Fire, Volume 5, Issue 6
2022 December - 44 articles
Cover Story: Urban wildfires are dangerous due to spotting fire through ember attacks. The ability to identify areas with high ignition probability supports fire connectivity analysis, which is important for fire management in urban areas. The pattern recognition neural network (PRNN) was designed to predict ignitability based on temporal vegetation indices (VIs) behavior and assess its performance in comparison with the actual urban wildfire. The results of the study confirm that time series multispectral images provide sufficient information to classify vegetation according to its probability of ignition. Among the considered VIs, the best predictor was MSAVI, which reflects changes in vegetation biomass and canopy cover. The precision of the PRNN (RMSE = 0.85) gives ground for the application of the proposed method in risk assessment and fuel treatment planning on WUI and adjoined urban areas. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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