# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 6 Conclusions  
This paper presents a comprehensive overview on the progress and development of RS-based applications in the detection and monitoring of forest burnt area. We identified a total of 329 relevant papers in a selected 48 peer-reviewed journals pertaining to all aspects of remote sensing of forest BA within the last two decades. A database containing information on RS-based forest BA estimates was constructed to serve as a base for meta-analysis. We analysed the papers with regard to the research focus, ecological zoning, spatial resolution of the RS images, temporal scope, tree species, spectral indices, reference data, overall accuracies, and classification methods applied. A deep meta-analysis was carried out to discuss the application of RS systems in forest post-fire studies worldwide, which differentiates our study from previous reviews. Topics under consideration include burned area estimates and mapping, burn severity assessment, and monitoring of post-fire forest recovery. Our review of the available papers indicates that the number of remote sensing studies conducted on forest BA increased substantially from the early 2000s to reach a peak in the 2011-2020 decade. The dominant studies were using optical sensors (261 cases, 80%) based on Landsat, Sentinel, Terra, Aqua, Sentinel, AVHRR, and SPOT satellites. According to the FAO Global Ecological Zone map, most of the reviewed studies were carried out within subtropical dry forest (21.4%, 92 cases). With regard to tree species discrimination, the studies mainly described pine (37.0%). The NDVI and dNBR were the most frequently used VI compared to other indices. The studies show that there are some challenges, mainly due to spectral mixing and poor validation data quality, in determining BA and BS. The use of multisensor images may improve the accuracy and reduce uncertainty in BA estimations. For instance, combining optical time series at moderate resolutions with radar data provides a good opportunity to improve the overall accuracy of the assessments. There is also a need for global and regional products with higher spatial resolution to provide the most accurate BA estimation. The use of RF, SVM, and MLH classifiers has allowed the detection of forest BA classes with no significant difference among them. During 2000-2020, classification techniques gradually tended to reach higher overall accuracies, specifically when using ground truth and combining data from different reference sources. Consequently, remote sensing is expected to improve methodologies to detect and monitor BA in a fast and cost-effective manner, and thus enhance its role in sustainable forest management. The analysed reference database is available online at [[https://www.mdpi.com/article/10.3390/rs1494714/s1](https://www.mdpi.com/article/10.3390/rs1494714/s1)]([https://www.mdpi.com/article/10.3390/rs1494714/s1](https://www.mdpi.com/article/10.3390/rs1494714/s1)), Table S1: BA supplement. Conceptualization, E.K., O.V., J.S., J.W. and J.C.; data curation, E.K., O.V., J.S. and J.W. ; formal analysis, E.K., J.S., J.W. and X.L. ; funding acquisition, E.K., J.S., J.W. and J.C.; investigation, E.K. and O.V. ; methodology, E.K., O.V., J.S., J.W. and J.C.; project administration, E.K., J.W., J.S. and J.C.; resources, E.K., S.L., X.L., D.D. and Y.W. ; software, O.V. and S.L., Y.W. ; supervision, E.K., J.S. and J.W. ; visualization, O.V., S.L., D.D. and Y.W. ; writing--original draft, E.K., O.V., J.S., J.W. and J.C.; writing--review and editing, E.K., J.S., J.W., X.L. and J.C. All authors have read and agreed to the published version of the manuscript. The reported study was funded by RFBR (grant No 19-55-80010/19), MOST (grant No 2018 YFE0184300), and NRF (grant No 120456) according to the research project No 19-55-80010; National Natural Science Foundation of China (grant No 41961060).