137 7 Applying Imaging Spectrometry in Urban Areas Martin Herold, Sebastian Schiefer, Patrick Hostert, and Dar A. Roberts CONTENTS 7.1 Introduction ................................................................................................... 137 7.2 Understanding Urban Spectra....................................................................... 138 7.2.1 Spatial Scale ...................................................................................... 138 7.2.2 Urban Materials vs. Land Cover and Use ........................................ 139 7.2.3 Terminology and Spectra Acquisition .............................................. 140 7.2.4 Spectral Characteristics of Urban Materials .................................... 141 7.2.5 Spectral Sensor Requirements .......................................................... 144 7.2.6 Directional Reflectance ..................................................................... 146 7.3 Application Case Studies .............................................................................. 151 7.3.1 Fire Danger Assessment ................................................................... 151 7.3.2 Indicators in Urban Ecology............................................................. 152 7.3.3 Management of Transportation Infrastructure.................................. 154 7.3.4 Disaster Response ............................................................................. 156 7.3.5 Urban Climate, Air Quality, and Human Health ............................. 157 7.4 Summary ....................................................................................................... 157 References .............................................................................................................. 158 7.1 INTRODUCTION The use of imaging spectrometry for urban applications has made considerable progress over the past few years in tandem with advances in high-spatial resolution urban remote sensing using sensors such as IKONOS. Imaging spectrometers acquire a large number of spectral bands with narrow bandwidths, and numerous studies have taken advantage of the vast amount of spectral detail for precise identification of chemical and physical material properties (Goetz et al., 1985). Traditionally, the majority of imaging spectrom- etry, also referred to as hyperspectral remote sensing, has focused on natural targets such as vegetation (e.g., Roberts et al., 1993) and minerals (e.g., Clark, 1999).