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

Mohsin Ramzan, Zulfiqar Ahmad Saqib (), Ejaz Hussain, Junaid Aziz Khan, Abid Nazir, Muhammad Yousif Sardar Dasti, Saqib Ali and Nabeel Khan Niazi ()
Additional contact information
Mohsin Ramzan: Institute of Geographical Information Systems, National University of Science & Technology (NUST), Islamabad 44000, Pakistan
Zulfiqar Ahmad Saqib: Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad 38040, 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

Land, 2022, vol. 11, issue 9, 1-19

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.

Keywords: land management; LST; remote sensing; climate change; surface temperature; LULC (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/11/9/1610/pdf (application/pdf)
https://www.mdpi.com/2073-445X/11/9/1610/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:9:p:1610-:d:919017

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().