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<p>Land use land cover (LULC) changes are a significant driver of environmental and climate change, which impact ecosystem services and human well-being. This study employs geo-information modelling and Google Earth Engine (GEE) to generate spatial-temporal changes on LULC and land surface temperature (LST) over the past three decades, focusing on the Mandi Bahauddin (MBD) district of Pakistan. The LULC maps were prepared for the years 1983, 2003, and 2023 by using supervised classification techniques, such as random forest and support vector machine (SVM). Our outcomes showed that the vegetation area decreased by 2.57%, while the built-up area increased by 4.58% from 1983 to 2023 in the MBD region. It was noted that average LST values increased by 0.95°C from 1983 (26.75°C) to 2023 (27.7°C) due to the expansion of built-up areas as well as the reduction of vegetative areas. In the present study, the regression coefficients (<i>R</i><sup>2</sup>) of 0.82, 0.79, and 0.75 were observed in 1983, 2003, and 2023, respectively, between LST and NDVI. According to our study, a positive relationship (<i>R</i><sup>2</sup>) between NDBI and LST was found at 0.77, 0.80, and 0.82, respectively, for 1983, 2003, and 2023. The findings underscore the importance of establishing and sustaining LULC planning and management procedures to mitigate and adapt to climate change, given the link between LULC and LST. Although the LULC results provide valuable insights for the judicious and optimal use of land resources, policy implications for this domain still require further development.</p>

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Analyzing temporal changes in urban land use and climate dynamics through Google Earth Engine

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Muhammad Zaib Arshad,
  • Sajjad Hussain,
  • Zafar Iqbal,
  • Saeed Ahmad Qaisrani,
  • Muhammad Mubeen,
  • Muhammad Tahir,
  • Shankar Karuppannan

摘要

Land use land cover (LULC) changes are a significant driver of environmental and climate change, which impact ecosystem services and human well-being. This study employs geo-information modelling and Google Earth Engine (GEE) to generate spatial-temporal changes on LULC and land surface temperature (LST) over the past three decades, focusing on the Mandi Bahauddin (MBD) district of Pakistan. The LULC maps were prepared for the years 1983, 2003, and 2023 by using supervised classification techniques, such as random forest and support vector machine (SVM). Our outcomes showed that the vegetation area decreased by 2.57%, while the built-up area increased by 4.58% from 1983 to 2023 in the MBD region. It was noted that average LST values increased by 0.95°C from 1983 (26.75°C) to 2023 (27.7°C) due to the expansion of built-up areas as well as the reduction of vegetative areas. In the present study, the regression coefficients (R2) of 0.82, 0.79, and 0.75 were observed in 1983, 2003, and 2023, respectively, between LST and NDVI. According to our study, a positive relationship (R2) between NDBI and LST was found at 0.77, 0.80, and 0.82, respectively, for 1983, 2003, and 2023. The findings underscore the importance of establishing and sustaining LULC planning and management procedures to mitigate and adapt to climate change, given the link between LULC and LST. Although the LULC results provide valuable insights for the judicious and optimal use of land resources, policy implications for this domain still require further development.