# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### Classification Algorithms
#### 4.3.3 Classification Accuracy  
Several metrics such as the Kappa coefficient, overall accuracy, and omission and commission errors are typically used with the confusion matrix for burned area classification performance evaluation. A total of 151 studied papers used the error matrix method, regression analysis, and modelling for validation of BA estimation results. Table 3 summarises the distribution of the reference data type and validation methods used in these studies. Out of 151 papers, 49 (32%) used ground truth data (field survey, composite burn index, GPS points, and forest inventory sample plots) for reference purposes. From these, 20 papers provided Kappa coefficients and overall accuracy together with the error matrix results. The second group of 31 studies was based on the reference data retrieved from high resolution imagery (e.g., Google Earth, WorldView, aerial photo). Seventeen studies of this group used the coefficient of determination (R\({}^{2}\)) with/or root mean square error (RMSE) value to describe the possible prediction accuracy of the efficient models. Thirty-seven studies used ancillary data produced by the government authorities (e.g., forest inventory maps, land use/land cover, official data of statistical agencies). In another thirty-four studies, the validation and assessment of forest BA were based on the combination of the above-mentioned reference data. For example, some studies used ground truth data and high-resolution images or ground truth data with local maps. The results in Table 3 indicate that though during the estimated 20 years the metrics of confusion matrixes were still in high demand among researchers, regression analysis and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference Data** & \begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} & \begin{tabular}{c} **Omission** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Commission** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **R\({}^{2}\)/RMSE** \\ \end{tabular} &
\begin{tabular}{c} **Number** \\ **of Studies** \\ \end{tabular} \\ \hline Ground truth data (field survey, CBI, GPS point) & 20 & 17 & 17 & 20 & 10 & 49 \\ Images (Google Earth, high resolution, aerial photo) & 9 & 8 & 8 & 10 & 17 & 31 \\ Ancillary data (forest inventory, forest BA maps, Land use land cover) & 10 & 9 & 8 & 9 & 16 & 37 \\ Combination of above data & 10 & 8 & 8 & 9 & 18 & 34 \\ Total number of studies & 49 & 41 & 49 & 48 & 61 & 151 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distribution of the reference data type and validation methods with the respective number of studies. modelling (R\({}^{2}\)/RMSE) were the most used methods in accuracy estimation and mapping of the forest BA. For a comprehensive analysis of the RS-based forest BA studies, the reported overall accuracies were measured and compared with several factors, such as the year of publication, classification method, respective sensor, and the reference data considered in the validation of classification results. The classification accuracy of estimates for forest BA varies significantly among the assessed studies in all four groups of factors, especially with respect to the classification accuracy of different sensors and reference data (Figure 9). From the box and whisker plots on Figure 9a, it can be seen that throughout 2000-2020, the median of overall accuracies were gradually improving. In 2000-2005, the median overall accuracies of BA classifications were about 85%, while in 2016-2020, the median increased sufficiently up to 94%. This positive tendency is likely a result of improved classification methods used in the evaluated studies, including novel classification techniques, advanced sensors, and the high quality of reference data. The most accurate results of the forest BA mapping were obtained using RF, SVM, and MLH classifiers (OA about 90%), followed by OBIA, ANN, and an unsupervised group of classifiers (OA about 88%).