LAUSR

Forest fire not only seriously affects the stability of the forest ecosystem but also threatens the safety of human life and property. The previously burned areas extracting method mainly focuses on optical images, susceptible to cloud and fog objective environmental factors. Although there are related studies on the threshold segmentation of single SAR feature types, further information mining for SAR image data, e.g., backscattering intensity, polarization decomposition, texture, and other features, is still insufficient. Therefore, this letter proposes a burned area extracting method using polarization and texture features of Sentinel-1A images to combine and comprehensively mine various SAR feature change information caused by forest fires with a random forest (RF) model. For validation purposes, we compared the burned areas’ extracted results with the reference data acquired based on Sentinel-2A optical imagery. The comparative results show that the SAR extraction results highly agree with the reference data, with an accuracy of 87.12%, and the commission and omission errors were 20.44% and 12.88%, respectively. The proposed machine learning method helps extract fire areas covered by thick smoke or persistent clouds and provides a reference to related research.