Abstract: The traditional remote sensing discrimination algorithm for transmission line forest fires usually uses the brightness temperature threshold method to directly perform the fire point discrimination, which makes it difficult to accurately set the threshold value and frequently lead to false discrimination or omissions. In order to realize the accurate remote sensing discrimination of transmission line forest fires, the idea of second discrimination of hot spots is proposed based on the traditional fire point discrimination algorithm, that is, after determining the possible hotspots according to the threshold value of the brightness temperature, the fuzzy comprehensive evaluation is carried out for the hotspots to determine the exact point of fire. According to the research of the time and spatial distribution of forest fires in Guangdong Province, six hazard factors causing the occurrence of forest fires are determined, and a fuzzy comprehensive evaluation method is proposed to realize the accurate discrimination of transmission line forest fires. Firstly, the fire risk decision-making set and threshold are determined according to the fire risk evaluation levels. Then, the factor set is established as input with the six hazard factors, and the membership function is constructed and the weight vector is set. The confidence of the hotspot is output according to the principle of maximum membership degrees, and compared with the threshold to determine the authenticity of the fire point. The model is verified using the forest fire database in Guangdong Province from 2005 to 2016 and by a case study of Guangdong Power Grid in 2017. The results show that the model has higher accuracy than traditional algorithms and meets the prevention and control requirements for transmission line forest fires.

Key words: transmission lines, forest fire discrimination, satellite remote sensing, hazard factors, fuzzy mathematics, membership function, weight vector