## 2 Materials and Methods
### Data and Processing  
#### 2.2.1 Pleiades Data and Processing  
The Pleaides system consists of a constellation of two satellites for VHR panchromatic (PA) and multispectral (XS) optical observation of the Earth's surface. Pleiades 1A (PHR 1A) was launched in December 2011, while the second satellite, Pleiades 1B (PHR 1B) was launched in December 2012. Both satellites are operating on the same, sun-synchronous orbit with 98.2\({}^{\circ}\) inclination and an offset of 180\({}^{\circ}\) from each other, which together with their ability to tilt dramatically allows a minimum revisit time of any point on the globe within 24 h. The PA and XS images are acquired simultaneously at a  
Figure 2: The study area in the Khumbu Himal; the red frame shows the acquisition area for the Pleiades data. The investigated glaciers are numbered, with 1 signifying the Ngozumpa glacier, 2 the Khumbu glacier, 3 the Nuptse glacier, 4 the Lhotese Nup glacier, and 5 the Lhotes glacier. Source: GeoEye via ESRI basemap. UTM 45 N.  
ground sampling distance of 0.5 m and 2 m, respectively [32], whereas the multispectral dataset can be pan-sharpened to a resolution of 0.5 m [14; 21]. The Pleiades system is the first of its kind that is capable of acquiring three or more nearly synchronous images of the same area with a stereo angle varying between \(\sim\)6\({}^{\circ}\) and \(\sim\)28\({}^{\circ}\), due to the high agility of the satellites [22]. For this study, Pleiades tri-stereo datasets were available from two acquisitions in the European Alps and three acquisitions in the Khumbu Himal (see Table 1), sampling a period of a year at each site. While the study area in the European Alps is of a size that could be covered with one Pleiades scene, the Khumbu Himal study area needed several images per acquisition. This poses particular challenges to the processing and analysis when different sectors of the study are covered by images from different dates (see Table 1 or Figures 3-5 for the exact acquisition dates), since, with acquisition times that are several weeks or even months apart, some surface changes are to be expected between the dates. Surface conditions, especially snow cover, can also be different within one acquisition period. This is clearly shown in the spring 2015 acquisition (see Figure 3), where the parts acquired on 8 April are still partly snow-covered, while there is no snow in the parts acquired on 20 May. For the spring 2015 and autumn 2015 acquisitions, three images were required to cover the Khumbu Himal study area, while four were needed for the spring 2016 acquisition (see Figure 5). Since the user does not have any influence on the tiling of the images in an acquisition, possible problems stemming from different distribution have to be kept in mind (see Section 4). \begin{table}
\begin{tabular}{c c} \hline \hline
**Acquisition** & **Acquisition Dates** \\ \hline European Alps 2014 & 23 September 2014 \\ European Alps 2015 & 21 September 2015 \\ Khumbu Himal Spring 2015 & 8 April and 30 May 2015 \\ Khumbu Himal Autumn 2015 & 7, 29, and 20 October 2015 \\ Khumbu Himal Spring 2016 & 24 March and 12 April 2016 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Available Pleiades datasets. Figure 3: Overview of the autumn 2015 Pleiades images of the Khumbu Himal study area. UTM 45 N.  
DEM based on the Pleiades images were generated by first using semi-global matching (SGM) [33] within the IMAGINE photogrammetry suite of ERDAS IMAGINE. This results in a dense photogrammetric point cloud. Since the rational polynomial coefficient (RPC) files are delivered with the original scene, orientation and georectification for the Pleiades imagery can be achieved through established and well-documented processing e.g., Haala and Tiris [34; 35]. The RPC file that is provided with the Pleiades data is used to define the initial functions for transforming the sensor geometry to image geometry.