## 6 Discussion
### Accuracy Evaluation of the DSMs  
Forest change detection based on DSMs delivers promising results, but highly depends on the quality of the DSM, making a quality evaluation of the DSMs is necessary. Using field data, a inventory plot area of 500 m\({}^{2}\) is statistically extrapolated to represent areas of 1 ha. In order to get pixel-wise evaluation results, LiDAR data are assumed to provide the highest level of detail and are therefore used as additional reference. The Traunstein forest is one of the best investigated long term forest test sites in Germany. The LiDAR2010 dataset has been used and evaluated already by other publications for height and timber volume estimations [4; 15; 18]. Up to now, LiDAR is considered as the most accurate remote sensing technology for forest height determination. In addition, the LiDAR DSMcovers the complete area of the investigated forest canopy and is better suited with respect to field inventory plots for comparing DSMs derived from other sensors. DSMs derived from the HRSC data (HRSC2003) as well as from aerial photographs (Aerial2012) show fewer details than LiDAR and display a weakness in the gap assessment, most probably due to shade effects. The DSM from very high resolution satellite data (WorldView-2) can almost reach similar accuracies as the aerial data, and shows sharp boundaries of forest regions. Although the DSMs from high resolution optical ALOS/PRISM, Cartosat-1 and RapidEye (~2.5-5 m pixel size), and InSAR (TanDEM-X) satellite data are less accurate than WorldView2012, they can still distinguish high from low level objects (e.g., trees from ground), and show quite accurately the average canopy height of densely forested regions. The quality of the generated DSMs is related to the resolution of the sensors, even though the DSM quality in high density forest region is less sensitive to image resolution than in low-density ones. As already shown, Aerial2012 has the highest accuracy, followed by HRSC2003 and WorldView2012. Based on the latest matching and post-processing techniques, the quality of the DSMs derived from stereo data has greatly improved. Previous studies have compared DSMs to investigate correlations at forest inventory plot level [20; 43]. Because they analyzed different test sites with different forest types, results from these studies cannot be directly compared, but they can be selectively used as references. The correlation coefficients between LiDAR and Aerial2012 calculated within this paper can reach 0.99, much higher than those reported in [20], in which the highest correlation between LiDAR and Photogrammetry-LiDAR(to calculate CHM) was 0.95. The lowest correlation we obtained (from RapidEye2009) is about 0.94. These results may reflect the latest dense matching DSM generation technique. Moreover, different forest types with different structure might also be the reason for other correlation results. In [43], the correlation at the plot level (size 500 m\({}^{2}\)) was analyzed for four different land cover types. As our results do not differentiate tree species, we compare them with the species mixture class, which was 0.64 in [43]. In our study, referring to H100 heights, the DSMs from Cartosat-1 reached a similar correlation value of 0.64. Aerial2012 reaches the highest correlation coefficients when the maximum height was calculated, which was around 0.83. The correlation coefficient between LiDAR2010 and 2008-H100 was 0.79. This result could be explained by the time difference between the DSMs and inventory H100, as the LiDAR2010 and 2008-H100 have two years distance and Aerial2012 and 2013-H100 have only one year time difference. The DSM quality evaluation in forest regions is not as easy as in urban regions, because the measured objects \"trees\" do not have as distinct heights and boundaries as buildings. In addition, taking LiDAR as reference is controversial. Firstly, LiDAR data are also remote sensing data, and thus they still show some discrepancies to field measurements.