Fusion of Airborne Hyperspectral and LiDAR Remote Sensing Data to Study the Thermal Characteristics of Urban Environments - Computational Approaches for Urban Environments - page 264

Chapter 11
Fusion of Airborne Hyperspectral and LiDAR
Remote Sensing Data to Study the Thermal
Characteristics of Urban Environments

Christian Berger, Frank Riedel, Johannes Rosentreter, Enrico Stein,
Sören Hese, and Christiane Schmullius

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, Texas. 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.

Keywords Data fusion ￿ Hyperspectral ￿ LiDAR ￿ Surface material ￿ Mapping ￿
Urban microclimate ￿ Modeling

11.1 Introduction

Over the past decades, the world has faced a continuous and increasingly dynamic
urbanization. While city dwellers make up one-half of the world's population today,
this share is predicted to add up to 70 % by 2050 (United Nations 2008 ). The