The subtropical dry forests of South-America have been impacted by deforestation and degradation over the last two centuries. The forests still provide important ecosystem services but overexploitation of the forested ecosystems may lead to irreversible loss of provision and regulation services. Therefore, this thesis analyzed the potential of 3D vegetation models based on low-cost stereo-photos taken from unpiloted aerial vehicles to map forest degradation in dry forests. The ecosystem of the Dry Chaco in Argentina, which is considered as one of the major hotspots of forest degradation worldwide, was taken as a case study. Therefore, new data on surface and vegetation height were collected during UAV-flights. Flights were realized at 44 locations in contrasting ecological settings of the Dry Chaco. The results of this research show that structure-from-motion algorithms are capable of reconstructing high resolution 3D surface models of the forest cover and this at a much lower cost than LIDAR derived products. Furthermore, this thesis proposes a set of relevant forest attributes that can be derived from these 3D surface models and compares these with indicators from traditional field-based forest inventories. In a next step, the correspondence between the forest structural indicators and degradation states was examined. Finally, the indicators were compared with high resolution laser data from a recently launched spaceborne sensor, which facilitates the assessment of forest degradation patterns at sub-continental scale.