Exploring the relationship between remote sensing-based vegetation indices and land surface temperature through quantitative analysis

Abstract

In an era of rapid urbanization, understanding the complex relationship between vegetation dynamics and land surface temperature (LST) is crucial for addressing the challenges posed by urban heat island (UHI) and promoting sustainable urban development. Our study aimed to explore these dynamics in a rapidly urbanizing environment by analyzing the relationships between remote sensing-based vegetation indices and LST through quantitative analysis. A cloud-free Landsat 8 OLI/TIRS level 2 satellite imagery of Ibadan region city for 2022 was obtained from the United State Geological Survey (USGS) and LST was estimated using the radiative transfer approach. Utilizing different combinations of spectral bands, seven vegetation indices including Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI), Green Normalized Difference Vegetation Index (GNDVI), Ratio Vegetation Index (RVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Green Chlorophyll Index (GCI) were analyzed. Combined with the spatial distribution of LST in Ibadan, regression analysis were performed to explore the relationship between vegetation indices and LST. Results indicate that among the seven vegetation indices, ARVI has the strongest correlation with LST in the study area (R2 = 0.65). Additionally, urban cores experience lower vegetation density and higher LST values, which can be attributed to land use intensity, anthropogenic heat release, and impervious surface cover while the peripheral areas exhibit higher vegetation indices and lower LST values. The findings of this study contribute to a deeper understanding of urban environmental dynamics and provide valuable insights for sustainable urban planning, ecosystem management, and climate adaptation strategies in rapidly urbanizing areas.