Urban forest structure and land cover composition effects on land surface temperature in a semi-arid suburban area

Using multispectral imagery and LiDAR data, we developed a high-resolution land cover dataset for a semi-arid, Colorado (USA) suburb. These data were used to evaluate patterns of land cover composition and vertical structure in relation to land use and age of development. Landsat 5 TM thermal band data for six separate dates were used to compare land surface temperature (LST) in urbanized and remnant shortgrass steppe reference areas. We used 2010 census blocks to extract LST and various explanatory variables for use in Random Forest models evaluating the relative importance of land cover composition, LiDAR-derived vertical structure variables, and the Normalized Difference Vegetation Index (NDVI) on LST patterns. We found that land cover, vertical structure, and LST varied between areas with different land use and neighborhood age. Older neighborhoods supported significantly higher tree cover and mean tree height, but differences in LST were inconsistent between Landsat image dates. NDVI had the highest variable importance in Random Forests models, followed by tree height and the mean height difference between trees and buildings. Models incorporating NDVI, vertical structure, and land cover had the highest predictive accuracy but did not perform significantly better than models using just vertical structure and NDVI. Developed areas were cooler on average than shortgrass steppe reference areas, likely due to the influence of supplemental irrigation in urbanized areas. Patterns of LST were spatially variable, highlighting the complex ways land cover composition and vertical structure can affect urban temperature.