Coastal areas are vulnerable to different natural hazards, including hurricanes, cyclones, tsunami, floods, coastal erosion, and saltwater intrusion. These hazards cause extensive social, ecological, economic, and human losses. Continued climate change and sea-level rise is expected to substantially impact the people living in coastal areas. Sea level rise poses serious threats for the people living in the coastal zone, which leads to coastal erosion, inundations in the low-lying areas, tidal water encroachment and subsequent salt-water intrusion, as well as the displacement of the people living along the coast. Coastal erosion is one of the biggest environmental threats in the coastal areas 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. For a lower middle-class country, such as Bangladesh, with limited internal resources, it is hard to cope with catastrophic natural hazards, such as coastal erosion and its related consequences. This research aims to advance the scientific understanding of past and future coastal erosion risk and associated changes in land change and land cover using geospatial analysis techniques. It also aims to understand the patterns and drivers of human perception of coastal erosion risk. To place the research questions and objectives in content, Chapter 1 includes a brief introduction and literature review of the coastal erosion context in Bangladesh. Chapter 2 assesses different methods of prediction to investigate 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. Chapter 3 evaluates historical land loss and how well predicted shorelines predict amounts of succeeding LULC resources lost to erosion. Chapter 4 focuses on the patterns and drivers of erosion risk perception using data from spatially explicit measures of coastal erosion risk derived from satellite imagery and a random sample survey of residents living in the coastal communities. In summary, this research advances our scientific understanding of past and future coastal erosion risk and associated changes in land change and land cover using geospatial analysis techniques. It also enhances the understanding of the patterns and drivers of human perception of coastal erosion risk by combining satellite imagery and social survey data. Compared to much of the coastal erosion literature, this work draws from a 35-year time series of satellite-derived shorelines at annual temporal resolution. This time depth enables us to employ a temporal design strategy expected to yield a robust characterization of space-time erosion patterns. This study also enabled us to assess how well predicted shorelines predict amounts of succeeding LULC resources lost to erosion by using long-term historical data. The innovative we use has potential applications to other deltas and vulnerable shorelines globally. While empirical results are specific to the project's study area, results can inform the region's shoreline forecasting ability and associated mitigation and adaptation strategies. / Doctor of Philosophy / Coastal erosion is a global problem. Coastal Bangladesh has one of the highest rates of erosion in the world. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to advance our understanding of coastal erosion hazard, this study assessed past and future coastal erosion risk and associated changes in land change and land cover and human risk perceptions using different geospatial and statistical analysis techniques. First, different methods of coastal erosion prediction were evaluated to investigate the performance of future shoreline position predictions. Second, the historical land loss was estimated and how well predicted shorelines predict amounts of succeeding LULC resources lost to erosion were assessed. Finally, the patterns and drivers of human perception of coastal erosion risk were explored.