Creation of a coastal zone information system for Qatar using remote sensing and GIS

Abstract

The aim of this thesis is to establish an ESI information system for the northern Qatar coastal zone using remote sensing and GIS technologies. Environmental Sensitivity Index (ESI) mapping is a classification and ranking of the relative sensitivities of various geologic, geomorphic and biologic coastal environment types on a scale of 1 to 10 in terms of indicating the persistence of oil and potential for biological habitat damage. ESI mapping is usually associated with oil spills response and contingency planning, because it provides a tool to assist spill-response coordinators and government agencies in planning or operating strategies for protection and spill management.

Remote sensing has great potential for application to the complex environmental problems of the coastal zone. Its potential is derived from the spatial, spectral and temporal digital data provided by the system, which can be used for physical and ecological studies of the coastal zone and marine environments. Remotely-sensed data can be combined with other spatial data in a GIS system for effective management of the coastal zone, though this has not often been achieved at operational level for various technical and logistical reasons which are discussed in the thesis.

Applying remote sensing and GIS to enhance the traditional approach of ESI mapping involves the processing of high spatial resolution airborne MEIS (Multi-detector Electro-optical Imaging Scanner) data (2 m spatial resolution) for the whole coastal zone of Qatar. This provides the missing information on the coastal zone vegetated habitats which are essential in the ESI mapping. Manipulating the compiled ESI information in the GIS system to overcome the difficulties of assembling the necessary materials of ESI in hardcopy form, handling it through manual techniques, and the difficulty of applying consistent procedures within such an approach are further challenges that have been tackled.

Several types of image processing techniques were applied to the MEIS data in order to find the best analysis to handle the large volume of digital data and at the same time reveal the information needed in the coastal zone for the ESI mapping. These techniques range from conventional classification and resampling to newer techniques of image compression, and automated spectral pixel unmixing analysis. Image compression and automated spectral pixel unmixing are found to be well studied to handle the large volume of digital data and reveal the information needed in the coastal zone for the ESI mapping. Better results would be achieved if the original MEIS flight lines data had not been mosaicked to form rectangular blocks of MEIS data. Detailed mapping of the very sensitive coastal resources in northern Qatar is achieved using visual interpretation of JPEG compressed digital images and a spectral library of surface materials is built for use in spectral pixel unmixing.

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