Shoreline Change Analysis with Deep Learning Semantic Segmentation Using Remote Sensing and GIS Data

Current trends in coastal erosion combined with projected climate change impacts place half of the global sandy beaches at risk of extinction. Approximately 190 million people are likely to experience coastal environmental risks by 2100, and mitigation of the risk related to coastal erosion requires insight into the underlying causes. Here we use a multi-proxy approach to determine the cause of coastal erosion on the west coast of South Africa. Erosion trends were determined using satellite analysis for a storm event in June 2017, and foredune morphology changes were measured using unmanned aerial vehicle photogrammetry following a storm in September 2023. Significant erosion was driven by intense wave energy over multiple spring tide cycles, resulting in northward sand movement. Climate change is rejected as a cause of the erosion based on optically stimulated luminescence dates that show some of the eroded sediments to be approximately 8 900 years old. Instead, the cause is thought to be the interruption of long-term sand supply. Long-term sand dynamics are seldom considered in coastal erosion vulnerability assessments, and sediment age may be a novel factor in understanding this dynamic.

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Comparative Analysis between Remote Sensing Burned Area Products in Brazil: A Case Study in an Environmentally Unstable Watershed

da Silva Junior,

Pacheco,

Ruiz-Armenteros

et al. 2024

Fire

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Forest fires can profoundly impact the hydrological response of river basins, modifying vegetation characteristics and soil infiltration. This results in a significant increase in surface flow and channel runoff. In response to these effects, many researchers from different areas of earth sciences are committed to determining emergency measures to rehabilitate river basins, intending to restore their functions and minimize damage to soil resources. This study aims to analyze the mapping detection capacity of burned areas in a river basin in Brazil based on images acquired by AMAZÔNIA-1/WFI and the AQ1KM product. The effectiveness of the AMAZÔNIA-1 satellite in this regard is evaluated, given the importance of the subject and the relatively recent introduction of the satellite. The AQ1KM data were used to analyze statistical trends and spatial patterns in the area burned from 2003 to 2023. The U-Net architecture was used for training and classification of the burned area in AMAZÔNIA-1 images. An increasing trend in burned area was observed through the Mann–Kendall test map and Sen’s slope, with the months of the second semester showing a greater occurrence of burned areas. The NIR band was found to be the most sensitive spectral resource for detecting burned areas. The AMAZÔNIA-1 satellite demonstrated superior performance in estimating thematic accuracy, with a correlation of above 0.7 achieved in regression analyses using a 10 km grid cell resolution. The findings of this study have significant implications for the application of Brazilian remote sensing products in ecology, water resources, and river basin management and monitoring applications.

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Shoreline Change Analysis with Deep Learning Semantic Segmentation Using Remote Sensing and GIS Data

Cited by 4 publications

References 28 publications

Surface water extraction from high-resolution remote sensing images based on an improved U-net network model

Surface water extraction from high-resolution remote sensing images based on an improved U-net network model

Sand budget failure underlies coastal erosion on the west coast of South Africa

Comparative Analysis between Remote Sensing Burned Area Products in Brazil: A Case Study in an Environmentally Unstable Watershed

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