## 7 Conclusions  
Detecting and predicting stand productivity is fundamental to forest monitoring. Besides, LiDAR high spectral and spatial resolution stereo imaging systems from both space-borne and airborne sensors are capable of providing height information. Therefore, adopting remote sensing data in forest monitoring can be a good choice, especially for frequent forest monitoring purposes. As stated in [20], the DSM quality could be further improved with upgraded image acquisition sensors and stereo-matching techniques, which are now available through new camera systems and computer vision approaches [17; 25; 33; 34; 48]. Therefore, a systematic investigation of DSMs derived from the different currently available sensors is performed in this research to show their potential usability in change detection in detail. A novel approach of this paper was not only forest decrease change and forest growth monitoring, but also a quality evaluation of DSMs from nine different sensors. As an overall conclusion, the data can be categorized in three sets according to their accuracy. The first set includes the DSMs from LiDAR and stereo imagery with less than 1 m resolution derived from airborne and WorldView-2 data. These are the most suitable images for a detailed monitoring of forest structure and height properties. With these data, even medium size gaps (5-10 m diameter) can be analyzed and small changes in the mean stand to single tree height detected. These data might be a good replacement of LiDAR for regular forest surveying tasks. The second set is the 2.5 m resolution Cartosat-1 and TanDEM-X data, where the quality is degraded but still sufficient for estimating the main changes, also in smaller areas. TanDEM-X has its strength in its large scale coverage, its operation independent of cloud cover, and its capability of delivering worldwide monitoring results. Data in the third set are ALOS/PRISM and RapidEye. The resolution and stereo capability influence the quality of DSMs derived from Rapid Eye, which is significantly lower than DSMs derived from other sensors. This is also caused by the cross-track instead of along-track capturing of stereo pairs, meaning that the time difference between stereo pair acquisition is at least several days. The cross-track RapidEye data are not the first choice for DSM generation, but based on the latest DSM generation approach they are able to represent roughly the forest canopy height and locate the main change regions. The ALOS/PRISM sensor has stopped acquiring data since 21 April 2011, and its valuable historical data would be used as pre-change event DSM. With the available DSMs from the pre- and post-change event, forest change can be highlighted. Problems may arise if forests are mixed with other land covers, like buildings. In this case spectral information will be needed to highlight the tree covered areas as a preprocessing step. We conclude that satellite data have a strong potential for future forest height and damage (change) monitoring, especially since more and more sensors providing DSMs are and will be in orbit within the next years, making the rapid response to forest calamities through automatic change detection using satellite data feasible. In combination with information from very high to high resolution multi-seasonal multispectral data, an annual update of forest data bases, capable to support regular management and create the basis for fast response needs seems possible [41]. After large windthrow events, space-borne stereo data could provide, in a short time, a first order evaluation of the affected forest area and even allow a rough estimate of the damaged timber volume. We wish to thank the forest management of Traunsteiner Stadtwald for reference data provision. A special thank goes to [PERSON] and [PERSON], foresters of the Traunsteiner Stadtwald, for their valuable support and discussion. Funding: The research was partially supported by the Space Directorate of the German Aerospace Centre (50 EE0919) and the RESA archive providing RapidEye data (317).