own 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only after the line is dug out for maintenance or repair (most of the data available to the planning facilities of UNI is obsolete).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, standard deviation is 0.58°C. This makes the model accurate to within ±1.2°C.
5. Conclusions and Future Directions
In this study, a high-resolution thermal infrared map of the Campus of the University of Northern Iowa was produced. Inspection of the temperature map revealed a number of buildings that may be subject to insulation improvement due to their high rooftop temperature, as well as a number of smaller leaks, mostly related to ventilation exhaust. Most of the heat anomalies on the campus, however, are caused by poorly insulated steam pipelines. In particular, the large hot spot in the eastern of the campus is the result of high concentration of steam pipelines in the region as well as the shielding effect of the buildings. Overall, high-resolution thermal infrared imagery proved to be a highly useful tool for heat anomalies detection.
The observation conditions mentioned in the beginning of the paper – cold, dry and clear April night – played a critical part in the success of this survey. First of all, this ensured maximum thermal contrast between the objects of interest (buildings, steam pipelines) and their surroundings. Second, the combination of the low flying altitude and a cold dry atmosphere reduced the absorption and emission of the infrared energy by the column of air between the sensor and the objects of interest. In this paper, no corrections were made for the influence of the atmosphere, but this approach would not suffice for a survey with different observation conditions. A significant limitation of this study is absence of on- plane camera calibration. The ground control data was used to construct the pixel-to-temperature conversion model instead, which resulted in a much smaller validation dataset. Another limitation is that for the present moment, no correction for the emissivity values of the objects in question was done. An assumption was made that the UNI pavements fall into the generic “concrete” category mentioned in the ASTER Spectral Library, which would introduce a certain degree of 