The acquisition of bathymetric data in shallower waters is difficult to attain using traditional survey methods because the areas to investigate may not be accessible to hydrographic vessels, due to the risk of grounding. For this reason, the use of satellite detection of depth data (satellite-derived bathymetry, SDB) constitutes a particularly useful and also economically advantageous alternative. In fact, this approach based on analytical modelling of light penetration through the water column in different multispectral bands allows to cover a big area against relatively low investment in time and resources. Particularly, the empirical method named band ratio method (BRM) is based on the degrees of absorption at different bands. The accuracy of the SDB is not comparable with that of traditional surveys, but we can certainly improve it by choosing satellite images with high geometric resolution. This article aims to investigate BRM applied to high geometric resolution images, IKONOS-2, concerning the Bay of Pozzuoli (Italy), and improve the accuracy of results performing the determination of the relation between band ratio and depth. Two non-linear functions such as the exponential function and the 3rd degree polynomial (3DP) are proposed, instead of regression line, to approximate the relationship between the values of the reflectance ratios and the true depth values collected in measured points. Those are derived from an Electronic Navigational Chart produced by the Italian Hydrographic Office. The results demonstrate that the adopted approach allows to enhance the accuracy of the SDB, specifically, 3DP supplies the most performing bathymetric model derived by multispectral IKONOS-2 images.

Abstract. Nautical charts generally report fundamental knowledge for the safety of navigation. This information also includes sea depth data reported as depth points or contour lines, which can be used to build a 3D model of the seabed. However, there are different interpolation methods for creating digital depth models, and there is no way to know in advance which of them is the best performing. The aim of this work is to compare different spatial interpolation methods applied on a dataset concerning the seabed of the Port of Naples (Italy) and extracted from the Electronic Navigational Chart (ENC) produced by the Hydrographic Institute of the Italian Navy, in scale 1:10.000. Four deterministic interpolation methods, i.e. Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), Local Polynomial Interpolation (LPI), Radial Basis Function (RBF), and two stochastic interpolation methods, i.e. Ordinary Kriging (OK) and Universal Kriging (UK), are applied using Geographic Information System (GIS) software. Since each method requires to set specific parameters and different options are available, e.g. the order of the polynomial function for GPI and LPI, or semi-variograms for OK and UK, twenty-three models are generated. The result quality is evaluated by Leave-One-Out cross-validation and the statistics of the residuals produced by each interpolation method in the measured points are compared and analysed. The experiments confirm that the stochastic approach is more versatile compared to deterministic approach and can produce better results, as it is testified by the great performance of the Ordinary Kriging, which produces the most accurate 3D models.

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Abstract. Different algorithms are available in literature to extract coastline from remotely sensed images and different approaches can be adopted to evaluate the result accuracy. In every case, a reference coastline is suitable to compare alternative solutions: usually, the visual photointerpretation on the RGB composition of the considered imagery and the manually vectorization of the coastline allow an accurate term of comparison, but they are laborious and time consuming. This article aims to demonstrate that a smart procedure is possible using a LiDAR-generated Digital Elevation Model (Lg-DEM) as a useful source from which to rapidly extract the reference coastline. The experiments are carried out on Sentinel-2 imagery, using six indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Enhanced Vegetation Index (EVI), Red-Green Ratio (RGR) and NIR-Red Ratio (NRR). The unsupervised classification algorithm named K-Means transforms each index resulting product in two clusters, i.e. water and no-water, while the automatic vectorization allows to detect the coastline as separation between land and sea. The coastline from Lg-DEM and the manually achieved one using photointerpretation are both assumed as references for testing result accuracy. In every case, the performance analysis of the six indices products induces similar results, confirming the combination of NDWI and K-Means as the most performing approach. The tests demonstrate that, when Lg-DEM and satellite images concern the same area in the same period or in absence of variations, the coastline extracted from Lg-DEM is useful as reference to compare various methods.

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