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Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments

Author

Listed:
  • Mohsin Ramzan

    (Institute of Geographical Information Systems, National University of Science & Technology (NUST), Islamabad 44000, Pakistan
    Department of Urban & Regional Planning, University of Buffalo, Buffalo, NY 14214-8030, USA)

  • Zulfiqar Ahmad Saqib

    (Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan
    Agricultural Remote Sensing Laboratory (ARSL), National Centre of GIS and Space Application (NCGSA), Islamabad 44000, Pakistan)

  • Ejaz Hussain

    (Institute of Geographical Information Systems, National University of Science & Technology (NUST), Islamabad 44000, Pakistan)

  • Junaid Aziz Khan

    (Institute of Geographical Information Systems, National University of Science & Technology (NUST), Islamabad 44000, Pakistan)

  • Abid Nazir

    (Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003, USA)

  • Muhammad Yousif Sardar Dasti

    (School of Geoscience and Info-Physics, Central South University, Changsha 410083, China)

  • Saqib Ali

    (Department of Computer Science, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan)

  • Nabeel Khan Niazi

    (Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan
    Faculty of Engineering and Science, Southern Cross University, Lismore, NSW 2480, Australia)

Abstract

Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan.

Suggested Citation

  • Mohsin Ramzan & Zulfiqar Ahmad Saqib & Ejaz Hussain & Junaid Aziz Khan & Abid Nazir & Muhammad Yousif Sardar Dasti & Saqib Ali & Nabeel Khan Niazi, 2022. "Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments," Land, MDPI, vol. 11(9), pages 1-19, September.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:9:p:1610-:d:919017
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    References listed on IDEAS

    as
    1. Abdullah Addas & Ran Goldblatt & Steven Rubinyi, 2020. "Utilizing Remotely Sensed Observations to Estimate the Urban Heat Island Effect at a Local Scale: Case Study of a University Campus," Land, MDPI, vol. 9(6), pages 1-26, June.
    2. Ran Goldblatt & Abdullah Addas & Daynan Crull & Ahmad Maghrabi & Gabriel Gene Levin & Steven Rubinyi, 2021. "Remotely Sensed Derived Land Surface Temperature (LST) as a Proxy for Air Temperature and Thermal Comfort at a Small Geographical Scale," Land, MDPI, vol. 10(4), pages 1-24, April.
    3. Abid Nazir & Saleem Ullah & Zulfiqar Ahmad Saqib & Azhar Abbas & Asad Ali & Muhammad Shahid Iqbal & Khalid Hussain & Muhammad Shakir & Munawar Shah & Muhammad Usman Butt, 2021. "Estimation and Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear Regression Model on Sentinel-II Satellite Data," Agriculture, MDPI, vol. 11(10), pages 1-14, October.
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