Cover Image

Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS

The rapid development of cities in developing countries results in deteriorating of agricultural lands. The majority of these agricultural lands are converted to urban areas, which affects the ecosystems. In this research, an integrated model of Markov chain and cellular automata models was applied...

Full description

Saved in:
Bibliographic Details
Main Authors: Al-sharif, Abubakr A. A., Pradhan, Biswajeet
Format: Article
Published: Springer Verlag 2014
Online Access:http://psasir.upm.edu.my/id/eprint/34594/
http://link.springer.com/article/10.1007%2Fs12517-013-1119-7
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.34594
record_format eprints
spelling my.upm.eprints.345942015-12-16T03:26:43Z http://psasir.upm.edu.my/id/eprint/34594/ Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS Al-sharif, Abubakr A. A. Pradhan, Biswajeet The rapid development of cities in developing countries results in deteriorating of agricultural lands. The majority of these agricultural lands are converted to urban areas, which affects the ecosystems. In this research, an integrated model of Markov chain and cellular automata models was applied to simulate urban land use changes and to predict their spatial patterns in Tripoli metropolitan area, Libya. It is worth mentioning that there is not much research has been done about land use/cover change in Libyan cities. In this study, the performance of integrated CA–Markov model was assessed. Firstly, the Markov chain model was used to simulate and predict the land use change quantitatively; then, the CA model was applied to simulate the dynamic spatial patterns of changes explicitly. The urban land use change from 1984 to 2010 was modelled using the CA–Markov model for calibration to compute optimal transition rules and to predict future land use change. In validation process, the model was validated using Kappa index statistics which resulted in overall accuracy more than 85 %. Finally, based on transition rules and transition area matrix produced from calibration process, the future land use changes of 2020 and 2025 were predicted and mapped. The findings of this research showed reasonably good performance of employed model. The model results demonstrate that the study area is growing very rapidly especially in the recent decade. Furthermore, this rapid urban expansion results in remarkable continuous decrease of agriculture lands. Springer Verlag 2014 Article PeerReviewed Al-sharif, Abubakr A. A. and Pradhan, Biswajeet (2014) Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7 (10). pp. 4291-4301. ISSN 1866-7511; ESSN: 1866-7538 http://link.springer.com/article/10.1007%2Fs12517-013-1119-7 10.1007/s12517-013-1119-7
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The rapid development of cities in developing countries results in deteriorating of agricultural lands. The majority of these agricultural lands are converted to urban areas, which affects the ecosystems. In this research, an integrated model of Markov chain and cellular automata models was applied to simulate urban land use changes and to predict their spatial patterns in Tripoli metropolitan area, Libya. It is worth mentioning that there is not much research has been done about land use/cover change in Libyan cities. In this study, the performance of integrated CA–Markov model was assessed. Firstly, the Markov chain model was used to simulate and predict the land use change quantitatively; then, the CA model was applied to simulate the dynamic spatial patterns of changes explicitly. The urban land use change from 1984 to 2010 was modelled using the CA–Markov model for calibration to compute optimal transition rules and to predict future land use change. In validation process, the model was validated using Kappa index statistics which resulted in overall accuracy more than 85 %. Finally, based on transition rules and transition area matrix produced from calibration process, the future land use changes of 2020 and 2025 were predicted and mapped. The findings of this research showed reasonably good performance of employed model. The model results demonstrate that the study area is growing very rapidly especially in the recent decade. Furthermore, this rapid urban expansion results in remarkable continuous decrease of agriculture lands.
format Article
author Al-sharif, Abubakr A. A.
Pradhan, Biswajeet
spellingShingle Al-sharif, Abubakr A. A.
Pradhan, Biswajeet
Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS
author_facet Al-sharif, Abubakr A. A.
Pradhan, Biswajeet
author_sort Al-sharif, Abubakr A. A.
title Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS
title_short Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS
title_full Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS
title_fullStr Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS
title_full_unstemmed Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS
title_sort monitoring and predicting land use change in tripoli metropolitan city using an integrated markov chain and cellular automata models in gis
publisher Springer Verlag
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/34594/
http://link.springer.com/article/10.1007%2Fs12517-013-1119-7
_version_ 1643831201006354432
score 13.211869

Similar Items