LANDSAT TM-8 DATA FOR RETRIEVING SALINITY IN AL-HUWAIZAH MARSH, SOUTH OF IRAQ

Authors

  • Hashim Ali Hasab Ministry of Higher Education & Scientific Research, Foundation of Technical Education, Baghdad, Iraq
  • Anuar Ahmad Faculty of Geoinformation Science and Real Estate (FGHT), Universiti Teknologi Malaysia, UTM Johor Bahru, Malaysia
  • Maged Marghany Geoscience and Digital Earth Center (Geo-DEC) Research Institute for Sustainability and Environment
  • Abdul Razzak Ziboon Ministry of Higher Education, University Technology, Construction and Building Dept, Baghdad, Iraq

DOI:

https://doi.org/10.11113/jt.v75.3988

Keywords:

Water quality, salinity modeling, remote sensing, NDVI, GIS

Abstract

Mesopotamia marshlands constitute the largest wetland ecosystem in the Middle East and western Eurasia. These marshlands are located at the confluence of Tigris and Euphrates rivers in southern Iraq. Al-Huwaizah marsh is the biggest marsh in southern Iraq covered by an area (2400 Km2-3000 Km2) and depth (1.5 m-5 m). The construction dams by Turkey and Syrian for water storage as well as hydroelectric power generation along Tigris and Euphrates rivers, led to reduce and deteriorate water quality in Iraq's marshes. Salinity has become one of the major problems affecting crop production and food security in central and southern Iraq. The objective of this study to develop a new algorithm to retrieve salinity and normalized difference vegetation index (NDVI) from optical remote sensing Landsat-8 (OLI/TIRS) data based on differential equations algorithms. The mathematical algorithms are linear, power and exponential algorithm. The integration between remote sensing techniques and geographic information system (GIS) to map hydrodynamic and the spatial variation of salinity distribution. There is a pressing need to quantify and map the spatial extent and distribution of the salinity in Al-Huwaizah marsh of southern Iraq during March-2013. The findings of this study proved that the integration between Landsat-8 data and GIS with salinity algorithms could provide a powerful tool for retrieving salinity in marshes zone.

References

John, P. K., John R. S. and Chester, F.2007. Hyperspectral Imaging Systems. 2007. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY. 19-40.

Abdul Jabbar, M. F., Al-Ma'amar A. F. and Shehab, A. T.. 2010. Change Detections in Marsh Areas, South Iraq, Using Remote Sensing and Gis Applications, Information Department/ Remote Sensing Division in the State Company of Geological Survey and Mining, Iraq. Iraqi Bulletin of Geology and Mining. 6(2): 17-39.

Curtiss O. D. and Gia M. L. 2004. Remote Sensing for Natural Resource Management and Environmental Monitoring. Naval Research Laboratory Washington, Ch8, (Costal Margins and Estuaries). 418-425.

Lowi, M. R. 1995. Rivers of Conflict, Rivers of Peace. Journal of International Aflairs; Summer 49: 1.

Lowi, M. R. 1995. Rivers of Conflict, Rivers of Peace. Journal of International Aflairs; Summer. 49: 1.

Ammenberg, P., P. Flink, T. Lindell, D. Pierson, and N. Strombeck. 2002. Bio-optical Modelling Combined with Remote Sensing to Assess Water Quality. International Journal of Remote Sensing. 23(8): 1621-1638.

Saad, M. and Gaber, A. 2013. Remote Sensing Application for Water Quality Assessment in Lake Timsah, Suez Canal, Egypt. Journal of Remote Sensing Technology. 1(3): 61-74.

Bunker, A. L. 2004. Protection of the Environment During Armed Conflict: One Gulf, Two Wars. Review of European Community & International Environmental Law. 13(2): 201-213.

Kerekes, J. P. and Baum, J. E. 2005. Full-spectrum Spectral Imaging System Analytical Model. Geoscience and Remote Sensing, IEEE Transactions. 43(3): 571-580.

Susan, L. U. 2004. Remote Sensing for Natural Resource Management and Environmental Monitoring. 2004. University of California, Davis. 345-400.

Zacharias, I. and Gianni, A. 2008. Hydrodynamic and Dispersion Modeling as a Tool for Restoration Of Coastal Ecosystems. Application to a Re-Flooded Lagoon. Environmental modelling and Software. 23: 751-767.

Paul M. M. and Magaly, K. 2011. Computer Aprocessing of Remotely-sensed Images an Introduction. Center for Remote Sensing, Boston University. 23-27.

Nas, B., Ekercin, S., Karabörk, H., Berktay, A., & Mulla, D. J. 2010. An Application of Landsat-5TM Image Data for Water Quality Mapping in Lake Beysehir, Turkey. Water, Air, & Soil Pollution. 212(1-4): 183-197.

Chebud, Y., Naja, G. M., Rivero, R. G., & Melesse, A. M. 2012. Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network. Water, Air, & Soil Pollution. 223(8): 4875-4887.

Sun, D., Qiu, Z., Li, Y., Shi, K., & Gong, S. 2014. Detection of Total Phosphorus Concentrations of Turbid Inland Waters Using a Remote Sensing Method. Water, Air, & Soil Pollution. 225(5): 1-17.

Azab, A. M. 2012. Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management in Irrigated Watersheds. Delft, the Netherland. 25-130.

Hazin, S. and Hashim, M. 2014. Separation of Different Vegetation Types in ASTER and Landsat Satellite Images Using Satelliteâ€derived Vegetation Indices. Jurnal Teknologi (Sciences & Engineering) 71(4): 109-114.

De Bie, C. J. M., Khan, M. R., Smakhtin, V. U., Venus, V., Weir, M. J. C. & Smaling, E. M. A. 2011. Analysis of Multi-Temporal SPOT NDVI Images for Small-Scale Land-Use Mapping. International Journal of Remote Sensing. 32: 6673-6693.

Bandaru, V., West, T. O., Ricciuto D. M. & César I. R.. 2013. Estimating Crop Net Primary Production Using National Inventory Data and MODIS-Derived Parameters. ISPRS Journal of Photogrammetry and Remote Sensing. 80: 61-71.

Golafshani, M. B., Shahnazari, A., Ahmadi, M. Z. & Aghajani, G. 2012. Compare the Parameters of the Water Balance In Traditional and Land Levening Paddy Fields in Qaemshahr City. Journal of Soil and Water. 1010-1017.

Huang, S., Liu, H., Dahal, D., Jin, S., Welp, L. R., Liu, J. & Liu, S. 2013. Modeling Spatially Explicit Fire Impact on Gross Primary Production in Interior Alaska Using Satellite Images Coupled With Eddy Covariance. Remote Sensing of Environment. 135: 178-188.

Jiang, Z., Huete, A. R., Didan, K. & Miura, T. 2008. Development of a Two-band Enhanced Vegetation Index Without a Blue Band. Remote Sensing of Environment. 112: 3833-3845.

Kolios, S. & Stylios, C. D. 2013. Identification of Land Cover/Land Use Changes in the Greater Area of the Preveza Peninsula in Greece Using Landsat Satellite Data. Applied Geography. 40: 150-160.

Mohamed, M. & Plante, R. 2002. Remote Sensing and Geographic Information Systems (GIS) for Developing Countries. In: Geoscience and Remote Sensing Symposium. IGARSS'02. 2002 IEEE International. 2002. IEEE. 2285-2287.

Ninomiya, Y. 2003. A Stabilized Vegetation Index and Several Mineralogic Indices Defined for ASTER VNIR And SWIR Data. In: Geoscience and Remote Sensing Symposium. 2003. IGARSS'03. Proceedings. 2003 IEEE International. 1552-1554.

O'connell, J., Connolly, J., Vermote, E. F. & Holden, N. M. 2013. Radiometric Normalization for Change Detection in Peatlands: A Modified Temporal Invariant Cluster Approach. International Journal of Remote Sensing. 34: 2905-2924.

Pournamdari, M. & Hashim, M. 2013. Detection of Chromite Bearing Mineralized Zones in Abdasht Ophiolite Complex Using ASTER And ETM+ Remote Sensing Data. Arabian Journal of Geosciences. 1-11.

Pu, R. 2012. Mapping Leaf Area Index Over a Mixed Natural Forest Area in the Flooding Season Using Ground-Based Measurements and Landsat TM Imagery. International Journal of Remote Sensing. 33: 6600-6622.

Sharma, R., Ghosh, A. & Joshi, P. 2013. Analysing Spatio-Temporal Footprints of Urbanization on Environment of Surat City Using Satellite-Derived Bio-Physical Parameters. Geocarto International. 28: 420-438.

Stevens, F. R. 2009. Bridging the Landsat Data Gap: Evaluating ASTER as an Alternative. University of Florida.

Van Den Bergh, F., Wessels, K. J., Miteff, S., Van Zyl, T. L., Gazendam, A. D. & Bachoo, A. K. 2012. HiTempo: A Platform For Time-Series Analysis of Remote-Sensing Satellite Data in a High-Performance Computing Environment. International Journal of Remote Sensing. 33: 4720-4740.

Sun, D., Qiu, Z., Li, Y., Shi, K., & Gong, S. 2014. Detection of Total Phosphorus Concentrations of Turbid Inland Waters Using a Remote Sensing Method. Water, Air, & Soil Pollution. 225(5): 1-17.

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Published

2015-06-25

Issue

Section

Science and Engineering

How to Cite

LANDSAT TM-8 DATA FOR RETRIEVING SALINITY IN AL-HUWAIZAH MARSH, SOUTH OF IRAQ. (2015). Jurnal Teknologi (Sciences & Engineering), 75(1). https://doi.org/10.11113/jt.v75.3988