Detecting Coastal Changes Through Photogrammetry: Integrating Historical Imagery and Recent LiDAR Data
T.S. Tang (TU Delft - Civil Engineering & Geosciences)
R. C. Lindenbergh – Mentor (TU Delft - Optical and Laser Remote Sensing)
José A.Á. Antolínez – Mentor (TU Delft - Coastal Engineering)
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Abstract
This study evaluates the feasibility of applying photogrammetry techniques to reconstruct historical coastal topography and assess decadal scale coastal change from historical aerial images, focusing on the Dutch coastal of Westkapelle. The workflow was validated on two benchmark datasets, Benchmark Toronto and Benchmark Westkapelle, to verify registration accuracy under ideal acquisition conditions before being applied to the Westkapelle test dataset for change detection.
The reconstruction pipeline employed Structure from Motion and Multi-View Stereo algorithms, followed by a two stage point cloud registration. Coarse alignment was achieved through Sample Consensus Initial, and fine registration used the Iterative Closest Point algorithm. In the two benchmark datasets, registration achieved sub-metre mean C2C distances: 0.790 m for Toronto, 0.626 m for Westkapelle. In the test dataset, the mean C2C distance improved from 10.287 m before registration to 2.025 m afterwards, with 95% of points within 6 m. DEM differencing, supported by JARKUS cross-shore transect profiles, revealed systematic elevation gains of up to +10 m along foredune ridges, primarily resulting from a combination of documented coastal nourishment and natural process between 1990 and 2020.
However, limitations of the historical dataset, including sparse image coverage, strongly oblique viewing geometry, lack of vertical imagery, and poor GCP distribution, introduced geometric distortions and inconsistencies. The resulting orthophoto contained substantial voids and warped features, particularly in urban areas and low texture surfaces, underscoring the challenges of dense stereo matching under suboptimal imaging conditions.
Despite these constraints, this study demonstrates that meaningful reconstructions of past coastal environments are achievable when supported by careful preprocessing, robust registration, and multi source validation. The proposed workflow offers a transferable approach for extracting geomorphic insights from historical imagery in other coastal settings.