Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures

Urban forestry is an important component of the urban ecosystem that can effectively ameliorate temperatures by providing shade and through evapotranspiration. While it is well known that vegetation abundance is negatively correlated to land surface temperature, the impacts of the spatial arrangement (e.g. clustered or dispersed) of vegetation cover on the urban thermal environment requires further investigation. In this study, we coupled remote sensing techniques with spatial statistics to quantify the configuration of vegetation cover and its variable influences on seasonal surface temperatures in central Phoenix. The objectives of this study are to: (1) determine spatial arrangement of green vegetation cover using continuous spatial autocorrelation indices combined with high-resolution remotely-sensed data; (2) examine the role of grass and trees, especially their spatial patterns on seasonal and diurnal land surface temperatures by controlling the effects of vegetation abundance; (3) investigate the sensitivity of the vegetation–temperature relationship at varying geographical scales. The spatial pattern of urban vegetation was measured using a local spatial autocorrelation index—the local Moran’s Iv. Results show that clustered or less fragmented patterns of green vegetation lower surface temperature more effectively than dispersed patterns. The relationships between the local Moran’s Iv and surface temperature are evidenced to be strongest during summer daytime and lowest during winter nighttime. Results of multiple regression analyses demonstrate significant impacts of spatial arrangement of vegetation on seasonal surface temperatures. Our analyses of vegetation spatial patterns at varying geographical scales suggest that an area extent of ˜200 m is optimal for examining the vegetation–temperature relationship. We provide a methodological framework to quantify the spatial pattern of urban features and to examine their impacts on the biophysical characteristics of the urban environment. The insights gained from our study results have significant implications for sustainable urban development and resource management.