Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

• Main Authors: Rees, Gareth, Hebryn-Baidy, Liliia, Belenok, Vadym
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2024
• Subjects: land use/land cover; land surface temperature; linear trend; air temperature; urban heat island; MODIS; Landsat; surface urban heat island; Scott Polar Research Institute
• Online Access: https://www.repository.cam.ac.uk/handle/1810/369407

Peer reviewed: True Acknowledgements: The author Lillia Hebryn-Baidy expresses her gratitude to the British Academy and the Council for At-Risk Academics for providing support to this research study through the Researchers at Risk Research Support Grants. Sincere gratitude goes to the Department of Geography at the University of Cambridge and the Scott Polar Research Institute for their support throughout the research process. The author Vadym Belenok expresses his gratitude to the Ukrainian-Polish project “Urban greenery monitoring as an element of sustainable development principles and green deal implementation”. Publication status: Published Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse ...