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University of Dundee (2006)

Monitoring desertification in south west Tripoli using multi-temporal remotely sensing data and GIS

Oune, Omar

Titre : Monitoring desertification in south west Tripoli using multi-temporal remotely sensing data and GIS

Auteur : Oune, Omar

Université de soutenance : University of Dundee

Grade : Doctor of Philosophy (Ph.D.) 2006

Remotely sensed data has potential value for vegetation change detection and mapping in arid/semi-arid environments, which can be used and analysed to extract information relevant to the understanding of useful information for the study of desert environments and desertification monitoring, assessment and mapping. In Libya, the vast agricultural development which occurred during the last decades was accompanied by desertification of different areas. Desertification varied in type and degree according to the geographical site, irregularity of rainfall and the prevalence of strong wind which significantly affects the stability of the fragile ecosystem. The potential of this study was to offer answers to the understanding of desertification indicators and has identified criteria for desertification assessment and the creation of land degradation maps using remote sensing data and a geographic information system (GIS). The indicators which mainly impact the study area are wind erosion, vegetation degradation, salinization, and deterioration of water resources etc. Landsat TM imagery has been used as a source of data to monitor land cover and its change over large areas.In this study, multi-temporal Landsat TM imagery has been used in order to map land cover and their changes during five-year intervals from 1988 to 2000. This was achieved by using a soil adjusted vegetation index formula to detect vegetation Thealgorithm classification technique has been used to map vegetation cover, Eolian Mapping (EM) vegetation of various densities, by used the Soil Adjusted Vegetation Index (SA VI) images : TM 1988, TM 1992, TM 1996 and TM 2000. The results of this technique show areas that have vulnerability to wind erosion susceptibility. and change detection algorithm has been used to calculate the vegetation changes in the period from 1988 to 2000. This is therefore one land degradation factor that can be created from remotely sensed data. The analysis clearly demonstrates a net decrease in vegetation cover. This situation exemplifies the deterioration of the natural vegetation cover. The information derived from remotely sensed data has been integrated in a GIS to identify relevant factors for developing a spatial model for desertification assessment and mapping. A Geographic Information System was used to combine and interpret a range of parameters (land cover, soil type, topography, climate, etc.).This study presents an efficient methodology to delineate the land degradation factors in study area, in a GIS environment. In this study have used one of the multi-criteria decision-making techniques, Analytical Hierarchical Process (AHP) which provides a systematic approach for assessing and integrating the impact of various factors, involving several levels. The methodology has been present for computing a composite index of land degradation factors derived from topographical, land cover, soil type and climate data. All data are finally integrated in a GIS environment to prepare a final desertification map. This land degradation factors computed from AHP method not only considers susceptibility of each area to emphasize the vulnerability of land to erosion but also takes into account the factors that are related to desertification.


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