Relationships between landscape pattern metrics, vertical structure and surface urban Heat Island formation in a Colorado suburb

Change in land surface temperature (LST) due to urbanization is a critical influence on the ecohydrological function and human health in cities. Past research has emphasized land cover composition and planimetric landscape pattern as drivers of LST patterns, but there have been few empirical studies examining the effects of vertical structure on thermal variation in cities. We used agglomerative cluster analysis to group 5 ha grid cells with similar land cover composition from high-resolution land cover data for our Aurora, Colorado, USA study area. We then compared the importance of different planimetric landscape pattern metrics and lidar-derived vertical structure variables as predictors of LST estimated from Landsat 5 thermal band data for each group. Variation in LST between cluster groups was analyzed with landscape and vertical structure data using random forest regression models and conditional variable importance metrics. Cluster analysis produced 7 distinct groups differing in land cover composition, planimetric landscape pattern, and vertical structure. Clusters with greater tree cover and higher mean tree height were generally cooler, while the height difference between buildings and trees had high conditional variable importance in random forest regression models. We found that the specific planimetric landscape pattern and vertical structure metrics most important for individual clusters differed, suggesting that the relative importance of variables depends on land-use and land cover. Our results highlight the importance of including vertical structure in empirical analyses of urban LST.