# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 3 Results

Table 4: Percentage of pixels detected as layover/shadows inside the two reference sites for each image, varying the DEM and the pixel spacing. A = ascending image; D = descending image. Figure 3: (**A**) Difference images at 2 m pixel spacing between the local LiDAR DSM, the three global DEMs, and the local LiDAR DTM; (**B**) a zoom over a flat area (cropland); (**C**) zoom over a forest area; and (**D**) two vertical profiles of the five DEMs at 2 m pixel spacing. Figure 4: (**A**) Difference images at 30 m pixel spacing between the local LiDAR DSM, the three global DEMs, and the local LiDAR DTM; (**B**) a zoom over a flat area (cropland); (**C**) zoom over a forest area; and (**D**) two vertical profiles of the five DEMs at 30 m pixel spacing. In Table 5, we present the results of windthrow detection with varying DEMs used for SAR image correction. We considered only the areas not detected as layovers/shadows for all DEMs analyzed. These results were obtained by calculating combined accuracies over the two reference areas. It is evident that the highest accuracies were achieved with images terrain-corrected with the LiDAR-derived DTM at 2 m pixel spacing (kappa accuracy around 0.53), followed by the DTM at 30 m pixel spacing (kappa accuracy around 0.48). The lowest accuracy (kappa around 0.2) was obtained by images terrain-corrected with the LiDAR-derived DSM at 2 m pixel spacing and COP DEM at 2 m pixel spacing. ALOS, COP, and SRTM at 30 m provided quite similar accuracies, ranging from values of kappa accuracy around 0.45 for ALOS and 0.41 for COP. Upon examination of the detection maps of the study areas shown in Figure 5, the results of Table 5 are confirmed. Additionally, it is quite clear that the use of the LiDAR-based DSM at 2 m pixel spacing creates a lot of scattered shadow areas due to the very detailed information of the tree crown shapes. ALOS, COP, and SRTM are also generating more false alarms compared to the LiDAR-based DTM (at both 2 and 30 m pixel spacing). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**DEM**} & **Pixel** & \multirow{2}{*}{\
\begin{tabular}{c} **Kappa** \\ **Spacing (m)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} } & \multicolumn{2}{c}{**Producer’s Accuracies (\%)**} & \multicolumn{2}{c}{**User’s Accuracies (\%)**} \\ \cline{3-8}  & & & **NW** & **W** & **NW** & **W** \\ \hline \multirow{3}{*}{DTM} & 2 & 0.532 & 82.3 & 84.6 & 79.9 & 95.3 & 52.0 \\ \cline{2-8}  & 30 & 0.489 & 80.1 & 83.0 & 77.1 & 94.6 & 48.7 \\ \hline \multirow{3}{*}{DSM} & 2 & 0.267 & 70.3 & 67.1 & 73.4 & 92.4 & 31.8 \\ \cline{2-8}  & 30 & 0.473 & 79.1 & 82.6 & 75.6 & 94.2 & 47.7 \\ \hline \multirow{3}{*}{ALOS} & 2 & 0.436 & 77.6 & 80.6 & 74.6 & 93.8 & 44.6 \\ \cline{2-8}  & 30 & 0.45 & 78.2 & 81.4 & 75.0 & 94.0 & 45.7 \\ \hline \multirow{3}{*}{COP} & 2 & 0.273 & 70.6 & 67.6 & 73.5 & 92.4 & 32.2 \\ \cline{2-8}  & 30 & 0.419 & 76.7 & 80.1 & 73.3 & 93.5 & 43.5 \\ \hline \multirow{3}{*}{SRTM} & 2 & 0.383 & 75.2 & 77.5 & 72.9 & 93.2 & 40.5 \\ \cline{2-8}  & 30 & 0.437 & 77.3 & 81.2 & 73.4 & 93.6 & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Windthrow detection results in terms of kappa accuracy, balanced accuracy, producer’s accuracy, and user’s accuracy.