A simple method based on the thermal anomaly index to detect industrial heat sources

Abstract

Because waste gas from industrial burning has a significant effect on urban environment, it is important to detect industrial heat sources from remote sensing data. Given existing pyrometry, it is difficult to identify small factories with low burning temperatures. In addition, existing fire detection methods (such as the contextual algorithm) are cumbersome, complex, and contain multiple thresholds to be determined. With the purpose of detecting industrial heat sources efficiently and simply, we introduced a simple method based on the thermal anomaly index (TAI) to detect industrial heat sources. This index was constructed based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) data with a detectable temperature of 400 K, which is lower than that used in most high-temperature detection methods. By confirming with the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and high-resolution images, the TAI confidently detected almost all hot spots with the VIIRS Nightfire product and detected many hot spots that were undetected by the VIIRS Nightfire product. Based on six images acquired over Tangshan, we determined that 54.52% of hot spots were undetected by the VIIRS Nightfire product, while the TAI method was able to detect these hot spots. With the MODTRAN 5 radiative transfer model, we simulated the high-temperature detection ability of the TAI. Compared with the VIIRS Nightfire product, the TAI is more sensitive when detecting hot spots below 700 K. Thus, this method can potentially detect family workshops engaged in the small-scale combustion of fuel.