## 2 Materials and Methods
### Geomorphological Features Mapping
Then, we created a knowledge-based classification ruleset which includes thresholds based on the backscatter information from Sentinel-1 and the spectral information from the Sentinel-2 images for each year. The size of the segmentation-derived image segments is controlled by the scale parameter (SP), which is directly related to the local variations of the bands used in the multiresolution segmentation algorithm [66, 67]. In general, the higher the value of the SP, the larger the resulting segments, and vice versa. First, multiresolution segmentation was applied at the pixel level to create suitable objects for the subsequent classification of the landslide, using the Sentinel-1 VV intensity change layer, and the landslide-dammed lake, using the Sentinel-1 VV intensity change and NDVI layers. The same features were used for classification in both years, whereby some adaptations in the thresholds for the 2019 mapping were required. The difference in the values of the Sentinel-1 VV polarization change layers between the year 2018 and 2019 could be related to the different time of the Sentinel-1 image acquisitions, and potential changes in the surface structure of the landslide, as well as different dielectric properties (e.g., moisture content) of the landslide. Another multiresolution segmentation was performed based on the unclassified segments from the first segmentation and using only the Sentinel-2 post-event images to create suitable objects for the  
Figure 4: Examples of the layers extracted from the Sentinel-1 and Sentinel-2 datasets. (**a**) and (**b**) show the subtraction layers using pre- and post-event Sentinel-1 VV polarization intensity images for 2018 and 2019, respectively. The Hitardalur landslide and the landslide-dammed lake are distinguishable. (**c**) and (**d**) show the edge extraction layers derived from the post-event Sentinel-2 images (Near-infrared (NIR) band (B8)) for 2018 and 2019, respectively. classification of the riverbeds with water. We excluded the Sentinel-1 datasets from this classification since riverbeds were barely visible in the datasets. Moreover, we classified the original riverbeds with water using only the pre-event Sentinel-2 image (20 June 2018). Therefore, we used the edge extraction layer sigma \(>\)5 followed by a manual refinement, merging, and using a threshold for the object length of \(>\)470 pixels. Figure 5 gives an overview of the OBIA mapping workflow and the features and thresholds which were used for the post-event geomorphological feature mapping for 2018 and 2019. Moreover, we semi-automatically delineated the part of the landslide where the material was deposited in the year 2018 as an input for the subsequent landslide volume estimation. Therefore, we re-segmented the landslide class. Then the slope information derived from the Arctic DEM was used to differentiate the landslide deposition area from the landslide source area with a slope threshold of \(<\)12\({}^{\circ}\). The value of 12\({}^{\circ}\) was selected based on the work of [PERSON] et al. [45], impressions from the field, and considering the visual comparison of the field photographs with the remote sensing data.