Fusion of airborne hyperspectral and LiDAR remote sensing data to study the thermal characteristics of urban environments

Abstract

This study focuses on the derivation of an urban surface material map to parameterize a 3D numerical microclimate model. For this purpose, fusion of airborne hyperspectral and light detection and ranging (LiDAR) remote sensing data is performed. In a first step, surface materials are extracted from the preprocessed input datasets using a hybrid, three-stage classification approach. The resulting map is then utilized in combination with the LiDAR object height information data to parameterize the microclimate model. To demonstrate the potential of data-driven microclimate modeling, two case studies are presented for selected test sites in the City of Houston, TX. The results of this study highlight that the synergistic combination of hyperspectral and LiDAR data enables reliable mapping of some of the key input parameters required for urban microclimate modeling. Moreover, classification-based microclimate simulations can reveal the thermal properties of urban neighborhoods under varying conditions and, thus, facilitate the identification of hot spot areas and critical land cover configurations.