## 4 Discussion  
During the last two decades, OBIA has been used for many applications [28; 29]. This research shows the advantage of integrating backscatter information from Sentinel-1 SAR data and spectral information from Sentinel-2 optical data within an expert knowledge-based classification approach in an OBIA environment to assess landslide-induced geomorphological changes. We used Hitardalur as a case study, where a large landslide blocked the river, and, consequently, a lake formed and the river changed its direction. The OBIA classification results show no significant change in the landslide area in the year after the event, but changes in the area of the landslide-dammed lake were detected. However, this might be partly explained by uncertainties in image segmentation and classification and seasonal variations due to different acquisition dates (July 2018 versus August 2019). Moreover, the spatial resolution of Sentinel-1 and Sentinel-2 images are limiting factors for river classification. Therefore, we first identified the existing riverbeds based on an edge detection layer and then assessed whether water was present in the riverbeds. The Hitardalur landslide has several interesting characteristics that make it suitable to be considered as a case study for creating DEMs using Sentinel-1 datasets. It is a large landslide with a deposition area spreading over the rather flat valley. Therefore, both ascending and descending Sentinel-1 datasets can be used. The resolution and accuracy of DEMs have a direct influence on subsequent analyses such as the landform classification and hydrological modeling [86]. We applied a straightforward workflow for generating DEM using Sentinel-1 image pairs from three different tracks. Our results are heterogeneous and indicate that the generation of DEMs has several limitations in this situation. InSAR techniques have been extensively used for DEM generation using SAR datasets, but the accuracy of the resulting DEMs depends on several factors related to the data and processing workflows and techniques. For selecting suitable SAR datasets, the orbit indetermination and B\({}_{\text{perp}}\) range, atmospheric conditions, and temporal decorrelation (due to scene changes between the two passes) need to be considered. To achieve a good vertical accuracy, the SAR image pair requires a large B\({}_{\text{perp}}\)[76]. The Sentinel-1 mission was mainly designed for the retrieval of surface deformations using differential InSAR (DInSAR) which requires a low B\({}_{\text{perp}}\) between consecutive SAR images, and DEM generation was not the primary goal of the mission [58]. The recommended B\({}_{\text{perp}}\) for the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Acquisition Date**} & **Orbit** & **Min** & **Max** & **Mean** & **Std. Dev.** & **RMSE (m)** & **Moran’s I** \\  & **Tracks** & **Elevation (m)** & **Elevation (m)** & **Elevation (m)** & **(m)** & **RMSE (m)** & **Moran’s I** \\ \hline \multicolumn{8}{c}{Arctic DEM} \\ \hline - & & 89 & 743 & 180 & 117 & - & - \\ \hline \multicolumn{8}{c}{Post-event DEMs} \\ \hline
11 July 2018 & & & & & & & \\ and & 16 & 18 & 750 & 176 & 115 & 30 & 0.92 \\
17 July 2018 & & & & & & \\
5 August 2018 & 118 & 78 & 654 & 173 & 100 & 37 & 0.97 \\
11 August 2018 & 155 & 87 & 681 & 192 & 130 & 46 & 0.97 \\
4 July 2019 & & & & & & & \\ and & 155 & 87 & 681 & 192 & 130 & 46 & 0.97 \\
10 July 2019 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation of the Sentinel-1 DEMs with respect to the ArcticDEM. Comparison is based on the area not influenced by the landslide. The [PERSON]’s I statistic is calculated on the difference map. the potential of post-event DEMs generated from Sentinel-1 image pairs from different orbit tracks for the estimation of the Hitardalur landslide volume. It is plausible that we will face more frequent and large landslides in the future due to climate change.