Analyzing the Impacts of Urbanization and Seasonal Variation on Land Surface Temperature Based on Subpixel Fractional Covers Using Landsat Images

Abstract

Impervious surface areas (ISAs) and vegetation are two major urban land cover types. Estimating the spatial distribution of ISA and vegetation is critical for analyzing urban landscape patterns and their impact on the thermal environment. In this paper, linear spectral mixture analysis (LSMA) is used to extract their respective subpixel land cover composition from bitemporal Landsat images and the accuracy of the fractional covers is assessed with a subpixel confusion matrix at the category level and the map level by comparing with the reference data from high-resolution images. The percent ISA was divided into discrete categories representing different urban development density areas. Mean land surface temperature (LST) is calculated for each ISA category to analyze the thermal characteristics of different levels of development in the urban area of Fuzhou, China. ISA and vegetation variations are also quantified between different ISA categories and different dates. The contribution index is also calculated based on each ISA category to analyze the impact of different landscape patterns on the urban thermal environment. The results show that ISA category is an important determinant of the urban thermal environment. Furthermore, seasonal variations significantly impact the strength of this relationship. In the study area, the contribution indices were highest in the 90%-100% ISA category in summer 2013 and early spring 2001. The analytical methodologies used in this study can help to quantify urban thermal environmental functions under conditions of urban expansion and explore the climate adaptation potential of cities.

Keywords

• Contribution index (CI)
• fractional vegetation cover (FVC)
• land surface temperature (LST)
• linear spectral unmixing
• percent impervious surface area (ISA)
• subpixel confusion matrix