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Open AccessArticle

Patterns of Historical and Future Urban Expansion in Nepal

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College of Applied Sciences (CAS)-Nepal, Tribhuvan University, Kathmandu 44613, Nepal
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The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), No. 20 Datun Road, Chaoyang District, Beijing 100101, China
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Centre for Tropical Environmental and Sustainability Science, College of Science and Engineering, James Cook University, Cairns, QLD 4870, Australia
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Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj 31587–77871, Iran
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Centre for Urban Research, RMIT University 124 La Trobe St, Melbourne, VIC 3004, Australia
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Department of Sustainable Energy Management, Faculty of Environmental Management, Prince of Songkla University, Hat Yai 90112, Thailand
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Center for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD 4350, Australia
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Global Institute for Interdisciplinary Studies, Kathmandu 44613, Nepal
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 628; https://doi.org/10.3390/rs12040628
Received: 6 December 2019 / Revised: 29 January 2020 / Accepted: 29 January 2020 / Published: 13 February 2020
Globally, urbanization is increasing at an unprecedented rate at the cost of agricultural and forested lands in peri-urban areas fringing larger cities. Such land-cover change generally entails negative implications for societal and environmental sustainability, particularly in South Asia, where high demographic growth and poor land-use planning combine. Analyzing historical land-use change and predicting the future trends concerning urban expansion may support more effective land-use planning and sustainable outcomes. For Nepal’s Tarai region—a populous area experiencing land-use change due to urbanization and other factors—we draw on Landsat satellite imagery to analyze historical land-use change focusing on urban expansion during 1989–2016 and predict urban expansion by 2026 and 2036 using artificial neural network (ANN) and Markov chain (MC) spatial models based on historical trends. Urban cover quadrupled since 1989, expanding by 256 km2 (460%), largely as small scattered settlements. This expansion was almost entirely at the expense of agricultural conversion (249 km2). After 2016, urban expansion is predicted to increase linearly by a further 199 km2 by 2026 and by another 165 km2 by 2036, almost all at the expense of agricultural cover. Such unplanned loss of prime agricultural lands in Nepal’s fertile Tarai region is of serious concern for food-insecure countries like Nepal.
Keywords: urban expansion; cropland loss; machine learning models; Markov chain urban expansion; cropland loss; machine learning models; Markov chain
MDPI and ACS Style

Rimal, B.; Sloan, S.; Keshtkar, H.; Sharma, R.; Rijal, S.; Shrestha, U.B. Patterns of Historical and Future Urban Expansion in Nepal. Remote Sens. 2020, 12, 628.

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