Surface temperature influences human health directly and alters the biodiversity and productivity of the environment. While previous research has identified that the composition of urban landscapes influences the physical properties of the environment such as surface temperature, a generalizable and flexible framework is needed that can be used to compare cities across time and space. This study employs the Structure of Urban Landscapes (STURLA) classification combined with remote sensing of New York City’s land surface temperature (LST). These are then linked using machine learning and statistical modeling to identify how greenspace and the built environment influence urban surface temperature. Further, changes in urban structure are then connected to changes in LST over time. It was observed that areas with urban units composed of largely the built environment hosted the hottest temperatures while those with vegetation and water were coolest. Likewise, this is reinforced by borough-level spatial differences in both urban structure and heat. Comparison of these relationships over the period between 2008 and 2017 identified changes in surface temperature that are likely due to the changes in the presence of water, low-rise buildings, and pavement across the city. This research reinforces how human alteration of the environment changes LST and offers units of analysis that can be used for research and urban planning.
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