Estimating the relationship between urban 3D morphology and land surface temperature using airborne LiDAR and Landsat-8 Thermal Infrared Sensor data

Abstract:

Urban forests are known for mitigating the urban heat island effect and heat-related health issues by reducing air and surface temperature. Beyond the amount of the canopy area, however, little is known what kind of spatial patterns and structures of urban forests best contributes to reducing temperatures and mitigating the urban heat effects. Previous studies attempted to find the relationship between the land surface temperature and various indicators of vegetation abundance using remote sensed data but the majority of those studies relied on two dimensional area based metrics, such as tree canopy cover, impervious surface area, and Normalized Differential Vegetation Index, etc. This study investigates the relationship between the three-dimensional spatial structure of urban forests and urban surface temperature focusing on vertical variance. We use a Landsat-8 Thermal Infrared Sensor image (acquired on July 24, 2014) to estimate the land surface temperature of the City of Sacramento, CA. We extract the height and volume of urban features (both vegetation and non-vegetation) using airborne LiDAR (Light Detection and Ranging) and high spatial resolution aerial imagery. Using regression analysis, we apply empirical approach to find the relationship between the land surface temperature and different sets of variables, which describe spatial patterns and structures of various urban features including trees. Our analysis demonstrates that incorporating vertical variance parameters improve the accuracy of the model. The results of the study suggest urban tree planting is an effective and viable solution to mitigate urban heat by increasing the variance of urban surface as well as evaporative cooling effect.