Lightning-caused forest fire risk rating assessment based on case-based reasoning: a case study in DaXingAn Mountains of China

Lightning-caused forest fires can cause serious damage to the social economy and public property and even threaten human life. Therefore, lightning-caused forest fire risk rating assessment is very important for forest management agency, because the risk rating assessment results could provide important information to prevent fires and allocate extinguishing resources. The existing forest fire risk rating assessment methods are more difficult for the area with sparse meteorological stations, imperfect lightning monitoring systems and complex terrain conditions. Based on remote sensing data and case-based reasoning principle, this paper proposed a method to overcome the limitations of existing forest fire risk rating assessment methods. The proposed method uses three dynamic and two static indexes to characterize the potential fire environment. The dynamic indexes are temperature condition index, vegetation condition index and water condition index. The static indexes are terrain fluctuation and LIS/OTD lightning density. In DaXingAn Mountains of China, the fire risk rating spatial distribution maps with 8-day cycle before the occurrences of historical lightning-caused fires were produced by using the lightning-caused forest fire risk rating assessment method during 2000–2006 in this paper. The results showed that most of the historical lightning-caused fires occurred in the region with high fire risk rating, and the spatial–temporal distribution changes of the lightning-caused fire risk rating followed the same trend as the changes in the number of lightning-caused fires. Therefore, the lightning-caused forest fire risk rating assessment method proposed in this paper could assess the fire risk rating effectively, and this method could also provide a reference for other countries and regions with sparse meteorological stations and imperfect lightning monitoring systems. Copyright Springer Science+Business Media Dordrecht 2016