SSU-Net: A Model for Mapping Burned Areas Using Landsat-8 and Sentinel-2

Abstract

Information on burned areas (BAs) is particularly important for research on forest fires. Mapping BAs contributes to managing and preventing further destruction. BA mapping has long been constrained by the temporal and spatial resolution of satellite data and classification accuracy, while also neglecting the spectral characteristics of optical satellites. To address this issue, a novel method for mapping and extracting BAs using a semantic segmentation model has been proposed herein. Leveraging medium spatial resolution satellites, Landsat-8 and Sentinel-2, and after determining the optimal spectral band combination, a spectral-spatial module was integrated into the U-Net; this resulted in a new model, known as the SSU-Net. SSU-Net precisely extracted BAs from a single post-fire image. The optimal band combinations for Landsat-8 and Sentinel-2 were bands 7-5-4 and 12-8-4, respectively. SSU-Net achieved an $F1$ score of 0.9296 and an intersection over union (IoU) of 0.8707 on Landsat-8 data. It achieved slightly higher performance on Sentinel-2 with an $F1$ score of 0.9314 and IoU of 0.8752. Compared with U-Net, SSU-Net improved the $F1$ scores by 4.8% and 3.4% for Landsat-8 and Sentinel-2, respectively, and IoU by 7.7% and 5.9%. In terms of generalization, SSU-Net achieved an $F1$ score of 0.9151 when trained on Landsat-8 data and tested on Sentinel-2 data, and 0.9313 vice versa, with improvements of 2.1% and 5.1% over U-Net, respectively. SSU-Net also demonstrated higher accuracy and generalization in BA mapping, supporting the integration of multisource remote sensing data and emphasizing its crucial role in sustainable forest management on a global scale.