# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 4 Results
### The Accuracies with Different Methods  
The overall accuracy of the classifications increases only slightly when adding the Sentinel-1 scenes after 1 or 2 scenes after the damage of 12 August (Figure 8) (see also Table 7). The latest date here is 20 August, except for 27 scenes in which all Sentinel-1  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method and the**} & \multicolumn{3}{c}{**OA**} & \multicolumn{3}{c}{**UA by Category**} & \multicolumn{3}{c}{**PA by Category**} \\ \cline{2-9}  & & **1**1** & **2**2**2 & **3**3**3 & **1**1** & **2**2**2 & **3**3**3 \\ \hline SVM 8 scenes & 0.729 & 0.778 & 0.618 & 0.400 & 0.892 & 0.438 & 0.250 \\ SVM 10 scenes & 0.720 & 0.754 & 0.562 & 0.500 & 0.917 & 0.375 & 0.125 \\ SVM 17 scenes & 0.759 & 0.795 & 0.610 & 0.667 & 0.901 & 0.510 & 0.250 \\ SVM 19 scenes & 0.771 & 0.805 & 0.619 & 0.750 & 0.913 & 0.531 & 0.250 \\ SVM 27 scenes & 0.769 & 0.781 & 0.688 & 0.800 & 0.955 & 0.458 & 0.167 \\ SVM 8 scenes, S & 0.735 & 0.792 & 0.535 & 0.500 & 0.884 & 0.469 & 0.208 \\ SVM 10 scenes, S & 0.755 & 0.816 & 0.571 & 0.462 & 0.901 & 0.490 & 0.250 \\ SVM 19 scenes, S & 0.784 & 0.807 & 0.686 & 0.625 & 0.948 & 0.490 & 0.208 \\ SVM 27 scenes, S & 0.788 & 0.832 & 0.683 & 0.500 & 0.919 & 0.571 & 0.292 \\ ik-NN 8 scenes & 0.700 & 0.775 & 0.415 & 0.435 & 0.871 & 0.327 & 0.263 \\ ik-NN 10 scenes & 0.690 & 0.762 & 0.467 & 0.250 & 0.867 & 0.404 & 0.105 \\ Ik-NN 17 scenes & 0.703 & 0.781 & 0.432 & 0.434 & 0.867 & 0.365 & 0.263 \\ ik-NN 19 scenes & 0.630 & 0.747 & 0.286 & 0.133 & 0.814 & 0.308 & 0.053 \\ MLR 8 scenes & 0.703 & 0.742 & 0.435 & 0.583 & 0.917 & 0.208 & 0.292 \\ MLR 10 scenes & 0.712 & 0.763 & 0.464 & 0.533 & 0.904 & 0.271 & 0.333 \\ MLR 17 scenes & 0.686 & 0.771 & 0.407 & 0.348 & 0.879 & 0.229 & 0.333 \\ MLR 19 scenes & 0.657 & 0.794 & 0.326 & 0.296 & 0.808 & 0.286 & 0.333 \\ \hline \hline \end{tabular}  
* Damage, 2 Severe damage, 3 Slight damage.  
\end{table}
Table 7: The overall accuracy (OA), user’s accuracy (UA) and producer’s accuracy (PA) in the validation data using support vector machine (SVM), improved k-NN method (ik-NN) and multinomial logistic regression with four different Sentinel-1 datasets. The latest scene after the damage was from 20 August 2017, except for 27 scenes in which all Sentinel-1 scenes were used. Segmentation-based results are indicated with ‘S’. A volume threshold of 75 m\({}^{3}\)/ha was used for the data.  
Figure 8: The overall accuracy with three different methods, ik-NN, MLR and SVM as a function of the acquisition date of the latest Sentinel-1 scene. The scenes were used until the date in the horizontal axis.  
scenes were used. SVM gave the largest OA and ik-NN slightly larger than MLR. MLR is the only parametric method of the three methods tested. All methods presume the selection of the estimation parameters. The OAs are slightly larger when the backscattering-based features are calculated using segmentation-based \(\sigma^{0}\) instead of stand boundaries-based ones (Table 7, notation 'S'). The proposed segmentation approach (tested only with SVM) increased the OA particularly when the number of the scenes is small (e.g., 10), as well as the PAs for the damage categories with 10 scenes. There are many possible combinations, therefore not all were tested here. The limited improvement can also be attributed to the fact that segmentation used the SAR data only now. A larger accuracy could be obtained when using also the inventory and other auxiliary data, similar to set of features used in the classification method.