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Remote Sens. 2013, 5(6), 2746-2762; doi:10.3390/rs5062746

Remotely Sensed Empirical Modeling of Bathymetry in the Southeastern Caspian Sea

1
Department of Environment, Faculty of Natural Resources, Tarbiat Modares University, P.O. Box 46414-356, Noor, Mazandaran 46417-76489, Iran
2
Estonian Marine Institute, University of Tartu, Mäealuse 14, Tallinn 12618, Estonia
3
Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, P.O. Box 15875-4416, Tehran 19967-15333, Iran
4
Faculty of Geo-Information Science and Earth Observation (ITC), P.O. Box 217, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 25 February 2013 / Revised: 20 May 2013 / Accepted: 20 May 2013 / Published: 30 May 2013
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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. View Full-Text
Keywords: bathymetry; estimation; satellite imagery; Landsat; artificial intelligence; Southeast Caspian Sea bathymetry; estimation; satellite imagery; Landsat; artificial intelligence; Southeast Caspian Sea
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Gholamalifard, M.; Kutser, T.; Esmaili-Sari, A.; Abkar, A.A.; Naimi, B. Remotely Sensed Empirical Modeling of Bathymetry in the Southeastern Caspian Sea. Remote Sens. 2013, 5, 2746-2762.

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