land Article Quantifying the Spatiotemporal Pattern of Urban Expansion and Hazard and Risk Area Identification in the Kaski District of Nepal Bhagawat Rimal 1 ID , Lifu Zhang 1,* ID , Hamidreza Keshtkar 2, Xuejian Sun 1 and Sushila Rijal 3,4 1 The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
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[email protected] (X.S.) 2 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran;
[email protected] 3 Faculty of Humanities and Social Sciences, Mahendra Ratna Multiple Campus, Ilam 57300, Nepal 4 Everest Geo-Science Information Service Center, Kathmandu 44600, Nepal;
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[email protected]; Tel.: +86-10-6483-9450 Received: 11 January 2018; Accepted: 13 March 2018; Published: 16 March 2018 Abstract: The present study utilized time-series Landsat images to explore the spatiotemporal dynamics of urbanization and land use/land-cover (LULC) change in the Kaski District of Nepal from 1988 to 2016. For the specific overtime analysis of change, the LULC transition was clustered into six time periods: 1988–1996, 1996–2000, 2000–2004, 2004–2008, 2008–2013, and 2013–2016. The classification was carried out using a support vector machine (SVM) algorithm and 11 LULC categories were identified. The classified images were further used to predict LULC change scenarios for 2025 and 2035 using the hybrid cellular automata Markov chain (CA-Markov) model. Major hazard risk areas were identified using available databases, satellite images, literature surveys, and field observations.