# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 3 Results  
As a first analysis, we visually compared the five DEMs at 2 and 30 m: we subtracted from four DEMs (i.e., DSM, ALOS, COP, and SRTM) the DTM obtained using LiDAR data (that we consider to be the most precise one). We considered the entire study area (Figures 3A and 4A), a small, almost flat area (Figures 3B and 4B), a small forest area (Figures 3C and 4C), and two transects (Figures 3D and 4D). As it could be seen in Figure 3A-C), subtracting the LiDAR DTM from the LiDAR DSM at 2 m pixel spacing results in the canopy height model where the crowns are quite visible (Figure 3C). When working at 30 m pixel spacing (Figure 4A), the crowns are no more visible, and the elevation value in the pixels is influenced by both the presence of the trees and by the gaps in the canopy. Subtracting the three satellite-based DEMs, the DTM generated large areas with high elevation differences (from \(-\)73 to +232 m). These differences are partially related to the tree's coverage and partially to the topography of the area. The topographic differences are clearly highlighting small valleys and microtopography related to water catchments, or very steep areas. These are the areas where we have the highest differences between the LiDAR-based DTM and the three global DEMs. In the difference images for the three global DEMs, a kind of tiling pattern is also visible; this could be related to the fact that the five DEMs were not mutually calibrated among each other, and it could also be an issue present in the ALS-derived DTM and DSM. In Figures 3D and 4D, a comparison among the five DEMs over two transects (one South to North and one West to East) is also shown. First, the canopy of the forest is very clearly highlighted in the LiDAR-derived DSM at 2 m pixel spacing (red line in Figure 3D), while moving at 30 m pixel spacing, the LiDAR DSM becomes more like the DTM. The DTM at 2 m pixel spacing is also describing much better the microtopography that gets lost at 30 m. Additionally, it is visible that the three global DEMs are neither DTMs nor DSMs, and they are losing the microtopography due to their lower pixel spacing. The three global DEMs are not much different at 2 m and 30 m pixel spacing, as the upsampling was done with an interpolation and not through a pansharpening process. From these graphs (Figures 3D and 4D), it seems also that the COP DEM (blue line) is the closest to the LiDAR DSM among the three global DEMs considered, while ALOS (green line) seems closer to the LiDAR DTM (black line). In Table 4, we present the percentage of pixels flagged as layovers/shadows within the two reference areas. The data are organised by image. Notably, there is a difference between the ascending and descending acquisitions, probably related to the topography of the two areas: while for the ascending images the percentage of layover/shadow pixels is around 36%, for the descending acquisitions it is much lower (around 8%). The only value that is not in line with the others is the one obtained using the LiDAR-based DSM at 2 m pixel spacing, where, for the descending images, the percentage of layover/shadow areas is much higher (about 22%). \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**DEM**} & **Pixel** & **9 August** & **16 August** & **3 August** & **23 August** \\  & **Spacing (m)** & **2018 (D)** & **2018 (A)** & **2019 (A)** & **2019 (D)** \\ \hline \multirow{3}{*}{DTM} & 2 & 7.6 & 37.1 & 37.1 & 7.6 \\ \cline{2-6}  & 30 & 8.9 & 36.3 & 36.3 & 8.9 \\ \hline \multirow{3}{*}{DSM} & 2 & 22.3 & 33.8 & 33.7 & 22.4 \\ \cline{2-6}  & 30 & 9.5 & 35.8 & 35.8 & 9.4 \\ \hline \multirow{3}{*}{ALOS} & 2 & 7.2 & 36.5 & 36.6 & 7.1 \\ \cline{2-6}  & 30 & 8.9 & 36.1 & 36.1 & 8.9 \\ \hline \multirow{3}{*}{COP} & 2 & 6.8 & 36.2 & 36.3 & 6.8 \\ \cline{2-6}  & 30 & 8.3 & 36.0 & 36.0 & 8.3 \\ \hline \multirow{3}{*}{SRTM} & 2 & 6.6 & 36.2 & 36.2 & 6.6 \\ \cline{2-6}  & 30 & 7.7 & 36.0 & 36.0 & 7.7 \\ \hline \end{tabular}
\end{table}