Coastal Change in Aruba and Bonaire
A Remote Sensing Assessment of Shoreline Change, Development, and Vegetation
J.C.M. Winterdal (TU Delft - Architecture and the Built Environment)
R.C. Lindenbergh – Mentor (TU Delft - Civil Engineering & Geosciences)
A. Rafiee – Mentor (TU Delft - Architecture and the Built Environment)
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Abstract
Coastal environments on small Caribbean islands are shaped by both natural processes and human activity, and both are expected to intensify with climate change and continued development. This thesis examines how coastal change between 2016 and 2023 varied across six coastal sections of Aruba and Bonaire, three on each island, selected to represent contrasting coastal types, levels of development, vegetation cover and exposure.
Shoreline positions were extracted from PlanetScope imagery using the Normalized Difference Water Index (NDWI). Change was then measured along transects, with summer and winter observations analysed separately. Building change was derived from the Google Open Buildings Temporal dataset, and vegetation extent was classified from PlanetScope imagery using the Normalized Difference Vegetation Index (NDVI). The three indicators were then compared spatially within and between sections.
Shoreline displacement varied considerably both between and within sections. Two of the Aruba sections showed mean landward displacement, while the Bonaire sections showed more mixed behaviour, often differing between seasons. Detected building area increased mainly in sections that were already developed, and detected vegetation extent increased in all six sections. The indicators did not follow a common spatial pattern, and where building growth and shoreline displacement occurred in the same section, this did not imply a causal link. Several mean displacements were similar in size to the shoreline extraction error, so small changes could not be interpreted reliably.
The results suggest that coastal change on both islands depends on local combinations of coastal setting, exposure, development and vegetation. Combining remote-sensing indicators is useful for comparing spatial patterns across sections, but the interpretation depends strongly on positional accuracy and image quality.