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Title: Urban land cover change detection analysis and modeling spatio-temporal Growth dynamics using Remote Sensing and GIS Techniques: A case study of Dhaka, Bangladesh
Author: Ahmed, Bayes
Advisor: Carmona, Pedro Latorre
Caetano, Mário
Pebesma, Edzer
Patel, Nilanchal
Keywords: Remote Sensing
Land Cover
Markov Chain
Cellular Automata
Multi Layer Perceptron Neural Network
Change Detection
Supervised Classification
Defense Date: 7-Mar-2011
Series/Report no.: Master of Science in Geospatial Technologies;TGEO0061
Abstract: Dhaka, the capital of Bangladesh, has undergone radical changes in its physical form, not only in its vast territorial expansion, but also through internal physical transformations over the last decades. In the process of urbanization, the physical characteristic of Dhaka is gradually changing as open spaces have been transformed into building areas, low land and water bodies into reclaimed builtup lands etc. This new urban fabric should be analyzed to understand the changes that have led to its creation. The primary objective of this research is to predict and analyze the future urban growth of Dhaka City. Another objective is to quantify and investigate the characteristics of urban land cover changes (1989-2009) using the Landsat satellite images of 1989, 1999 and 2009. Dhaka City Corporation (DCC) and its surrounding impact areas have been selected as the study area. A fisher supervised classification method has been applied to prepare the base maps with five land cover classes. To observe the change detection, different spatial metrics have been used for quantitative analysis. Moreover, some postclassification change detection techniques have also been implemented. Then it is found that the ‘builtup area’ land cover type is increasing in high rate over the years. The major contributors to this change are ‘fallow land’ and ‘water body’ land cover types. In the next stage, three different models have been implemented to simulate the land cover map of Dhaka city of 2009. These are named as ‘Stochastic Markov (St_Markov)’ Model, ‘Cellular Automata Markov (CA_Markov)’ Model and ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model. Then the best-fitted model has been selected based on various Kappa statistics values and also by implementing other model validation techniques. This is how the ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model has been qualified as the most suitable model for this research. Later, using the MLP_Markov model, the land cover map of 2019 has been predicted. The MLP_Markov model shows that 58% of the total study area will be converted into builtup area cover type in 2019. The interpretation of depicting the future scenario in quantitative accounts, as demonstrated in this research, will be of great value to the urban planners and decision makers, for the future planning of modern Dhaka City.
Description: Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
Appears in Collections:NIMS - MSc Dissertations Geospatial Technologies (Erasmus-Mundus)

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