The impact of spatial arrangements of built land cover types on urban warming

While the relationship between fractional cover of built features and the urban heat island (UHI) has been well studied, relationships of how spatial configuration (e.g., clustered, dispersed) of these features influence urban warming are not well understood. As buildings and paved materials are defining features of the urban environment it is important to explore the spatial pattern of these features to understand how they influence urban warming effect. The goal of this study is to examine if and how spatial arrangements of anthropogenic features (buildings, paved surfaces) influence land surface temperatures (LST) in an urban environment. This study focuses on Las-Vegas, NV, a desert city that has undergone dramatic urban center expansion and population growth since the 1960s. The data used to classify land cover and extract building consist of Geoeye-1 (formerly Orbview 5) image. The image used was taken on October 12, 2011 and has a spatial resolution of 3m. Classification was carried out using object based image analysis (OBIA). A spatial autocorrelation approach (i.e., local Moran's I) that measures the spatial dependence of a point location to its neighboring points and describes how clustered or dispersed points are arranged in space was employed. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data acquired on July 6, 2005 (daytime) and August 27, 2005 (nighttime) were regressed against spatial patterns of anthropogenic features. Results from this study suggest that clustered spatial arrangements of buildings and paved surfaces elevate surface temperatures more severely.