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Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model

The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Institutes of Ecology, Friedrich Schiller University, Jena 07743, Germany
College of Natural Resources, University of Tehran, Tehran 1417466191, Iran
Institute of Remote Sensing and GIS, School of Earth and Space Science, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(9), 288;
Received: 7 August 2017 / Revised: 1 September 2017 / Accepted: 6 September 2017 / Published: 8 September 2017
Spatial–temporal analysis of land-use/land-cover (LULC) change as well as the monitoring and modeling of urban expansion are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans and policies and by power, politics and poor governance in many less-developed countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. The current study evaluated the LULC changes and urban expansion of Jhapa district of Nepal. The spatial–temporal dynamics of LULC were identified using six time-series atmospherically-corrected surface reflectance Landsat images from 1989 to 2016. A hybrid cellular automata Markov chain (CA–Markov) model was used to simulate future urbanization by 2026 and 2036. The analysis shows that the urban area has increased markedly and is expected to continue to grow rapidly in the future, whereas the area for agriculture has decreased. Meanwhile, forest and shrub areas have remained almost constant. Seasonal rainfall and flooding routinely cause predictable transformation of sand, water bodies and cultivated land from one type to another. The results suggest that the use of Landsat time-series archive images and the CA–Markov model are the best options for long-term spatiotemporal analysis and achieving an acceptable level of prediction accuracy. Furthermore, understanding the relationship between the spatiotemporal dynamics of urbanization and LULC change and simulating future landscape change is essential, as they are closely interlinked. These scientific findings of past, present and future land-cover scenarios of the study area will assist planners/decision-makers to formulate sustainable urban development and environmental protection plans and will remain a scientific asset for future generations. View Full-Text
Keywords: land-use/land-cover change; urbanization; CA–Markov; Nepal; remote sensing land-use/land-cover change; urbanization; CA–Markov; Nepal; remote sensing
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Rimal, B.; Zhang, L.; Keshtkar, H.; Wang, N.; Lin, Y. Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model. ISPRS Int. J. Geo-Inf. 2017, 6, 288.

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