Conclusion
In this paper we demonstrate the application of STURLA classification to quantify the relationship between urban structure and surface temperature in Philadelphia. We show it can be applied to cities with different historical patterns of growth in a reproducible manner. Furthermore, patterns in class abundance and composition can be used to determine the surface temperature signature of a composite landscape. Additional research is needed to compare cities of vastly different urban structure and identify patterns in the relationship between urban structure with social and ecological properties of the environment. Understanding general urban structure-environmental function relationships will help build tools for effective urban planning and management under global change scenarios.
Table 1: 10 most common STURLA classes in Philadelphia and their ST statistics. STURLA class codes: t-trees; g-grass; b-bare soil; w-water; p-paved; l-low building; m-medium building