# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 4 Discussion
A problem could be that geolocation errors caused by the positive elevation bias of the DSM have a bigger impact at 2 m than at 30 m: residual shifts remain within the same pixel at 30 m but result in wrong pixel collocation at 2 m. Additionally, the high level of detail in crown characterisation led to numerous false layovers and shadow areas, likely caused by excessive height variations that also interfered with terrain correction. This is evident in the map of Figure 5, which shows numerous scattered regions of layover and shadow, likely due to the shadows cast by tree crowns. While using a detailed DSM can be beneficial, as it may capture canopy gaps that influence VHR SAR images, the temporal gap of 3 to 4 years between the LiDAR DSM and the imagery is problematic. Canopy gaps present in the DSM may have since been partially filled, while new gaps could have emerged, given that selective logging is routinely practiced in the study area. This temporal misalignment between the datasets could therefore be one of the reasons for the bad outcomes of this terrain correction. Among the global DEMs, COP is the one that, if used in the SAR image corrections, is resulting in the lowest accuracy in windthrow detection. From Figures 3D and 4D, it emerged that, at least over our study area, COP is the closest to the LiDAR DSM, and on the official documentation of the COP DEM, it is specified that it is a digital surface model that represents the surface of the Earth, including buildings, infrastructure, and vegetation [12]. COP global DEM is based on Tandem-X data: InSAR-based DEMs represent the phase height corresponding to the mean scattering centre, meaning that the true surface elevation is not correctly captured over vegetation. The mean scattering centre height depends on the vegetation, the considered resolution, and the wavelength. Considering a common 30 m pixel spacing for the COP DEM and the SRTM, the only differences between these two DEMs lie in the potential vegetation difference due to the temporal separation between the products and the COSMO SkyMed acquisitions and in the use of the X- and C-band signals, respectively. In [33], InSAR tree heights predicted with Tandem-X data were compared with tree heights generated with LiDAR. The RMSE was between 0.7 and 2.3 m. Many other studies (e.g., [33; 34]) exist on the use of Tandem-X data to retrieve forest canopy, and all of them highlight the fact that the penetration of Tandem-X data in the canopy is dependent also on environmental factors, and it is not always the same. Indeed, the COP DEM over vegetated areas has, in any case, lower values with respect to the LiDAR-derived DSM, so it is not properly correct to call it DSM over vegetation. Tandem-X data also has a very high spatial resolution (less than 3 m) when acquired in stripmap mode, but we expected that in the generation of the COP DEM this detail got lost. ALOS is the global DEM that seems to provide images with the best terrain corrections to be used for windthrow detection. In [35], [PERSON] et al. evaluated the accuracy of some global DEMs and a LiDAR DTM; in particular, they considered all three global DEMs used in this study. From their results, it emerged that, as expected, a LiDAR DTM is the most precise in both barren land and forested areas, while among the global DEMs, ALOS seems the most accurate. On the opposite, COP DEM resulted in the least accurate. It is worth noting that in [35] they used COP DEM at 90 m.  
A factor that should be considered, that is independent from the results, is also the data availability. As this study points out, and also other studies did [32], a local LiDAR DTM is the best option, but it is also the most expensive, as LiDAR data needs to be acquired if not available, and thus is frequently an option that is not viable in many parts of the world. It is important to note that in emergency scenarios, obtaining SAR images with the same orbit and optimal imaging conditions, as utilised in this study, is often challenging.