Structure of Urban Landscape and Surface Temperature: A Case Study in Philadelphia, PA

Abstract

Discerning the relationship between urban structure and function is crucial for sustainable city planning and requires examination of how components in urban systems are organized in three-dimensional space. The Structure of Urban Landscape (STURLA) classification accounts for the compositional complexity of urban landcover structures including the built and natural environment. Building on previous research, we develop a STURLA classification for Philadelphia, PA and study the relationship between urban structure and land surface temperature. We evaluate the results in Philadelphia as compared to previous case studies in Berlin, Germany and New York City, United States. In Philadelphia, STURLA classes hosted ST that were unique and significantly different as compared to all other classes. We find a similar distribution of STURLA class composition across the three cities, though NYC and Berlin showed strong correlation with each other but not with Philadelphia. Our research highlights the use of STURLA classification to capture a physical property of the urban landscape

Temporal change in relationships between urban structure and surface temperature

“…All available LST rasters for the defined time period that contained less than 30% cloud cover over the NYC boundaries were used. The time period was chosen as between June 21 st and September 22 nd for each year as it would allow us to look at both the mean across days and the variance between them (Hamstead et al, 2016; Mitz et al, 2021). These provide a wide representation of summer temperatures to validate that STURLA identifies patterns between urban structure and LST and thus can be used in future urban planning practices.…”

“…STructure of URban LAndscape (STURLA) classification studies have demonstrated that urban structure can be explained by a discrete number of heterogeneously distributed three-dimensional 120 m 2 pixels composed of differing landscape elements (e.g., trees or high-rise buildings) (Hamstead et al, 2016). Previously, STURLA has been used to investigate LST (Hamstead et al, 2016; Kremer et al, 2018; Larondelle et al, 2014; Mitz et al, 2021), microbial diversity (Stewart et al, 2021), and air pollution (Cummings et al, 2022). STructure of URban LAndscape explicitly uses the height of buildings in cities from publicly available records and thus may be more meaningful for urban planners and designers when planning infrastructure.…”

Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

“…Urban sprawl contributes to an increase in impervious surfaces, such as buildings, roads, parking lots, technical infrastructure, etc., which are the indicators of the degree of urbanisation, reflecting the environmental quality of urban areas. In turn, the growth of built-up land affects, among other things, the formation of urban heat islands [7][8][9][10], increased pollution [11], water management [12] and the structure and functioning of the city [13][14][15]. Thus, land cover types and the spatial configuration of their structures affect the quality of human life [16].…”

Temporal Change in Relationships Between Urban Structure and Surface Temperature

“…trees or highrise buildings) (Hamstead et al, 2016). Previously STURLA has been used to investigate ST (Hamstead et al, 2016;Kremer et al, 2018;Larondelle et al, 2014;Mitz et al, 2021), phylogenetic structure of the atmospheric microbiome (Stewart et al, 2021) and particulate air pollution (Cummings et al, 2021). STURLA captures urban spatial patterns that historically have been difficult to quantify due to the high density and the patchy nature of spatial patterns within a city.…”

“…All available ST rasters for the defined time period that contained less than 30% cloud cover over the NYC boundaries were used. The time period was chosen as between June 21 st and September 22 nd for each year as it would allow us to look at both the mean across days and the variance between them (Hamstead et al, 2016;Mitz et al, 2021). Likewise these provide a wide representation of summer temperatures to validate that STURLA identifies patterns between urban structure and ecosystem function and thus be used in future urban planning practices.…”