Remote sensing and GIS-based modelling for coastal erosion susceptibility mapping
Date
2025
Authors
Musana, Hasan
Editors
Advisors
Ostendorf, Bertram
Lewis, Megan
Lewis, Megan
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Thesis
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Abstract
Coastal erosion is a complex challenge driven by the interaction of natural forces and human activities. In recent years, it has become an urgent issue, causing significant degradation of coastal habitats and marine ecosystems. The socio-economic consequences are particularly severe in regions with established coastal economies, where essential infrastructure including residential properties, industrial developments, tourism facilities, and transportation networks are at risk. Indonesia ranks among the largest archipelagic nations globally, comprising more than 17,500 islands and a coastline extending more than 99,000 km including 893 km in the Banten Province (mainland and islands). Given this vast expanse protection of its extensive coastal regions from erosion is a national priority. Furthermore, coastal areas are also highly attractive for settlement due to the abundant resources they offer to meet human needs, making them increasingly dynamic and strategically significant for development. Consequently, nearly two-thirds of Indonesia’s population resides in coastal areas with a 2.2% annual growth rate. However, these coastal areas are also highly vulnerable to erosion, leading to environmental degradation and infrastructure damage. Examining historical coastal erosion patterns enables the prediction of areas that may be susceptible to coastal erosion in the future risks and informs mitigation strategies Several studies have assessed coastal vulnerability using various methodologies, including the widely applied Coastal Vulnerability Index (CVI). However, there is a significant gap in research aimed at identifying the factors that influence coastal erosion susceptibility in tropical coastal regions. These areas exhibit distinct characteristics compared to non-tropical coasts. This study aims to develop a model of a Tropical Coastal Susceptibility Index (TCSI), by integrating two key parameters that are intrinsically linked to the features of the tropical coast, -precipitation and mangrove extent, alongside seven CVI parameters; shoreline change rate (SCR), tidal range (TR), elevation, lithology, sea-level rise (SLR), significant wave height (SWH), land-use land-cover (LULC). The TCSI model is constructed by calculating each parameter’s value using the CVI formula, adjusted by dividing it by the mangrove parameter. TCSI values tend to be lower in regions that contain mangroves, and higher in areas that lack such habitats. To develop the TCSI, this study involved multiple methodologies including Google Earth Engine (GEE) for the analysis of satellite imagery and the application of the Digital Shoreline Analysis System (DSAS) to assess shoreline change rate. GEE facilitated the extraction of shoreline data from Landsat 5 Thematic Mapper (TM), and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) images. The Modified Normalised Difference Water Index (MNDWI) was applied to differentiate between water and non-water features within the multispectral images. The rate of shoreline change was subsequently determined using Landsat imagery from four different years (1990, 2000, 2010, and 2020) through the DSAS tools integrated within ArcGIS. Additionally, GEE was used to classify mangrove extent, and land-use and land-cover information, applying vegetation Indices (VI) and Random Forest (RF) classification for accurate data extraction. This research highlights the efficiency of the GEE platform, as it enables remote sensing analysis without the need to download extensive datasets. By using advanced geospatial methods, this study contributes to a better understanding of coastal erosion susceptibility in tropical regions, providing valuable insight for coastal risk analysis and mitigation strategies.
School/Discipline
School of Biological Sciences
Dissertation Note
Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 2025
Provenance
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