Remotely Sensed Empirical Modeling of Bathymetry in the Southeastern Caspian Sea

Abstract

Abstract:Remotely sensed imagery is proving to be a useful tool in estimating water depths in coastal zones. On the other hand, many coastal zone studies in the southern part of the Caspian Sea are only concerned with areas of shallow water and would benefit from easily updated bathymetric estimates. In this study, we tested three different methods for extracting bathymetry information from Landsat 5 data in the southeastern Caspian Sea, Iran. The first method used was a single band algorithm (SBA), utilizing either blue or red bands. The second method was principal components analysis (PCA), and the third method was the multi-layer perceptron (back propagation) neural network between visible bands and one output neuron (bathymetry). This latter MLP-ANNs method produced the best depth estimates (r = 0.94). The single band algorithm utilizing a red band also produced reasonably accurate results (r = 0.66), while the blue band algorithm and PCA did not perform (correlation between the estimated and measured depths 0.49 and 0.21, respectively). Furthermore, the shallow waters have negative influences on the accuracy of bathymetric modeling, thus the correction of data in these shallow waters is challenged by the presence of continental aerosols, bottom reflectance, and adjacency of land.

A Simple Method for Extracting Water Depth From Multispectral Satellite Imagery in Regions of Variable Bottom Type

“…Because the rate of light attenuation in water is wavelength dependent, the ratio of reflectance for two different wavelengths is a function of water depth and, in theory, less sensitive to bottom albedo. However, authors have had limited success applying the ratio algorithm to regions with highly variable bottom type (Doxani et al, 2012;Lyons et al, 2011), inspiring some to use more complex methods such as artificial neural networks and machine learning techniques (Ceyhun & Yalcin, 2010;Eugenio et al, 2015;Gholamalifard et al, 2013;Liu et al, 2015;Makboul et al, 2017;Sandidge & Holyer, 1998).…”

Assessment of Spatial Interpolation Methods to Map the Bathymetry of an Amazonian Hydroelectric Reservoir to Aid in Decision Making for Water Management

“…Currently, there are different methods described in the literature that are used to generate bathymetric maps, such as spatial interpolation methods based on collected depth samples [4], topographic data-based methods [5] and remote sensing-based methods [6,7]. Among the above cited methods, spatial interpolation methods are the most frequently used to obtain bathymetric maps [4,8,9].…”

Bathymetry of the Coral Reefs of Weizhou Island Based on Multispectral Satellite Images

“…Mohamed et al [22] improved the linear multiband method by using a fitting algorithm called Ensemble Learning (EL) and obtained better performance and accuracy than those obtained by conventional PCA and generalized linear models. Many other related studies [2,[23][24][25] were also found to focus on the improvements, comparisons, and applications of these algorithms.…”