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Accueil du site → Doctorat → Allemagne → 2013 → Assessment of Impacts of Changes in Land Use Patterns on Land Degradation/Desertification in the Semi- arid Zone of White Nile State, Sudan, by Means of Remote Sensing and GIS

Technische Universität Dresden (2013)

Assessment of Impacts of Changes in Land Use Patterns on Land Degradation/Desertification in the Semi- arid Zone of White Nile State, Sudan, by Means of Remote Sensing and GIS

Abdelnasir Ibrahim Ali Hano

Titre : Assessment of Impacts of Changes in Land Use Patterns on Land Degradation/Desertification in the Semi- arid Zone of White Nile State, Sudan, by Means of Remote Sensing and GIS

Auteur : Abdelnasir Ibrahim Ali Hano

Université de soutenance : Technische Universität Dresden

Grade : Doctor of Natural Science (Dr.rer.nat) 2013

Résumé
In Sudan, land degradation/desertification (LDD) has devastated large areas and consequently, it includes social, economic, and environmental aspects. LDD results from various factors, including climatic variation and human activities. Probably the LU practices and their changes have contributed to an increase of LDD in that area. Remote sensing technology has become unique and developed tool for providing temporal and spatial information for the LDD research and other environmental aspects. Determination of LDD and its relationship to land use pattern change (LUC) at spatiotemporal scale is rare, critical issue, and is one of the recommended research in semi-arid regions of Sudan. The study was carried out to derive accurate and improved spatiotemporal information : to assess the status of land LDD of vegetation and soil, to assess and model influences of the LUC on LDD, and moreover to analyse the synergistic factors that have caused the land use change and/or LDD in semi- arid zone of Elgeteina Locality in While Nile State, Sudan during the last 36 years, using appropriate remote sensing (RS) and GIS technology. The study used four-cloud free images of different sensors (MSS 1973, TM 1986, ASTER 2009 and TM 2010). The imageries were Geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Then subsets of the study area were taken, ranging from 1600-2000 Km2. The study applied the new approach of integration between vegetation and soil indices and in situ data to assess the LDD. Comparison between pixel based image analysis (PBIA) and latterly approach of object based image analysis (OBIA) was done by selecting the best one for mapping LUC and LDD accurately. The change detection - matrix was applied to estimate the spatiotemporal of changes in land use and land degradation. Moreover, correlation and model approach was employed for fusing the climatic, socioeconomic and remote sensing data to determine the relationships between the different factors and to analyse the reasons for the LUC and LDD as well as for modelling LU effects on LDD. The study revealed that : The changes in land use patterns (RA, FWL and FML) took place in 1973 – 86 – 2009, and affecting thoroughly different patterns of the vegetation cover. Likewise the LUC affected soil degradation which led to the movement of sand dunes in 1973 – 2009. The agricultural activity is the dominant and has more effect on LDD particularly on the vegetation cover degradation. The population growth and the socioeconomic status of local people are the main indirect human inducing factors responsible for LUC and/or LDD. SARVI is slightly more efficient than NDVI, SAVI, ND4-25 and ND42-57, for detecting the vegetation status in semi-arid area, therefore the study selected it for the assessment. GSI proved highly efficient in determining the different types of soil degradation, and in producing the map of top soil grain size, which assisted in the assessment of land degradation and desertification. OBIA-fuzzy logic classification performed better than the PBIA- hybrid classification for assessing LU patterns impact on LDD. The study recommends to : replication of this study by using different imagery with high resolutions and sophisticated software, such as eCognition and Feature Analyst (FA) for increasing the validity and accuracy of the assessment and modelling of LU patterns and LDD status in dry land is important in the Sudan.

Mots Clés : land degradation, desertification, Remote sensing, Sudan ; Land Degradation, Desertifikation, Fernerkundungstechnologien, Sudan

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Page publiée le 13 novembre 2015, mise à jour le 2 décembre 2018