Ahmed, B;
Ahmed, R;
(2012)
Modeling urban land cover growth dynamics using multi‑temporal satellite images: a case study of Dhaka, Bangladesh.
ISPRS International Journal of Geo-Information
, 1
(1)
3 - 31.
10.3390/ijgi1010003.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Modeling urban land cover growth dynamics using multi‑temporal satellite images: a case study of Dhaka, Bangladesh |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/ijgi1010003 |
| Publisher version: | http://dx.doi.org/10.3390/ijgi1010003 |
| Language: | English |
| Additional information: | © 2012 by the authors; licensee MD PI, Basel, Switzerland. This arti cle is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons .org/licenses/by/3.0/). |
| Keywords: | Remote sensing, Land cover, Markov chain, Cellular automata; Multi layer perceptron neural network, Change detection, Supervised classification, GIS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1418952 |
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