## 6 Conclusions  
Our analysis demonstrate that DEM based on Pleiades imagery are well-suited for the analysis of surface elevation changes on mountain glaciers, and especially offer the possibility for regional high spatio-temporal resolution assessments of remote areas for which no field data is available. As demonstrated, the relative accuracy is sufficient to investigate glacier surface elevation changes, even of magnitudes below 1 m, as experienced in the Khumbu Himal, and can therefore be applied over relatively short periods of time, such as those required for annual assessments.  
However, some specific problems have to be mentioned and kept in mind while ordering, processing, and analyzing the data. Especially steep areas remain problematic: the noise levels are higher, and gaps occur more frequently, especially if the gradient of the terrain exceeds 40\({}^{\circ}\). For example, the DEM shows lots of gaps in the very steep south face of Nuptse or in the highest parts of Ngozumpa Glacier. The problem is compounded if steep areas are also affected by shadow, even though Pleiades offers a high image depth of 12 bit. This poses a particular problem for the study of geomorphological processes occurring on this steep terrain, such as the case of glacier accumulation via avalanches from steep headwalls. Fresh snow cover also poses a challenge for the DEM calculation, which is shown in the problems at the Ngozumpa glacier (see Section 2.2.1).  
As no direct information on the mass can be gained from the remote sensing data, the conversion from volume to mass changes that is required for the calculation of geodetic glacier mass balances requires additional information. Especially for glaciers in areas where changing firm bodies can have a significant influence on the mass balance, as in the European Alps, or where the thickness and density of the firm bodies are unknown, as in the Khumbu Himal. Optical information can be used to determine the distribution of ice, snow, and less reliably, firm on the glacier surface during the time of acquisition, which can help with the determination of the spatial distribution of the increase or decrease of mass. However, ultimately assumptions, especially for the density of snow, are necessary to calculate glacier mass change from the volume change determined from the DEM differencing. Surface classifications of glaciers based on the high quality, multispectral Pleiades optical imagery can determine the debris cover or monitor snow and firm extent. This can be helpful to convert volume change to mass change, especially in areas where glaciers have changing firm bodies, and to extract surface objects, in order to map and monitor features such as proglacial lakes, ice faces, and avalanche cones that would help refine the geodetic mass balance calculations offered by the Pleiades workflow.  
[PERSON], [PERSON] and [PERSON] conceived and designed the presented study. [PERSON] and [PERSON] processed the Pleiades and ALS data. [PERSON] contributed to the generation of the DEM, mainly through the filtering of the raw point clouds. [PERSON] and [PERSON] analyzed the results, [PERSON] wrote the paper.  
This research has been funded by the FFG--Osterreichische Forschungsforderungsgesellschaft under the asap--Austrian Space Applications Programme (project-nr. 847999), the Austrian Science Fund (grant V309) also contributed to the funding.  
We would like to thank three anonymous reviewers for their comments and suggestions which helped us to significantly improve the paper.  
The authors declare no conflict of interest, the founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.