USE OF REMOTE SENSING TECHNIQUES AND NUMERICAL MODELLING TO PREDICT COASTAL EROSION IN VIETNAM
PDF

How to Cite

Sokolewicz, M., Bergsma, L., Schemmekes, L., Nguyen, H., & Boersen, S. (2020). USE OF REMOTE SENSING TECHNIQUES AND NUMERICAL MODELLING TO PREDICT COASTAL EROSION IN VIETNAM. Coastal Engineering Proceedings, (36v), papers.65. https://doi.org/10.9753/icce.v36v.papers.65

Abstract

Accurate prediction of coastal erosion is of importance for the investment planning of measures enhancing resilience to natural hazards. Field data on historical is generally lacking. Recent advances in deriving historical shoreline position from freely available satellite images combined with numerical modelling of shoreline erosion provide a reliable method for prediction of coastal erosion. This paper discusses the available tools and presents their application in a study case in Quang Ngai City, Vietnam.

Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/oOk9-bmDorA
https://doi.org/10.9753/icce.v36v.papers.65
PDF

References

Bruun, P. 1954. Coast erosion and the development of beach profiles. Beach erosion board technical memorandum. No. 44. U.S. Army Engineer Waterways Experiment Station. Vicksburg, MS.’

Canny, J., 1986. A computational approach to edge detection, IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, pp. 679-698.

Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., and van de Giesen, N. 2016. A 30 m resolution surface water mask including estimation of positional and thematic differences using Landsat 8, SRTM and OpenStreetMap: A case study in the Murray‐Darling Basin, Australia. Remote Sensing, 8(5), 386. Luijendijk, A., Hagenaars, G., Ranasinghe, R., Baart, F., Donchyts, G. and Aarninkhof, S. 2016. The State of the World’s Beaches. Sci Rep 8, 6641.

Ly, N. T. H., Hoan, N. T. and Dung. N. M. 2016 A practical approach to determine typhoon-induced design wave conditions at nearshore areas, Ninth International Conference on Coastal and Port Engineering in Developing Country, Rio de Janeiro, Brasil. Otsu, N. 1979. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics Vol. SMC-9, No. 1.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., 2011. Scikit-learn machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830. USACE. 1984. Shore protection manual. Coastal Engineering Research Center (CERC), US Army Corps of Engineers Research and Development Center, Coastal and Hydraulics Laboratory, v. 2, Vicksburg, Mississippi.

USACE. 2012. Coastal Engineering Manual, EM 1110-2-1100, US Army Corps of Engineers, April 2002.

Van der Walt, S., Schonberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T., 2014. scikit-image: image processing in Python. PeerJ 2, e453. Vousdoukas, M.I., Ranasinghe, R., Mentaschi, L., Plomaritis, T.A., Athanasiou, P., Luijendijk, A. and Feyen, L. 2020. Sandy coastlines under threat of erosion. Nat. Clim. Chang. 10, 260–263

Vos, K., Splinter, K.D., Harley, M.D., Joshua, A.S., Turner, S.I. 2019. CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environ. Model. Softw., 122, 104528.

Xu, H., 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.