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A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects

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  • Srishti Gaur

    (Agricultural and Food Engineering, Indian Institute of Technology, Kharagpur 721302, India)

  • Rajendra Singh

    (Agricultural and Food Engineering, Indian Institute of Technology, Kharagpur 721302, India)

Abstract

Land use land cover (LULC) modeling is considered as the best tool to comprehend and unravel the dynamics of future urban expansion. The present paper provides a comprehensive review of existing LULC modeling techniques and novel approaches used by the research community. Moreover, the review also compares each technique’s applications, utility, drawbacks, and broader differences. The rationale behind such a comparison is to highlight the strengths/weakness of individual techniques. The review further highlights the utility of the hybridization of different techniques (e.g., machine learning model combined with statistical models) to LULC modeling to complement their strengths. Although significant progress has been made in LULC modeling, the review highlights the need to incorporate the policy framework into LULC modeling for better urban planning and management. The present review will help researchers and policymakers to achieve better land management practices and ultimately assist in achieving Sustainable Development Goal-15 (SDG-15) (i.e., life on land).

Suggested Citation

  • Srishti Gaur & Rajendra Singh, 2023. "A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects," Sustainability, MDPI, vol. 15(2), pages 1-12, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:903-:d:1024596
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    References listed on IDEAS

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    1. Srishti Gaur & Arnab Bandyopadhyay & Rajendra Singh, 2021. "From Changing Environment to Changing Extremes: Exploring the Future Streamflow and Associated Uncertainties Through Integrated Modelling System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1889-1911, April.
    2. Guan, DongJie & Li, HaiFeng & Inohae, Takuro & Su, Weici & Nagaie, Tadashi & Hokao, Kazunori, 2011. "Modeling urban land use change by the integration of cellular automaton and Markov model," Ecological Modelling, Elsevier, vol. 222(20), pages 3761-3772.
    3. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    4. Yamashita, Ryohei & Hoshino, Satoshi, 2018. "Development of an agent-based model for estimation of agricultural land preservation in rural Japan," Agricultural Systems, Elsevier, vol. 164(C), pages 264-276.
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    1. Kai Zhu & Yufeng Cheng & Weiye Zang & Quan Zhou & Youssef El Archi & Hossein Mousazadeh & Moaaz Kabil & Katalin Csobán & Lóránt Dénes Dávid, 2023. "Multiscenario Simulation of Land-Use Change in Hubei Province, China Based on the Markov-FLUS Model," Land, MDPI, vol. 12(4), pages 1-27, March.
    2. Onggarbek Alipbeki & Gauhar Mussaif & Chaimgul Alipbekova & Aizada Kapassova & Pavel Grossul & Meirzhan Aliyev & Nursultan Mineyev, 2023. "Untangling the Integral Impact of Land Use Change, Economic, Ecological and Social Factors on the Development of Burabay District (Kazakhstan) during the Period 1999–2021," Sustainability, MDPI, vol. 15(9), pages 1-36, May.

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