OJCE  Vol.4 No.4 , December 2014
Using GIS for Time Series Analysis of the Dead Sea from Remotely Sensing Data
Abstract: Developed tools of Remote Sensing and Geographic Information System are rapidly spread in recent years in order to manage natural resources and to monitor environmental changes. This research aims to study the spatial behavior of the Dead Sea through time. To achieve this aim, time series analysis has been performed to track this behavior. For this purpose, fifteen satellite imageries are collected from 1972 to 2013 in addition to 2011-ASTGTM-DEM. Then, the satellite imageries are radiometrically and atmospherically corrected. Geographic Information system and Remote Sensing techniques are used for the spatio-temporal analysis in order to detect changes in the Dead Sea area, shape, water level, and volume. The study shows that the Dead Sea shrinks by 2.9 km2/year while the water level decreases by 0.65 m/year. Consequently, the volume changes by 0.42 km3/year. The study has also concluded that the direction of this shrinkage is from the north, northwest and from the south direction of the northern part due to the nature of the bathymetric slopes. In contrast, no shrinkage is detected from the east direction due to the same reason since the bathymetric slope is so sharp. The use of the Dead Sea water for industrial purposes by both Israel and Jordan is one of the essential factors that affect the area of the Dead Sea. The intensive human water consumption from the Jordan and Yarmouk Rivers for other usages is another main reason of this shrinkage in the area as well.
Cite this paper: El-Hallaq, M. and Habboub, M. (2014) Using GIS for Time Series Analysis of the Dead Sea from Remotely Sensing Data. Open Journal of Civil Engineering, 4, 386-396. doi: 10.4236/ojce.2014.44033.

[1]   Smith, R.B. (2012) Introduction to Remote Sensing. Lecture Notes.

[2]   Gong, J.Y., et al. (2008) A Review of Multi-Temporal Remote Sensing Data Change Detection Algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Hong Kong), 37, 757-762.

[3]   Singh, A. (1989) Digital Change Detection Techniques Using Remotely-Sensed Data. International Journal of Remote Sensing, 10, 989-1003.

[4]   Kandare, K. (2000) The Time Series Change Detection Methods of Remote Sensing. ISPRS SC Newsletter, Vienna.

[5]   AUG Signals (2012) Change Detection. AUG SIGNALS. (Online)

[6]   Deer, P. (1999) Digital Change Detection Techniques. Civilian and Military Application Published in the UK. Taylar & Francis Ltd., London.

[7]   Lu, D., et al. (2004) Change Detection Techniques. Taylor & Francis Ltd., London.

[8]   Richard, J., et al. (2005) Image Change Detection Algorithms. A Systematic Survey. IEEE Transactions on Image Processing, 14, 294-307.

[9]   Théau, J. (2012) Change Detection. In: Danko, D.M. and Kresse, W., Eds., Springer Handbook of Geographic Information, Springer, New York, 7.

[10]   Omar, M. (1996) Shrinking of Dead Sea Raises Deep Environmental Concerns. Christian Information Centre, Jerusalem.

[11]   Al-Zubaidy, R., Khaled, M. and Shambour, Y. (2011) Prediction of the Dead Sea Water Level Using Neural Networks. 4th International Symposium on Innovation in Information & Communication Technology, Amman, 29 November-1 December 2011, 147-154.

[12]   Abu Ghazleh, S., et al. (2010) Rapidly Shrinking Dead Sea Urgently Needs Infusion of 0.9 km 3/a from Planned Red-Sea Channel: Implication for Renewable Energy and Sustainable Development. 4, 1995-6665.

[13]   Morin, E., et al. (2009) Flash Flood Prediction in the Dead Sea Region Utilizing Radar Rainfall Data. Journal of Dead-Sea and Arava Research, 1, 1066-1076.

[14]   US Geological Survey (1998) Overview of Middle East Water Resources: Water Resources of Palestinian, Jordanian and Israeli Interest. Water Data Bank Project, Executive Action Team, New York, 41.

[15]   Green, E.P., et al. (2000) Remote Sensing Handbook for Tropical Coastal Management. UNESCO, Paris.

[16]   Chavez, Jr. (1996) Image-Based Atmospheric Corrections—Revisited and Improved. Photogrammetric Engineering and Remote Sensing, 62, 1025-1036.

[17]   Wen, W. (2008) Wetland Change Prediction Using Markov Cellular Automata Model in Lore Lindu National Park Central Sulawesi Province. Master Thesis, BOGOR Agricultural University, Indonesia.

[18]   Ramsey, R.D. (2013) Image Standardization. RS/GIS Laboratory. (Online)

[19]   Irons, J. (2013) Chapter 11. Landsat Hand Book. NASA. (Online)

[20]   Chinea. J.D. (2013) Supervised Classification. Universidad de Puerto Rico, Recinto Universitario de Mayagüez. (Online)

[21]   USGS (2008) Landsat Update. Landsat Update.

[22]   Akin, E. and Cooley, S. (2013) Lake Basin Volume. GIS 4 Geomorphology. (Online)

[23]   Cooley, S. (2013) Minimum Eroded Volume. GIS 4 Geomorphology. (Online)