Coastal erosion is one of the most significant environmental threats to coastal communities globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to assess the impacts of historical and predicted shoreline change on different land use and land cover, this study estimated historical shoreline movement, predicted shoreline positions based on historical data, and quantified and assessed past land use and land cover change. Multi-temporal Landsat images from 1988–2021 were used to quantify historical shoreline movement and past land use and land cover. A time-series classification of historical land use and land cover (LULC) were produced to both quantify LULC change and to evaluate the utility of the future shoreline predictions for calculating amounts of lost or newly added land resources by LULC type. Our results suggest that the agricultural land is the most dominant land cover/use (76.04% of the total land loss) lost over the studied period. Our results concluded that the best performed model for predicting land loss was the 10-year time depth and 20-year time horizon model. The 10-year time depth and 20-year time horizon model was also most accurate for agricultural, forested, and inland waterbody land use/covers loss prediction. We strongly believe that our results will build a foundation for future research studying the dynamics of coastal and deltaic environments.
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Localizing Socio-Environmental Problem Solving
In this paper, we describe iPlan, a web-based software platform for constructing localized, reduced-form models of land-use impacts, enabling students, civic representatives,
and others without specialized knowledge of land-use planning practices to explore and evaluate possible solutions to complex, multi-objective land-use problems in their own local contexts.
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- Award ID(s):
- 1713110
- NSF-PAR ID:
- 10341742
- Editor(s):
- Weinberger, A.; Chen, W.; Hernández-Leo, D.; Chen, B.
- Date Published:
- Journal Name:
- Computersupported collaborative learning
- ISSN:
- 1573-4552
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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