 GPS to record their positions. In addition to these samples, two reference targets - two sheets of plywood, 2.5 by 1.2 m, covered in black matte paint - were set up two hours before the start of the survey, approximately 1 m above the ground level, as far from expected sources of thermal pollution as possible [8
]. These reference targets were later used in two different ways. First, both targets were used together with other ground samples to verify the pixel to radiance/temperature conversion model. In case the GPS positioning error would be too high to reliably identify the ground samples on the thermal imagery, two plywood targets would be a fall-back option to get at least some data for the model verification. Based on the proposed spatial resolution of 0.29 m, the size of the targets used would be enough for an accurate visual location. Second, the plywood targets were used to investigate the change of the temperature of the exposed objects overnight, if any. The sampling period was selected to match the data collection interval of the on-campus meteorology station.
Data from the on-campus automatic meteorology station located on the rooftop of the Latham Hall were collected every 30 minutes and contained temperature, pressure, and relative humidity values. Data on wind direction and speed were obtained from the nearby (6 km) meteorology station in Waterloo, Iowa. Throughout the night, the ambient air temperature stayed close to -6 °C, with humidity of 79%. Wind direction and speed were stable for the whole time of the survey (330°, 8 m/s). Except for the wind speed, meteorological conditions were favorable for the thermal infrared survey – clear sky, low ambient temperatures and absence of precipitation all benefit thermal contrast [6
2.4 GIS Data
Thermal imagery requires significant amount of processing. Automated means of analysis must be found in order to fully employ the benefits of remote sensing approach. Using GPS coordinates as a spatial reference, ground control samples were transformed into a shapefile. Next, the raster extraction tool from the ESRI ArcGIS toolbox was used to extract matching pairs of aerial and ground measurements. Another shapefile with on-campus buildings was prepared by digitizing a high- resolution raster image in ArcMap. This shapefile was used to analyze raster data separately for every building. Finally, a shapefile with the structure of the UNI steam pipeline network was provided by the facility services department. This shapefile was used to locate the segments of the steam network that were in any way obscured on the thermal image.
3.1 Georeferencing and Mosaicing
As was mentioned before, aerial data was provided in the form of SAF files, which is an archive that stores a time series of images in the single-file form. In order to access individual raster images, another format had to be used. Portable Network Graphics format was chosen because it supports 16- bit grayscale data as well as lossless compression. An open-source software library, libpng
, developed by Schalnat et al.
] was used to create PNG rasters. Raw data was extracted from the SAF files with a small application written by the author of this paper in compliance with SAF specifications.
Before georeferencing and mosaicing, a histogram of the pixel values distribution was created (Figure 3
From the histogram, it is clear that the majority of pixels belong to the range of values between 1,300 and 2,100, which corresponds to less than 5% of the 16-bit range used by the camera. In order to improve visual contrast, the image histogram was stretched accordingly, using 850 and 2,835 as boundaries. Figure 4
is an example of an image before and after the histogram stretch. These boundary values were selected according to the Chebyshev's Inequality, which guarantees that 99% of the dataset are within the range specified.
After stretching the histogram, raster images were mosaiced manually in the ERDAS Imagine software using existing high-resolution airborne imageries of the UNI Campus as a reference. RMSE for the result