Shoreline Detection Accuracy from Video Monitoring Systems
Jaime Arriaga Garcia (Environmental Fluid Mechanics)
Gabriela Medellin (The National Coastal Resilience Laboratory (LANRESC), Mexican National Council for Science and Technology, Universidad Nacional Autónoma de México)
Elena Ojeda (Université de Caen Normandie)
Paulo Salles (Mexican National Council for Science and Technology, The National Coastal Resilience Laboratory (LANRESC), Universidad Nacional Autónoma de México)
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Abstract
Video monitoring has become an indispensable tool to understand beach processes. However, the measurement accuracy derived from the images has been taken for granted despite its dependence on the calibration process and camera movements. An easy to implement self-fed image stabilization algorithm is proposed to solve the camera movements. Georeferenced images were generated from the stabilized images using only one calibration. To assess the performance of the stabilization algorithm, a second set of georeferenced images was created from unstabilized images following the accepted practice of using several calibrations. Shorelines were extracted from the images and corrected with the measured water level and the computed run-up to the 0 m contour. Image-derived corrected shorelines were validated with one hundred beach profile surveys measured during a period of four years along a 1.1 km beach stretch. The simultaneous high-frequency field data available of images and beach surveys are uncommon and allow assessing seasonal changes and long-term trends accuracy. Errors in shoreline position do not increase in time suggesting that the proposed stabilization algorithm does not propagate errors, despite the ever-evolving vegetation in the images. The image stabilization reduces the error in shoreline position by 40 percent, having a larger impact with increasing distance from the camera. Furthermore, the algorithm improves the accuracy on long-term trends by one degree of magnitude (0.01 m/year vs. 0.25 m/year).
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