Abdelgadir Abuelgasim

Work place: Department of Geography and Urban Planning, College of Humanities and Social Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi 15551, UAE



Research Interests: Environmental Sciences


Dr. Abuelgasim received both his masters and PhD degrees from Boston University in 1996. Following he worked for one year as an assistant professor of remote sensing and surveying at Sultan Qabous University in Oman with the department of civil engineering. He later joined the NASA Goddard Space Flight Center in Maryland, USA in 1998 as a visiting scientist where he worked in a research program exploiting multi-angle remote sensing observations. In 2000 he joined the Canada Centre for Remote Sensing and the Canadian Space Agency as a research scientist. Dr Abuelgasim also served as the international geospatial data and remote sensing consultant during 2012 for the Nile Basin Initiative. Dr Abuelgasim is currently an assistant professor at the United Arab Emirates University and his research and teaching interests are focused on the analysis of geospatial data for information products generation, physical and human geography, hydrology, and on the effects of human induced environmental and ecological changes.

Author Articles
Mapping Sabkha Land Surfaces in the United Arab Emirates (UAE) using Landsat 8 Data, Principal Component Analysis and Soil Salinity Information

By Abdelgadir Abuelgasim Rubab Ammad

DOI: https://doi.org/10.5815/ijem.2017.04.01, Pub. Date: 8 Jul. 2017

Sabkha is an Arabic word for a salt-flat area found mainly along arid area coastlines and inlands within sand dunes areas. The sabkha that form within the sand are relatively flat and very saline areas of sand or silt that forms just above the water-table where the sand is cemented together by evaporite salts from seasonal ponds. Such shallow water is normally highly saline. Here the crust is rich in gypsum and halite veins where the underline thin layer is made of sand and silt. Such sabkha have an average thickness of a meter or slightly less. On the other hand, marine sabkha represent transitional environments between the land and the sea. The UAE is home to some of the largest concentrations of sabkha both coastal and inland. The coastal areas of Abu Dhabi include several small shoals, islands, protected lagoons, channels and deltas, an inner zone of intertidal flats with algal mats and broad areas of supratidal sabkha salt flats.
Identifying sabkha habitats from remotely sensed data is a challenging process. Traditional classification techniques of multispectral data alone, usually fail to properly identify sabkha pixels or provide lower rates of mapping accuracy for sabkha habitats. The primary objective of this research is to develop a much more accurate methodology for properly mapping and identifying sabkha areas from remotely sensed data. Properly mapping sabkha habitats from remotely sensed data is the first steps towards studying the ecological changes within such habitats using earth observation techniques. Furthermore, sabkha habitats can in certain situations be a geotechnical hazard due to its highly salinity and with adverse effects on concrete, asphalt, steel and other structures, in addition to their sporadic heaves and collapses. As the UAE continue to build major infrastructure and development projects identifying the location of such habitats is vitally important.
In this research a new technique that combines the multispectral information of Landsat 8, principal component analysis and spectral soil salinity detection is developed. The study area is located in the western part of the UAE along the border with the Kingdom of Saudi Arabia, an area known to include large tracks of inland and coastal sabkha. Landsat 8 data from path 161 and row 43 was acquired for the study. A multi-source classification approach was followed that utilizes the multispectral data of Landsat 8 along with components from the principal components analysis and the spectral salinity index maps. The preliminary results confirmed by field observations show that the combined data improved the classification accuracy to almost 90% in comparison to multispectral data alone of 78%.

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