sible relationship between the walls temperature and the halo intensity. This effect is attributed to the reflection and re-emission of thermal radiation by the ground surface. Treado & Burch [12
] attempted to derive the temperature of the object using its halo, but no clear relationship was found. Nonetheless, this effect serves the purpose of illustrating the wind-sheltering effect [8
] of the on-campus buildings. The direction and the speed of the wind remained steady throughout the night (330°, 8 m/s). As a result, the areas to the south of the structures are noticeably warmer than their northern counterparts.
As was mentioned before, the halo effect is poorly suited for analysis of wall temperatures. The study by Baraniak and Williams [1
] is an example of a ground-based thermal imaging survey that deals with the building's walls directly. That study demonstrated that a number of significant temperature anomalies can be found by examining the structures from the side view. Because of the sharp turns the plane was performing at the end of each flight line, a number of oblique images of the buildings in the vicinity of the campus were produced. One of the best examples is present in the Figure 9
. The building shown is Cedar Falls High School, located about 1 km to the north of the campus. The high visual contrast between the rooftop and the walls is obvious. No data on the insulation levels of this building was collected at the time of this paper being written, neither were there any ground samples available for that particular location. It should be noticed that factors other than temperature, such as the directional emissivity of the surfaces as well as wind effects contribute significantly to the observed contrast. Nonetheless, it would be useful to investigate the possibility of automated analysis of oblique thermal imagery in the future.
4.3 Steam pipelines
Several papers [2
] illustrate that aerial infrared imagery is convenient and cost-efficient when heat loss from buried pipelines is analyzed. As shown by MacKay [2
], pipeline locations must be known during the analysis because of the vast range of surface types, which may completely mask the thermal trace. Compared to other objects on campus, steam pipelines stand out most vividly (Figure 10
). Combined with the shielding effect of the buildings, the heat coming from underground pipelines is the most possible explanation of the elevated temperatures in the eastern part of the campus. Estimating exact heat loss from underground pipelines requires precise knowledge of the burial depth and insulation levels of the pipeline. However, this information is usually obtained only after the line is dug out for maintenance or repair (most of the data available to the planning facilities of UNI is obsolete). However, thermal maps are still valuable as an excellent tool to locate segments of the steam network that require attention and, possibly, improvement.
4.4 Automated analysis
One of the issues not addressed in many papers is automated analysis of the imagery. As was mentioned in the Methodology section, a shapefile with vector representation of on-campus buildings was created. Using this shapefile and the extraction tool from the ESRI ArcGIS Toolbox, separate imagery datasets for each of the buildings were created. Pixel values from each dataset were extracted and used for simple statistical analysis, such as histogram plots. A number of examples are presented in Figure 11
(polygons from the shapefile are shown in red). Histogram peaks define the most frequent pixel values (or temperatures, for that matter). This approach allows for numerical comparison of the average temperatures between pairs of buildings automatically. For the UNI Campus the benefits are not so obvious, given the low number of buildings. For surveys of large residential areas, however, automation is important.
Temperature estimates produced by the model were compared with the ground measurements. The results of the validation are presented in Table 1
. The mean temperature error is -0.25°C, st