Investigating the Relationship between Land Use/Land Cover Change and Land Surface Temperature Using Google Earth Engine; Case Study: Melbourne, Australia

Yashar Jamei (), Mehdi Seyedmahmoudian, Elmira Jamei, Ben Horan, Saad Mekhilef and Alex Stojcevski
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Yashar Jamei: School of Engineering, Deakin University, Geelong, VIC 3216, Australia
Mehdi Seyedmahmoudian: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
Elmira Jamei: College of Engineering and Science, Victoria University, Melbourne, VIC 3011, Australia
Ben Horan: School of Engineering, Deakin University, Geelong, VIC 3216, Australia
Saad Mekhilef: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
Alex Stojcevski: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

Sustainability, 2022, vol. 14, issue 22, 1-34

Abstract: The rapid alteration to land cover, combined with climate change, results in the variation of the land surface temperature (LST). This LST variation is mainly affected by the spatiotemporal changes of land cover classes, their geospatial characteristics, and spectral indices. Melbourne has been the subject of previous studies of land cover change but often over short time periods without considering the trade-offs between land use/land cover (LULC) and mean daytimes summer season LST over a more extended period. To fill this gap, this research aims to investigate the role of LULC change on mean annual daytime LST in the hot summers of 2001 and 2018 in Melbourne. To achieve the study’s aim, LULC and LST maps were generated based on the cost-effective cloud-based geospatial analysis platform Google Earth Engine (GEE). Furthermore, the geospatial and geo-statistical relationship between LULC, LST, and spectral indices of LULC, including the Normalised Difference Built-up Index (NDBI) and the Normalised Difference Vegetation Index (NDVI), were identified. The findings showed that the mean daytime LST increased by 5.1 °C from 2001 to 2018. The minimum and maximum LST values were recorded for the vegetation and the built-up area classes for 2001 and 2018. Additionally, the mean daytime LST for vegetation and the built-up area classes increased by 5.5 °C and 5.9 °C from 2001 to 2018, respectively. Furthermore, both elevation and NDVI were revealed as the most influencing factors in the LULC classification process. Considering the R 2 values between LULC and LST and their NDVI values in 2018, grass (0.48), forest (0.27), and shrubs (0.21) had the highest values. In addition, urban areas (0.64), bare land (0.62), and cropland (0.61) LULC types showed the highest R 2 values between LST regarding their NDBI values. This study highlights why urban planners and policymakers must understand the impacts of LULC change on LST. Appropriate policy measures can be proposed based on the findings to control Melbourne’s future development.

Keywords: land use/land cover; land surface temperature; Google Earth Engine; Melbourne (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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