Fusion of Airborne Hyperspectral and LiDAR Remote Sensing Data to Study the Thermal Characteristics of Urban Environments - Computational Approaches for Urban Environments - page 268

11.3.2 Data Basis

This study makes use of the Compact Airborne Spectrographic Imager (CASI) and
LiDAR data provided within the 2013 IEEE GRSS Data Fusion Contest (Image
Analysis and Data Fusion Technical Committee 2013 ). Both datasets cover the
campus and the neighboring urban area of the University of Houston and share
a common spatial resolution of 2.5 m. The CASI image was acquired between
12:37:10 and 12:39:40 CDT on June 23, 2012. Captured at an average height of
1,676 m aboveground, it features 144 spectral bands in the 380-1,050 nm region
that were calibrated to at-sensor spectral radiance .W=.cm 2 sr nm//.TheLiDAR
data were recorded between 09:37:55 and 10:38:10 CDT on June 22, 2012. The
airborne sensor's average height aboveground was 610 m. A DSM with elevation
in meters above sea level was derived from the point cloud, registered to the CASI
data, and delivered in GeoTIFF format. The nominal vertical accuracy of the DSM
is 10-15 cm; its horizontal accuracy is about 20-30 cm.

11.4 Methods

The overall workflow presented in this study consists of three consecutive steps:
(i) data preparation, (ii) material mapping, and (iii) microclimate modeling.
Figure 11.1 illustrates the role of the datasets being used in the context of each stage
of the data fusion approach. After data preparation, surface materials are extracted
from the preprocessed CASI and LiDAR data by means of feature fusion (Pohl and
van Genderen 1998 ). The surface material map is then utilized in combination with
the object height information provided by the LiDAR data to parameterize a 3D
microclimate model for simulating the spatial patterns of urban air temperature at
day- and nighttime. In the following sections, the three abovementioned steps are
described in more detail.

Fig. 11.1 The conceptual workflow of this study