Enhanced contextual forest fire detection with prediction interval analysis of surface temperature using vegetation amount

Abstract

ABSTRACTEarly detection of small forest fires is important for forest management because it can prevent fires from spreading and causing severe environmental and economic damage. This study proposes a new method that uses a negative relationship between vegetation amount and land surface temperature to determine a temperature threshold for detecting small forest fires. The proposed method analyses the differences between brightness temperature in remote-sensing data and that estimated from a regression model of brightness temperature and vegetation amount measured by the normalized difference vegetation index. The upper prediction interval of estimated brightness temperature based on a statistical test of the differences was used for the temperature threshold. This method was compared with the Moderate Resolution Imaging Spectroradiometer contextual algorithm using two accuracy measures: precision and recall. The results showed that the proposed method improved the recall accuracy, and its precision accuracy was similar to that of the contextual algorithm. This indicates that the proposed method detected more small forest fires with a similar false detection rate as that of traditional methods.