[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Wildfire Risk Assessment Using Multi-Source Remote Sense Derived Variables

摘要： This study focuses on the forest fire risk assessing using entirely remote sensing derived variables. These variables include Fuel moisture content (FMC), Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Elevation and Slope. The Difference and Anomaly of FMC in time series are also calculated since FMC is one of the critical factors in assessing the wildfire risk. The logistic regression model is used to integrate all the variables in the fire occurred and none-occurred areas to derive the Fire Risk Index (FRI). A case study of the above methodology is applied to assess the FRI in Yunnan Province in China. The result shows that the AUC is to 0.8 for grassland and 0.81 for woodland, which indicates the good performance of the presented methodology in assessing forest fire risk.