Burned-Area Mapping Using Post-Fire PlanetScope Images and a Convolutional Neural Network

Abstract

Forest fires result in significant damage, including the loss of critical ecosystems and individuals that depend on forests. Remote sensing provides efficient and reliable information for forest fire detection on various scales. The purposes of this study were to produce burned-area maps and to identify the applicability of transfer learning. We produced a burned-area (BA) maps using single post-fire PlanetScope images and a deep learning (DL)-based algorithm for three cases in the Republic of Korea and Greece. Publicly accessible Copernicus Emergency Management Service and land cover maps were used as reference data for classification and validation. The DL model was trained using six schemes, including three vegetation indicators, and the data were split into training, evaluation, and validation sets based on a specified ratio. In addition, the model was applied to another site and assessed for transferability. The performance of the model was assessed using its overall accuracy. The U-Net model used in this study produced an F1-score of 0.964–0.965 and an intersection-over-union score of 0.938–0.942 for BAs. When compared with other satellite images, unburned and non-forested areas were accurately identified using PlanetScope imagery with a spatial resolution of approximately 3 m. The structure and seasonality of the vegetation in each target area were also more accurately reflected because of the higher resolution, potentially lowering the transferability. These results indicate the possibility of efficiently identifying Bas using a method based on DL with single satellite images.