and human livelihoods. This study...
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Format: | Article |
Language: | English |
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Elsevier
2021-12-01
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Series: | Environmental Challenges |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2667010021003747 |
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author | Faruk Hossain Md Masud Parves Rana Md Moniruzzaman |
author_facet | Faruk Hossain Md Masud Parves Rana Md Moniruzzaman |
author_sort | Faruk Hossain |
collection | DOAJ |
description | Agricultural transformation is one of the important factors of rural planning and sustainable land management. There are natural and man-made reasons of this transformation, which brings both positive and negative impacts on the physical environment, food security, and human livelihoods. This study aims to explore a case of land use conversion from traditional agricultural land to the man-made wetland for fisheries in Rajshahi, Bangladesh. The conversion might be a result of purposeful land use and land cover changes to continue agricultural production for the market demands. A hybrid model of logistic regression and water index has been used to investigate the dynamics of LULC (Land Use Land Cover) changes. Moderate-resolution multi-temporal Landsat imageries of 2000, 2010, and 2020 have been used for visual interpretation and quantitative analysis. During the study period of 20 years, wetlands for entrepreneurial pond culture were remarkably increased, while agricultural land and vegetation experienced a decreasing trend. It is predicted that wetlands are going to be further increased by diminishing agricultural land and vegetation until 2030. Despite the fact of suitable data unavailability, this study also shows that the remote sensing approach provides a powerful tool for analysing and monitoring spatiotemporal agricultural transformation. Moreover, the empirical findings of land use modelling might be useful for agricultural planning and decision-making in rural Bangladesh. |
first_indexed | 2024-12-22T01:40:05Z |
format | Article |
id | doaj.art-1c6c06e3ce2742648634a8706aa38e30 |
institution | Directory Open Access Journal |
issn | 2667-0100 |
language | English |
last_indexed | 2024-12-22T01:40:05Z |
publishDate | 2021-12-01 |
publisher | Elsevier |
record_format | Article |
series | Environmental Challenges |
spelling | doaj.art-1c6c06e3ce2742648634a8706aa38e302022-12-21T18:43:16ZengElsevierEnvironmental Challenges2667-01002021-12-015100400Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, BangladeshFaruk Hossain0Md Masud Parves Rana1Md Moniruzzaman2Department of Geography and Environmental Studies, University of Rajshahi, Rajshahi-6205, BangladeshCorresponding author at: University of Rajshahi, Bangladesh.; Department of Geography and Environmental Studies, University of Rajshahi, Rajshahi-6205, BangladeshDepartment of Geography and Environmental Studies, University of Rajshahi, Rajshahi-6205, BangladeshAgricultural transformation is one of the important factors of rural planning and sustainable land management. There are natural and man-made reasons of this transformation, which brings both positive and negative impacts on the physical environment, food security, and human livelihoods. This study aims to explore a case of land use conversion from traditional agricultural land to the man-made wetland for fisheries in Rajshahi, Bangladesh. The conversion might be a result of purposeful land use and land cover changes to continue agricultural production for the market demands. A hybrid model of logistic regression and water index has been used to investigate the dynamics of LULC (Land Use Land Cover) changes. Moderate-resolution multi-temporal Landsat imageries of 2000, 2010, and 2020 have been used for visual interpretation and quantitative analysis. During the study period of 20 years, wetlands for entrepreneurial pond culture were remarkably increased, while agricultural land and vegetation experienced a decreasing trend. It is predicted that wetlands are going to be further increased by diminishing agricultural land and vegetation until 2030. Despite the fact of suitable data unavailability, this study also shows that the remote sensing approach provides a powerful tool for analysing and monitoring spatiotemporal agricultural transformation. Moreover, the empirical findings of land use modelling might be useful for agricultural planning and decision-making in rural Bangladesh.http://www.sciencedirect.com/science/article/pii/S2667010021003747Agricultural landConversionWater indexLULCWetlandsFish farming |
spellingShingle | Faruk Hossain Md Masud Parves Rana Md Moniruzzaman Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, Bangladesh Environmental Challenges Agricultural land Conversion Water index LULC Wetlands Fish farming |
title | Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, Bangladesh |
title_full | Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, Bangladesh |
title_fullStr | Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, Bangladesh |
title_full_unstemmed | Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, Bangladesh |
title_short | Modelling agricultural transformation: A remote sensing-based analysis of wetlands changes in Rajshahi, Bangladesh |
title_sort | modelling agricultural transformation a remote sensing based analysis of wetlands changes in rajshahi bangladesh |
topic | Agricultural land Conversion Water index LULC Wetlands Fish farming |
url | http://www.sciencedirect.com/science/article/pii/S2667010021003747 |
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