MBES-CARIS Data Validation for Bathymetric Mapping of Shallow Water in the Kingdom of Bahrain on the Arabian Gulf

Abstract

Sound navigating and ranging (SONAR) detection systems can provide valuable information for navigation and security, especially in shallow coastal areas. The last few years have seen an important increase in the volume of bathymetric data produced by Multi-Beam Echo-sounder Systems (MBES). Recently, the General Bathymetric Chart of the Oceans (GEBCO) released these MBES dataset preprocessed and processed with Computer Aided Resource Information System (CARIS) for public domain use. For the first time, this research focuses on the validation of these released MBES-CARIS dataset performance and robustness for bathymetric mapping of shallow water at the regional scale in the Kingdom of Bahrain (Arabian Gulf). The data were imported, converted and processed in a GIS environment. Only area that covers the Bahrain national water boundary was extracted, avoiding the land surfaces. As the released dataset were stored in a node-grid points uniformly spaced with approximately 923 m and 834 m in north and west directions, respectively, simple kriging was used for densification and bathymetric continuous surface map derivation with a 30 by 30 m pixel size. In addition to dataset cross-validation, 1200 bathymetric points representing different water depths between 0 and −30 m were selected randomly and extracted from a medium scale (1:100,000) nautical map, and they were used for validation purposes. The cross-validation results showed that the modeled semi-variogram was adjusted appropriately assuring satisfactory results. Moreover, the validation results by reference to the nautical map showed that when we consider the total validation points with different water depths, linear statistical regression analysis at a 95% confidence level (p < 0.05) provide a good coefficient of correlation (R 2 = 0.95), a good index of agreement (D = 0.82), and a root mean square error (RMSE) of 1.34 m. However, when we consider only the validation points (~800) with depth lower than −10 m, both R 2 and D decreased to 0.79 and 0.52, respectively, while the RMSE increased to 1.92 m. Otherwise, when we consider exclusively shallow water points (~400) with a depth higher than −10 m, the results showed a very significant R 2 (0.97), a good D (0.84) and a low RMSE (0.51 m). Certainly, the released MBES-CARIS data are more appropriate for shallow water bathymetric mapping. However, for the relatively deeper areas the obtained results are relatively less accurate because probably the MBSE did not cover the bottom in several deeper pockmarks as the rapid change in depth. Possibly the steep slopes and the rough seafloor affect the integrity of the acquired raw data. Moreover, the interpolation of the missed areas' values between MBSE acquisition data points may not reflect the true depths of these areas. It is possible also that the nautical map used for validation was not established with a good accuracy in the deeper regions.

The Contribution of Multispectral Satellite Image to Shallow Water Bathymetry Mapping on the Coast of Misano Adriatico, Italy

“…In addition to the relative SDB obtained from the ln(blue)/ln(green), additional two band ratios (ln(blue)/ln(red) and ln(green)/ln(red)) were calibrated to the local datum, as expressed in Equations (9) and (10) The common use of blue and green ratio in absolute SDB studies is based on the fact that they can penetration deeper in the water and hence allows offshore bathymetry mapping, which can reach 30 m in very clear water. However the use of blue and red band ratio in shallow turbid water can provide more precise water depth information due to the water reflectance in red enhanced by the presence of sediment particles, normally absorbed by clear water.…”

“…Accurate and reliable bathymetry information is important for coastal erosion studies, maritime navigation, the mapping and monitoring of benthic habits, dredging planning, and coastal management in general. Traditional methods for bathymetry surveys use boats and ships equipped with multi-beam echo sounder or single beam echo sounder [9,10] or topo-bathymetry Lidar [11,12]. Such methods are able provide accurate water depth information; however, they are expensive and inefficient in hard-to-reach shallow waters [13].…”

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

“…Niu et al [26] used the feed-forward neural network (FFNN) and support vector machine (SVM) classifiers to infer ship location from acoustical data in a deep waterway channel. Bannari et al [27] suggested an approach for bathymetric mapping of shallow water of Arabian Gulf near Bahrain using the kriging procedure. Kim and Lee [28] proposed the ship traffic extraction neural network (STENet) to forecast the maritime traffic of the caution area, which represented an increased risk for ships, due to things such as berth limitations.…”

Earth observation applications for coastal sustainability: potential and challenges for implementation

“…Traditionally, active remote sensing methods, such as ship-borne multi-beam echo sounder (MBES) and airborne light detection and ranging (LiDAR), have achieved accurate bathymetry results (Calder and Mayer 2003;Van Son et al 2009;Kennedy et al 2014). However, these conventional methods are very expensive, time-consuming, often require special permissions to work in sensitive coastal areas, and, in the case of MBES, inapplicable to shallow waters (e.g., Gao 2009;Monteys et al 2015;Bannari and Kadhem 2017).…”