Comparative study of approaches to bathymetry detection in Nasser/Nubia Lake using multispectral SPOT-6 satellite imagery

Hassan Mohamed, Abdelazim Negm, Kazuo Nadaoka, Tarek Abdelaziz, Mohamed Elsahabi
Received 2015/12/01, Accepted 2016/02/23, Published 2016/03/25

Hassan Mohamed1), Abdelazim Negm1), Kazuo Nadaoka2), Tarek Abdelaziz3), Mohamed Elsahabi4)

1) Department of Environmental Engineering, Egypt-Japan University of Science and Technology, Egypt
2) Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology
3) Nile Research Institute, Ministry of Water Resources and Irrigation, Egypt
4) Department of Civil Engineering, Aswan University, Egypt

Collecting and analysing bathymetric information is essential for lake management. This is particularly true regarding Lake Nasser/Nubia in Egypt, where accumulated sediment in the lake must be examined. This is typically accomplished through field measurements, which are time consuming and costly. However, remotely sensed imagery provides wide coverage, low cost, and time-saving solutions for bathymetric measurements, especially in shallow areas with high erosion or sediment accumulation, such as at the entrance of Lake Nasser/Nubia. In this study, bagging (Bag) and least square boosting (LSB) fitting algorithms that use reflectance of green and red band logarithms, green/red band logarithms ratio, and blue/red band logarithms ratio are proposed for bathymetry detection. For validation, the proposed approaches were compared with the ratio method (RM) and neural network (NN) conventional methods. Bathymetric data obtained from all methods using SPOT-6 imagery were evaluated by means of global positioning system (GPS) and echo sounder data field measurements. The Bag ensemble outperformed all methods with 0.85 m RMSE, whereas RM, LSB, and NN yielded 1.03, 0.99, and 0.97 m respectively. The results showed that the proposed approaches outperform and are more accurate than RM conventional method and the Bag approach is more accurate than the NN model when applied over shallow water depths of up to 6.5 m.

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Copyright (c) 2016 Japan Society of Hydrology and Water Resources

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