Journal
REMOTE SENSING
Volume 12, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/rs12040628
Keywords
urban expansion; cropland loss; machine learning models; Markov chain
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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 km(2) (460%), largely as small scattered settlements. This expansion was almost entirely at the expense of agricultural conversion (249 km(2)). After 2016, urban expansion is predicted to increase linearly by a further 199 km(2) by 2026 and by another 165 km(2) 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.
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