A deep convolutional neural network for burn progression mapping using Sentinel-1 SAR time-series

Forest fires burn various natural ecosystems worldwide and can harm the environment and human life. Accordingly, real-time monitoring of this phenomenon and early decision-making warning is vital. Active remote sensing systems, such as Synthetic Aperture Radar (SAR) sensors, provide an excellent opportunity for burn mapping because they can penetrate through clouds and smoke day and night. In this study, the potential of Sentinel-1 SAR data was investigated by deploying a deep convolutional neural network (CNN) based framework to map burn progression dynamically, in both supervised and transfer manners. Accordingly, an optimized deep architecture was designated to use SAR data features with high sensitivity to map the burned areas. The proposed method includes three main steps: 1) extraction of SAR indices, 2) training deep CNN model with a limited number of scenes and 3) assessing the transferability of the CNN for estimating burn progression for any unseen scene. Sentinel-1 SAR indices of log-ratio, radar burn difference (RBD), and difference of dual-polarization SAR vegetation index (Delta DPSVI) were obtained to be fed to the CNN. To validate the efficiency of the proposed approach, two fire events, i.e. the Derazno fire in Iran (2021) and the Rossomanno-Grottascura-Bellia fire in Italy (2017), were considered. For the scenes including training samples, the proposed method improved the overall accuracies (OAs) of classical machine learning techniques (i.e. SVM and RF) significantly (more than 4%). However, the improvement was minor when compared to a CNN using only log-ratio as the input channel (log-ratio CNN). For the scenes without training samples (unseen dates), the investigated transferred model performed substantially better (3% higher OA) compared to the other machine learning methods and the log-ratio CNN. This finding approves that the obtained SAR indices boost the transferability of the CNN model for burn progression mapping at unseen scenes.