Pixel-based Sharpening Enhancement of Surface Urban Heat Island (SUHI): Spatial-temporal Patterns and Underlying Drivers
Ms CHEN Yanhua PhD Student, Department of Geography, HKU
Urban heat island (UHI) is one phenomenon that enhances temperatures in urban areas comparing with rural areas. Attributed to urban land transformation, UHI refers to increasing sensible heat and decreasing latent heat flux. The rising temperatures in urban areas and those experiencing rapid urbanization strongly influences the urban climate, hydrology, soil, the atmospheric environment, biological habits and the health of urban residents. Therefore, assessing and monitoring UHI is needed to mitigate its effects and to help develop new strategies for sustainable urbanization. With the availability of thermal infrared data derived from remote sensing, UHI can also refer to the land surface temperature (LST) difference between urban areas and their surrounding (rural) areas, which is described as Surface Urban Heat Island (SUHI). A key challenge for thermal remote sensing applications in urban areas is the assessment of the magnitude/intensity of SUHI effects at multiple spatial and temporal scales considering the distribution of the different land cover types (e.g., urban areas vs. green or blue infrastructures).
The overarching goal of this research is to develop a method for quantifying the magnitude of the variation of SUHI from thermal remote sensing images and identifying the main drivers underlying its spatial and temporal patterns (i.e., land cover types). The specific objectives can be summarized as follow: (1) to understand the effects of SUHI in urbanized surfaces in terms of spatial and temporal change; (2) to assess the magnitude of the variation of SUHI across space and time using a continuous function that includes both urban and rural surfaces; (3) to disentangle the effects of SUHI at pixel-level by sharpening discrete measurements of LST and (4) to analyze the main underlying factors behind the spatial-temporal patterns and change of SUHI. Such a method consists in a series of cloud-based algorithms (second derivative methods) developed in Google Earth Engine (GEE) that are able to process large sets of remote sensing images and extract a pixel-level sharpen enhanced value (SUHIen) of SUHI in urban areas. SUHIen has been applied to 21 cities in mainland China. It provides a comprehensive perspective of the surface thermal environment to assess UHI effects that can consistently, cost-efficiently and quickly support strategies for sustainable urbanization.