andard 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 error into the radiance to temperature conversion. Provided that the selection of the emissivity level does influence the derived temperature values directly, this is an important consideration. However, with the help of the Campus Planning Facilities, it would be possible to obtain the samples and exact types of all on- campus building materials, which would improve the accuracy of temperature retrieval. With the help of GIS data, emissivity correction can be applied to individual objects, instead of calibrating the whole map to a single level of emissivity. This work is reserved for a future paper. Finally, the oblique imagery obtained in this survey can be georeferenced and processed in the manner similar to the nadir imagery. As it was mentioned earlier, it would be necessary to account for the anisotropic nature of emissivity as well as the directional wind effects. This might be an opportunity to analyze the heat anomalies present in the buildings' walls.