Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/8280
Título: Urban sprawl analysis and modeling in Asmara, Eritreia: Application of Geospatial Tools
Autor: Tewolde, Mussie Ghebretinsae
Orientador: Pebesma, Edzer
Bañon, Filiberto Pla
Cabral, Pedro
Kohler, Thomas
Palavras-chave: Asmara
Change Detection
eCognition Definiens
Geospatial tools
Image Classification
Remote Sensing
Urban Sprawl
Data de Defesa: 7-Fev-2011
Relatório da Série N.º: Master of Science in Geospatial Technologies;TGEO0046
Resumo: Urbanization pattern of Greater Asmara Area for the last two decades (1989 to 2009) and a prediction for the coming ten years was studied. Satellite images and geospatial tools were employed to quantify and analyze the spatiotemporal urban land use changes during the study periods. The principal objective of this thesis was to utilize satellite data, with the application of geospatial and modeling tools for studying urban land use change. In order to achieve this, satellite data for three study periods (1989, 2000 and 2009) have been obtained from USGS. Object-Based Image Analysis (OBIA); and image classification with Nearest Neighbor algorithm in eCognition Developer 8 have been accomplished. In order to assess the validation of the classified LULC maps, Kappa measure of agreement has been used; results were above minimum and acceptable level. ArcGIS and IDRISI Andes have been employed for LUCC quantification; spatiotemporal analysis of the urban land use classes;to examine the land use transitions of the land classes and identify the gains and losses in relation to built up area; and to characterize impacts of the changes. Since, the major concern of the study was urban expansion, the LULC classes were reclassified in to built up and non-built up for further analysis. Urban sprawl has been measured using Shannon Entropy approach; results indicated the urban area has undergone a considerable sprawl. Finally, LCM has been used to develop a model, asses the prediction capacity of the developed model and predict future urban land use change of the GAA. Multi-layer perceptron Neural Network has been used to model the transition potential maps, results were successful to make ‘actual’ prediction with Markov Chain Analyst.Despite the GAA is center of development and its regional economic and social importance, its trend of growth remains the major factor for diminishing productive land and other valuable natural resources. The findings of the study indicated that, in the last twenty years the built up area has tripled in size and impacted the surrounding natural environment. Thus, the findings of this study might support decision making for sustainable urban development of GAA.
Descrição: Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
URI: http://hdl.handle.net/10362/8280
Aparece nas colecções:NIMS - MSc Dissertations Geospatial Technologies (Erasmus-Mundus)

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