Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high temperature of a fire in this channel is used for initial screening, the threshold is relatively high. Although screening results are tested at different levels, few small fires will be lost under these strict test conditions. However, crop burning fires often occur in East Asia at a small scale and relatively low temperature, such that their radiative characteristics cannot meet the global threshold. Here, we propose a new weighted fire test algorithm to accurately detect small-scale fires based on differences in the sensitivity of test conditions to fire. This method reduces the problem of small fires being ignored because they do not meet some test conditions. Moreover, the adaptive threshold suitable for small fires is selected by bubble sorting according to the radiation characteristics of small fires. Our results indicate that the improved algorithm is more sensitive to small fires, with accuracies of 53.85% in summer and 73.53% in winter, representing an 18.69% increase in accuracy and a 28.91% decline in error rate.

Keywords

• visible infrared imaging radiometer suite
• active fire identification
• mid-infrared screening
• weighted fire algorithm
• adaptive threshold