# [PERSON] Glacier Status Analysis Based on Historical Data and Moving Detection Using RPAS
## 6 Data Processing
### RPAS Data Processing
#### 6.1.3 Analyzing the Glacier Flow Speed  
CloudCompare software calculates the distance between two similar point clouds in the direction of normals; in this case, the solution cannot be successful due to irregular shifts of the glacier.  
Here we do not have two almost identical point clouds, between which we measure the differences in the perpendicular direction to the surfaces, meaning in direction of normals. Here is the original point cloud and a new point cloud, which was created by irregularly differentially moving the first point cloud. Measuring the movement of the flow and its direction is thus much more complicated. It is necessary to define a point in the original point cloud and, according to its unique surroundings, try to find this point in the second shifted point cloud. If we succeed, we have the direction and magnitude of the shift. These parameters will be different for each point. The M3C2 (Model-to-Model Cloud Comparison) distance in a special module in CloudCompare software solves this problem.  
The next part was the analysis of the joined models and the search for real shifts in time as described above. A comparison of changes was performed using the Model-to-Model Cloud Comparison (M3C2) method [16]. This is a method of calculating the distance or discrepancies between two clouds generally, and not in the classical direction of normals. The method calculates distances only for so-called core points, which is the selection of a smaller sample of points from the whole reference point cloud. The points are usually selected based on the minimum distance they must contain. In the first step, the normal vectors for each point are calculated. All points located at the maximum spherical distance (\(s\)) specified by the user from the core points are used to calculate the normals. If the clouds contain normal vectors from post processing, these normals can be used.  
For each core point, a normal vector is calculated, which is the average of the normals of all points that lie from the core point with the maximum distances. This vector forms the axis of the cylinder. In the next step, a cylinder with a user-defined height (\(h\)) and radius (\(r\)) is interposed by a cloud. The core point forms the center of this cylinder. The position of a point is calculated as the average of all points that are in this cylinder. A more detailed description of the method can be found in [PERSON] et al. (2013) [17; 18; 19], (Figures 13 and 14).  
Figure 14: Areas of significant change on the Knud Rasmussen Glacier in the time span of four hours. The values: the flow speed; the purple colored parts on the left are caused by missing data from flight No.1.  
Figure 13: Histogram of the Model-to-Model Cloud Comparison (M3C2) distance. Negative values are errors from the computation occur on the borders only, caused by missing marginal data in both files. These data were filtered out.  
The results of the analysis show that the glacier moves fastest in the middle with the speed of 2-3 m in a time span of four hours. The histogram (Figure 13) shows that the mean speed of the glacier is one meter per four hours. From the results it can be concluded that with an error of 0.67 m the glacier Knud Rasmussen moves at an average speed of around six m per day. The fastest parts can have a speed of 12-15 m per day, while the border parts move practically by this method immeasurably in the short time span (Figure 14). This agrees with other studies [10; 20; 21; 22].