Bathymetry from shallow coastal environment using Landsat 8  (Case Study: Southeastern part of the Caspian Sea)

Abdollahi Kakroodi, Ataollah; Amini, Leila; Hasanlou, Mahdi

doi:10.22113/jmst.2019.136043.2177

Bathymetry from shallow coastal environment using Landsat 8 (Case Study: Southeastern part of the Caspian Sea)

Document Type : Original Manuscript

Authors

Ataollah Abdollahi Kakroodi ¹

Leila Amini ¹

Mahdi Hasanlou ²

¹ Department Remote Sensing and GIS, Faculty of Geography,.University of Tehran, Tehran, Iran.

² School of Survey and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

10.22113/jmst.2019.136043.2177

Abstract

In order to study in shallow coastal areas, free Landsat 8 image with relatively high radiometric resolution and the presence of two bands, coastal blue and blue is suitable. In this study, in addition to Landsat satellite imagery, hydrographic data has also been used. In order to increase the accuracy and reduce the error, tried as much as possible the passing time of the Landsat 8 sensor close to the acquisition terrain date. The purpose of this study is bathymetry of the southeastern part of the Caspian Sea using the PCA algorithm on the pre-processed visible region. In this study, the FLAASH and Dark Object Subtract (DOS) atmospheric corrections were applied separately to visible bands. The obtained depth results by applying PCA transformation to these two types atmospheric correction is investigated. PCA algorithm is implemented in four different forms. Statistical parameters R^2, RMSE, and NRMSE are calculated between obtained data by PCA and hydrographic data in two types of atmospheric correction. The results show that in both of atmospheric correction, the accuracy of estimated depth by PCA is increased when four or three PCA components are introduced as algorithm inputs compared with only two or one PCA components are used. As well as, the use of four components, the accuracy of DOS in bathymetry with values of R2=0.91, RMSE=0.3,and NRMSE=0.05 has shown a better result in comparison to FLAASH correction's values R2=0.87, RMSE=0.38, and NRMSE=0.06.

Keywords

bathymetry

Landsat-8

hydrographic data

PCA analysis

20.1001.1.20088965.1400.20.3.8.2

Subjects

اقیانوس‌شناسی ماهواره‌ای

Abrahimikia, M., Saadat Seresht, M. and Taj Firooz, B. 2012. Evaluating Bathymetry Methods by Satellite Imagery, Conference of Geomatic. National Cartographic Center. 88.

Amini, J. 2009. Computer Processing of Remote Sensed Images. University of Tehran Press. Tehran. 270p.

Doxani, G., Papadopoulou, M., Lafazani, P., Pikridas, C., Tsakiri-Strati, M. 2012. Shallow-Water Bathymetry Over Variable Bottom Types Using Multispectral WorldView-2 Image. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B8: 159–164.

Fatemi, S. B. and Rezaei, Y. 2012. Principles of Remote Sensing. Azadeh Press, Tehran. 123-148 p.

FLAASH Module User’s Guide, ENVI FLAASH Version 4.2 August. 2005 Edition.

Gholamalifard, M., Kutser, T., Esmaili-Sari, A., Abkar, A. A., and Naimi, B., 2013, Remotely Sensed Empirical Modeling of Bathymetry in the Southeastern Caspian Sea, Remote Sensing. 2013. 5: 2746-2762. doi: 10.3390/rs 50 62746.

Hedley, J. D., Harborne, A. R and Mumby, P. J. 2005. Simple and robust removal of sun glint for mapping shallow-water benthos. International Journal of Remote Sensing.

Gao, J. Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Geography. 33(2009):103–116.

Jagalingam, P., Akshaya, B. J. and Arkal, V, H. 2015, Bathymetry mapping using Landsat 8 Satellite Imagery, 8th International Conference on Asian and Pacific Coasts (APAC 2015).

Kakroodi, A.A., Leroy, S.A.G., Kroonenberg, S.B., Lahijani, H.A.K., Alimohammadian, H., Boomer, I., Goorabi, A., 2015. Late Pleistocene and Holocene sea-level change and coastal palaeoenvironment evolution along the Iranian Caspian shore. Marine Geology 361, 111e125.

Khan, M. A., Fadlallah, Y. H., and Al-Hinai, K. G. 1992. Thematic mapping of stibtidal coastal habitats in the western Arabia Gulf using Landsat TM data-Abu Ali Bay, Saudi Arabia. Lnternational Journal of Remote Sensing, 13: 605-614.

Lyzenga, D. R. 1978. Passive remote sensing techniques for mapping water depth and bottom features. Applied optics. 17: 379–383.

Nazeer, M., Nichols, J. E., and Yung, Y. 2014. Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. International Journal of Remote Sensing. 35(16): 6271–6291.

Philpot, W. D. 1989. Bathymetric Mapping with Passive Multispectral Imagery. Applied Optics. 28(8): 1569–1578. doi:10.1364/AO.28.001569.

Spitzer, D., and R. J. Dirks. 1986. Shallow Water Bathymetry and Bottom Classification by Means of the Landsat and SPOT Optical Scanners. In 1986 International Symposium/Innsbruck. International Society for Optics and Photonics. 136–138.

Stumpf, R. P., Holderied, K, and Sinclair, M. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography. 48 (1, part 2): 547–556.

Yuan, J., and Niu, Z. 2008. Evaluation of Atmospheric Correction Using FLAASH. In Paper Presented at International Workshop on Earth Observation and Remote Sensing Applications, Beijing, June 30–July 2: 1–6. IEEE. doi: 10.1109/EORSA.2008.4620341.

Zonn, I. S., Kostianoy, A. G., Kosarev, A. N and Glantz, M. H. 2010. The Caspian Sea Encyclopedia.