## 1 Introduction  
Height is one of the most important attributes to characterize forest stands. Besides being indispensable for estimating forest timber volume, it is very helpful for forest structure analyses, classification, mapping and change detection [1]. New satellite sensors and advanced image processing techniques provide data and tools for an improved forest monitoring. The question is: can forest height and timber volume estimation be carried out efficiently using remote sensing data? To answer this question, an extensive understanding about the quality and potential applicability of height information derived from the latest sensors and processing technologies is necessary. Although forest height is currently monitored mainly through forest inventory plots, Digital Surface Models (DSMs) and Canopy Height Models (CHMs) from remote sensing data have recently gained more attention. In general, four main remote sensing techniques are available to acquire three-dimensional data in forests: LiDAR (Light Detection And Ranging), aerial photogrammetry, satellite photogrammetry and Interferometric Synthetic Aperture Radar (InSAR) [2; 3; 4]. In the last 20 years, different airborne LiDAR (also known as airborne laser scanning, ALS) systems have been used to measure canopy height [1; 5; 6; 7; 8; 9; 10]. Full-waveform LiDAR systems provide an insight into forest canopies, delivering even detailed information on the vertical structure of forests [11; 12]. Recent research presented solutions for single tree identification even if crowns are interlaced [13; 14]. Moreover, LiDAR systems are able to penetrate the forest canopy, capturing additional information about the vegetation structure below the top forest layer. Besides LiDAR, photogrammetry uses overlapping images taken from different viewing-angles to derive height information [15]. Aerial photogrammetry is not a new technology, but improvement in forest monitoring applications has been relatively slow due to limits in image quality and image processing capabilities. Height information from LiDAR is more direct and more accurate than data derived from photogrammetry [16], and it is presently considered as reference for forest height determination. However, the fast developments in computer vision methodology in recent years, especially the development of dense image matching algorithms, have largely improved the quality of DSMs generated from photogrammetry, thus boosting the research in aerial photogrammetry for forest structure monitoring [3; 15; 17; 18; 19; 20; 21]. The costs for acquiring aerial imagery are much lower than the costs for LiDAR data [22] and image data acquisitions are performed regularly for all German federal states. Additionally, multispectral aerial photographs allow identifying tree species groups [15; 21]. Satellite photogrammetry has also been assessed for forest attribute estimation [3; 4; 23], since it can be performed more efficiently and at a lower cost with respect to aerial photogrammetry. In addition to a larger field of view, satellite images often provide spectral information in the visible and Near Infrared (NIR) ranges (e.g., WorldView-2 satellite data). In general, two main acquisition types for satellite sensors are available to derive a DSM: across-track and along-track. Across-track stereoscopy captures data from two or more adjacent orbits, which means that changes in surface reflectance between the two orbits can impair the quality of the generated DSM. Therefore, since its introduction by the experimental MOMS-02 system in the early 1990s [24], this technology is now used by the majority of recent satellites, for instance SPOT-5, IKONOS, GeoEye-1/2, WorldView-2/3, QuickBird, ALOS/PRISM, Cartosat-1, and Ziyuan-3 [25]. These satellites obtain stereo imagery from the same orbit from two or more views, either by using multiple cameras or by adjusting the viewing angles [25].