Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally.

Globally, coastal zones, rivers and riverine areas, and deltas carry enormous values for ecosystems, socio-economic, and environmental perspectives. These often highly populated areas are generally significantly different from interior hinterlands in terms of population density, economic activities, and geophysical and ecological processes. Geospatial technologies are widely used by scholars from multiple disciplines to understand the dynamic nature of shoreline changes globally. In this paper, we conduct a systematic literature review to identify and interpret research patterns and themes related to shoreline change detection from 2000 to 2021. Two databases, Web of Science and Scopus, were used to identify articles that investigate shoreline change analysis using geospatial technique such as remote sensing and GIS analysis capabilities (e.g., the Digital Shoreline Analysis System (DSAS). Between the years 2000 and 2021, we initially found 1622 articles, which were inspected for suitability, leading to a final set of 905 articles for bibliometric analysis. For systematic analysis, we used Rayyan—a web-based platform used for screening literature. For bibliometric network analysis, we used the CiteSpace, Rayyan, and VOSviewer software. The findings of this study indicate that the majority of the literature originated in the USA, followed by India. Given the importance of protecting the communities living in the riverine areas, coastal zones, and delta regions, it is necessary to ask new research questions and apply cutting-edge tools and technology, such as machine learning approach and GeoAI, to fill the research gaps on shoreline change analysis. Such approaches could include, but are not limited to, centimeter level accuracy with high-resolution satellite imagery, the use of unmanned aerial vehicles (UAV), and point cloud data for both local and global level shoreline change and analysis.

This study investigates coastal erosion, revetment as a shoreline protection strategy, and human perceptions of revetments in the Lower Meghna estuary of the Bangladesh where new revetments were recently constructed. Questions addressed were: (1) How do rates of shoreline change vary over the period 2011–2019? (2) Did new revetments effectively halt erosion and what were the magnitudes of erosion change? (3) How have erosion rates changed for shorelines within 1 km of revetments, and (4) How do households perceive revetments? High-resolution Planet Lab imagery was used to quantify shoreline change rates. Analysis of household survey data assessed human perceptions of the revetment’s desirability and efficacy. Results revealed high rates of erosion for 2011–2019 with declining erosion after 2013. New revetments effectively halted erosion for protected shorelines. Significant spatial trends for erosion rates existed for shorelines adjacent to revetments. Survey respondents overwhelmingly had positive attitudes about a desire for revetment protection; however, upstream respondents expressed a strong majority perception that revetment acts to make erosion worse. Highlights of the research include integration of remote sensing with social science methods, the timing of the social survey shortly after revetment construction, and results showing significant erosion change upstream and downstream of new revetments.

This research investigates the spatiotemporal dynamics of shoreline change and associated population impacts in deltaic Bangladesh. This region is among the world’s most dynamic deltas due to monsoon precipitation that drives tremendous discharge and sediment volumes from the Ganges–Brahmaputra–Meghna drainage basin. Theoretically, it draws on the concept of adaptive cycles that theorizes systems transitioning through phases of growth, conservation, release (collapse), and reorganization, with a focus on the cycle’s release (collapse) phase and coupled linkages between the natural system of shoreline change and social system of household behavior. We use Landsat imagery to produce and describe a thirty-year record of shoreline change for an 80-km stretch of the Lower Meghna estuary. Household survey data characterized population impacts and risk perception for a subregion with high erosion rates. Results identified significant space–time differences and patterns of shoreline change and population impacts consistent with the adaptive cycle. North, central, and south regions exhibited statistically significant differences in space–time patterns of shoreline change. Substantial numbers of households reported displacement due to riverbank erosion and high levels of experience and worry about future displacement. Results demonstrate how geospatial analysis of a multidecade record of shoreline change along with analysis of household survey data can identify regions most vulnerable to riverbank erosion with implications to inform mitigation and adaptation. This work adds empirical demonstration of coupled adaptive cycles to the literature. Limitations and complexities of the adaptive cycle framework are discussed.

Coastal residents of Bangladesh are now confronted with the increased incidence, variability, and severity of weather-related hazards and disasters due to climate change-induced sea level rise (SLR). Many researchers hold the view that as a consequence residents of such area have either already migrated to inland locations or intend to so in the near future. We examine the migration of households following a flash flood event that took place in August 2020 and address intentions for future migration in the Lower Meghna Estuary of coastal Bangladesh. The data obtained for this study include 310 household surveys, field observations, and informal discussions with respondents and local people. Based on the analysis of the field data, this empirical research found one household migrated to other district within one year after the event. When the respondents were asked about their future migration intensions, only a tiny proportion, namely 21 (6.77%) households, likely will leave the study area to settle in other districts while the remaining 289 households likely will stay in the Lakshmipur district. This finding challenges the existing narratives about vulnerability to environmentally induced migration. Moreover, it provides evidence of non-migration, which is a new as well as thriving area of investigation in relation to coastal Bangladesh.