Investigating the Relationship between Land Use/Land Cover Change and Land Surface Temperature Using Google Earth Engine; Case Study: Melbourne, Australia
Open Access
- 10 November 2022
- journal article
- research article
- Published by MDPI AG in Sustainability
- Vol. 14 (22) , 14868
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 R2 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 R2 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
This publication has 91 references indexed in Scilit:
- Landscape pattern and ecosystem service value changes: Implications for environmental sustainability planning for the rapidly urbanizing summer capital of the PhilippinesLandscape and Urban Planning, 2013
- Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effectsRemote Sensing of Environment, 2012
- Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong KongRemote Sensing, 2011
- Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, IranRemote Sensing of Environment, 2009
- Thermal remote sensing of urban climatesRemote Sensing of Environment, 2003
- Use of normalized difference built-up index in automatically mapping urban areas from TM imageryInternational Journal of Remote Sensing, 2003
- A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture statusRemote Sensing of Environment, 2002
- The surface temperature-vegetation index space for land cover and land-cover change analysisInternational Journal of Remote Sensing, 1996
- A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation coverRemote Sensing Reviews, 1994
- The reflectance at the 950–970 nm region as an indicator of plant water statusInternational Journal of Remote Sensing, 1993