## 6 Discussion
### Change Detection  
The change detection accuracy is another important aspect of DSM accuracy. Forest decrease/growth analysis is still an on-going research topic for both environmental research and forest management. In this paper, height change maps and detected forest change masks were generated and evaluated by a comparison with reference change masks. Change detection in forest regions using optical 2D remote sensing data can be a challenging task, because of the similar spectral and textural features between trees and other vegetation. However, changes in height can provide an efficient and more accurate indication for forest change. Focusing on forest changes, our previous results [34; 50] prove the good performance of the DSMs from satellite images. The quality of these DSMs directly influences the change detection accuracy [34]. Therefore, comparing the change detection results from various sensor combinations helps to select proper data sources and enhance the forest change algorithms.  
By using LiDAR2001 and HRSC2003 as the pre-change DSMs separately, two groups of results are generated. The height change accuracies in Table 7 are much higher when LiDAR2001 data are used as the pre-change DSM, possibly because the change reference is also produced using LiDAR2001 data as the pre-change DSM. When using LiDAR2001 as the pre-change DSM, all results show the correct location and approximate size of the damages. More false detections were introduced when using less accurate DSM, for example the ones derived from RapidEye2009.  
Although the results from using HRSC2003 as the pre-change DSM show some false positives, the locations of the main changes are still highlighted correctly. The comparison confirms that satellite data are well suited to support a fast response for detecting damage from sudden events, such as the investigated storm event Kyrill, because all main changes are detected through the automatic change detection procedure. According to the accuracy requirements and available funding, proper sensor combinations can be selected. Forest changes due to management activities can also be detected through comparing these DSMs [29]. However, these changes are not analyzed in this paper. In addition, more research is required to analyze the relationship between forest density and storm damage.  
In addition to forest damage monitoring, the tree growth was extracted and evaluated. Our results confirm the findings from [37], whose analysis of the same test site was mainly based on aerial images. Forest growth is barely detectable when only spectral information is available. By dividing the height difference with the time distance (measured in years), PAIs were derived as an indicator for the average forest growth per year. In our study, more data sources, especially DSMs from satellites, were tested; these data were acquired over a relatively long time interval. Therefore, it is possible to provide reasonable average tree growth PAIs which are less affected by annual variations of growth. The relationship between tree height and PAIs also matches the results from [37], in which shorter trees, which are assumed to be younger, grow faster than taller trees. Besides tree age, tree species, environment [37; 51] and health conditions might have influenced the tree growth. For mixed temperate forests an approximate mean annual growth rate of 0.30-0.40 m can be expected over a 100-year period [37]. With WorldView-2 data and aerial datasets, as shown in Figure 12, the main tree height in this forest is 25-30 m and the results for the PAIs are in the range of 0.30-0.50 m. The change detection is performed based on the assumption that the pre-event LiDAR data are available, which is usually true for most parts of Europe.