With global warming, the incidence of forest fires has risen significantly, negatively affecting the ecological balance and the environment. This paper is aimed at summarizing the current state of research and global development trends regarding monitoring of forest fires with remote sensing. In the present study, a total of 8841 documents from 2009 to 2024 were extracted with the goal of gaining insights into the progress and dynamics of forest fire monitoring based on remote sensing and the core library of the Web of Science (WoS) and visualized and analyzed with CiteSpace bibliometric software. Results show that research on remote sensing to monitor forest fires has mainly gained momentum in the last 16 years and has shown a significant increase after 2018. The United States, as a major research center, leads the global scientific community in terms of national and institutional collaborations. Forests, with 382 publications, was ranked first as the leading published journal in the field. Dr. Sun of the University of Northeast Forestry University, China, became the most prolific author, ranking first with 16 citations. The influence of driving factors on forest fires imposed on properties of soil drivers is revealed through the analysis of trends in research areas. Forest fire spread, index, fire trend will be the next focus of research. By combining biblio-metric methods and systematic reviews, these findings provide forest fire managers and researchers with a deeper comprehension of the development of monitoring forest fires using remote sensing.

It is of great significance to map forest fire burn scars for post-disaster management and assessment of forest fires. Satellites can be utilized to acquire imagery even in primitive forests with steep mountainous terrain. However, forest fire burn scar mapping extracted by the Burned Area Index (BAI), differenced Normalized Burn Ratio (dNBR), and Feature Extraction Rule-Based (FERB) approaches directly at pixel level is limited by the satellite imagery spatial resolution. To further improve the spatial resolution of forest fire burn scar mapping, we improved the image super-resolution reconstruction via sparse representation (SCSR) and named it modified image super-resolution reconstruction via sparse representation (MSCSR). It was compared with the Burned Area Subpixel Mapping–Feature Extraction Rule-Based (BASM-FERB) method to screen a better approach. Based on the Sentinel-2 satellite imagery, the MSCSR and BASM-FERB approaches were used to map forest fire burn scars at the subpixel level, and the extraction result was validated using actual forest fire data. The results show that forest fire burn scar mapping at the subpixel level obtained by the MSCSR and BASM-FERB approaches has a higher spatial resolution; in particular, the MSCSR approach can more effectively reduce the noise effect on forest fire burn scar mapping at the subpixel level. Five accuracy indexes, the Overall Accuracy (OA), User’s Accuracy (UA), Producer’s Accuracy (PA), Intersection over Union (IoU), and Kappa Coefficient (Kappa), are used to assess the accuracy of forest fire burn scar mapping at the pixel/subpixel level based on the BAI, dNBR, FERB, MSCSR and BASM-FERB approaches. The average accuracy values of the OA, UA, PA, IoU, and Kappa of the forest fire burn scar mapping results at the subpixel level extracted by the MSCSR and BASM-FERB approaches are superior compared to the forest fire burn scar mapping results at the pixel level extracted by the BAI, dNBR and FERB approaches. In particular, the average accuracy values of the OA, UA, PA, IoU, and Kappa of the forest fire burn scar mapping at the subpixel level detected by the MSCSR approach are 98.49%, 99.13%, 92.31%, 95.83%, and 92.81%, respectively, which are 1.48%, 10.93%, 2.47%, 15.55%, and 5.90%, respectively, higher than the accuracy of that extracted by the BASM-FERB approach. It is concluded that the MSCSR approach extracts forest fire burn scar mapping at the subpixel level with higher accuracy and spatial resolution for post-disaster management and assessment of forest fires.