Shoreline Detection Accuracy from Video Monitoring Systems
Por:
Arriaga, Jaime, Medellin, Gabriela, OJEDA, ELENA, SALLES, PAULO
Publicada:
1 ene 2022
Resumen:
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).
Filiaciones:
Arriaga, Jaime:
Delft Univ Technol, Fac Civil Engn & Geosci, NL-2628 CN Delft, Netherlands
Medellin, Gabriela:
Univ Nacl Autonoma Mexico, Inst Ingn, Sisal 97355, Mexico
Natl Coastal Resilience Lab LANRESC, Sisal 97355, Mexico
OJEDA, ELENA:
Univ Caen Normandie, CNRS, UMR 6143, Lab Morphodynam Continentale & Cotiere, F-14000 Caen, France
SALLES, PAULO:
Natl Coastal Resilience Lab LANRESC, Sisal 97355, Mexico
Univ Nacl Autonoma Mexico, Inst Ingn, Sisal 97355, Mexico
Green Published, gold
|