###### Abstract  
Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hitardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hitara river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that--without further post-processing--the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery. Keywords:object-based image analysis; Sentinel-1; Sentinel-2; digital elevation model; InSAR; landslide; landslide-dammed lake; river; Iceland +
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vehicles (UAVs) [15; 16] has been used for landslide mapping and analysis [15; 17]. However, mapping landslides using only optical imagery is challenging because of the spectral, spatial, and temporal characteristics of landslides [18]. SAR imagery has the potential to be used for hazard assessments by providing large-scale two-dimensional high spatial and temporal resolution images of the Earth's surface [19]. Radar pulses can penetrate through clouds (nearly weather independent), and they can provide data during the day and night (sun independent) [20]. According to [PERSON] and [PERSON] [19], the surface reflectivity measured by radar imagery (also known as the radar backscatter coefficient \(\sigma^{0}\)) is a function of the radar system parameters (such as the frequency, polarization, incident angle) and the surface parameters (such as the topography, roughness, dielectric properties of the medium, moisture). These parameters can be used to extract features from SAR imagery. Moreover, different polarizations provide different information about features of interest on the ground, and depending on the structure and position of the feature of interest, they might appear differently with varying polarizations [21]. The combined interpretation of optical and SAR imagery provides promising opportunities for landslide investigation while existing approaches still need to be improved [22; 23; 24; 25]. Several studies have shown that object-based image analysis (OBIA) can achieve better classification accuracies than pixel-based techniques [26; 27]. OBIA has been used for more than two decades as a framework for feature extraction from different imagery [28; 29]. OBIA allows the use of spectral, textural and spatial information and thus provides a suitable framework for landslide mapping [30]. The integration of optical and SAR imagery within an OBIA approach for mapping geomorphological features is promising since the properties of both data types can be exploited in one interlinked workflow [24]. The estimation of landslide volumes is another key parameter for hazard analysis. There are several methods for estimating the volume of a landslide, of which the use of pre- and post-event digital elevation models (DEMs) can provide a good estimation [31]. However, accurate post-event DEMs are rarely freely available. Thus, the straightforward generation of post-event DEMs based on freely available data is important. There are several techniques to create DEMs, such as using optical imagery and stereoscopy or using SAR imagery and interferometric SAR (InSAR) techniques.