Near Real-Time Burned Area Progression Mapping With Multispectral Data Using Ensemble Learning

Monitoring the wildfire progression is essential to quantify the fire-disturbance areas for emergency responses. To combine the advantages of pixelwise machine learning (ML) method and region-based deep learning (DL) segmentation model, this study proposes a two-phase hybrid framework for near real-time burned area progression mapping: the first one intends to depict burned area delimitation using a contextual algorithm HRNet to exclude the unburned areas outside the perimeter and minimize omission errors, which partially remain unburned patches within the delimitation as commission errors. The second phase refines the burned area spatially using ensemble fusion based on an updating support vector machine (SVM) model under the voting scheme as new imagery arrives to reduce the commission errors consecutively. The validation results showed that the accuracy of perimeter prediction using the HRNet can reach 96.77% in Kappa. The iterative optimization can improve the average Kappa value from 62.55% to 70.75% for burned area pixel classification using pixelwise SVM alone. The proposed ensemble learning framework can further refine the burned area progression results, reaching an average Kappa up to 85.19%, at four acquisition dates with Sentinel-2 and Landsat-8 available during the Sand fire event that occurred in California.