Modeling Urban Land Cover Growth Dynamics Using Multi‑Temporal Satellite Images: A Case Study of Dhaka, Bangladesh

ISPRS International Journal of Geo-Information

Bayes Ahmed¹ 

[1] Clear Air and Sustainable Environment (CASE) Project, Dhaka South City Corporation (DSCC), Nagar Bhaban, Fulbaria, Dhaka-1000, Bangladesh

关键词: remote sensing;    land cover;    Markov chain;    cellular automata;    multi layer perceptron neural network;    change detection;    supervised classification;    GIS;   

DOI  :  10.3390/ijgi1010003

来源: mdpi

【 摘 要 】

The primary objective of this research is to predict and analyze the future urban growth of Dhaka City 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. At the beginning, a fisher supervised classification method has been applied to prepare the base maps with five land cover classes. In the next stage, three different models have been implemented to simulate the land cover map of Dhaka city of 2009. These have been 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 by implementing a method to compare land cover categories in three maps: a reference map of time 1, a reference map of time 2 and a simulation map of time 2. This is how the “Multi Layer Perceptron Markov (MLP_Markov)” Model has been qualified as the most appropriate model for this research. Later, using the MLP_Markov model, the land cover map of 2019 has been predicted. The MLP_Markov model extrapolates that built-up area increases from 46% to 58% of the total study area during 2009–2019.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.