Prediction of Urban Growth and Sustainability Challenges Based on LULC Change: Case Study of Two Himalayan Metropolitan Cities

Bhagawat Rimal, Sushila Rijal and Abhishek Tiwary ()
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Bhagawat Rimal: College of Applied Sciences-Nepal, Tribhuvan University, Kathmandu 44613, Nepal
Sushila Rijal: Centre d’Etudes Sud-Asiatiques et Himalayennes, Aubervilliers, 93300 Paris, France
Abhishek Tiwary: Institute of Sustainable Futures, De Montfort University, Leicester LE1 9BH, UK

Land, 2025, vol. 14, issue 8, 1-26

Abstract: Urbanization, characterized by population growth and socioeconomic development, is a major driving factor of land use land cover (LULC) change. A spatio-temporal understanding of land cover change is crucial, as it provides essential insights into the pattern of urban development. This study conducted a longitudinal analysis of LULC change in order to evaluate the tradeoffs of urban growth and sustainability challenges in the Himalayan region. Landsat time-series satellite imagery from 1988 to 2024 were analyzed for two major cities in Nepal—Kathmandu metropolitan city (KMC) and Pokhara metropolitan city (PMC). The LULC classification was conducted using a machine learning support vector machine (SVM) approach. For this study period, our analysis showed that KMC and PMC witnessed urban growth of over 400% and 250%, respectively. In the next step, LULC change and urban expansion patterns were predicted based on the urban development indicator using the Cellular Automata Markov chain (CA-Markov) model for the years 2040 and 2056. Based on the CA-Markov chain analysis, the projected expansion areas of the urban area for the two future years are 282.39 km 2 and 337.37 km 2 for Kathmandu, and 93.17 km 2 and 114.15 km 2 for PMC, respectively. The model was verified using several Kappa variables (K-location, K-standard, and K-no). Based on the LULC trends, the majority of urban expansion in both the study areas has occurred at the expense of prime farmlands, which raises grave concern over the sustainability of the food supply to feed an ever-increasing urban population. This haphazard urban sprawl poses a significant challenge for future planning and highlights the urgent need for effective strategies to ensure sustainable urban growth, especially in restoring local food supply to alleviate over-reliance on long-distance transport of agro-produce in high-altitude mountain regions. The alternative planning of sustainable urban growth could involve adequate consideration for urban farming and community gardening as an integral part of the urban fabric, both at the household and city infrastructure levels.

Keywords: cultivated land loss; cellular automata; Himalayan region; machine learning model; urban growth (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:8:p:1675-:d:1727912

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