## 3 Materials and Methods
### Materials
#### 3.1.2 Remote Sensing and Ground Truth Data Acquisition  
As shown in Table 1, all data came from three sources and were acquired at three disparate points in time. Ideally, all data should be acquired simultaneously, but we were limited in this study.  
The aerial imagery was acquired by the States of Austria, where one-third of Austria was updated every year. All aerial imagery for the study site was recorded on 11 September 2018 and 12 September 2018 with an UltraCam Eagle Mark 3 431S61680X916102-f100 mounted on a Beechcraft Super King Air B200 D-IWAW. Four bands were acquired with a spatial resolution of 20 cm: three bands for RGB and one band in the near-infrared (NIR) spectrum (Table 1). After acquisition, the raw data were geometrically and radiometrically corrected with the corresponding calibration data of the camera, and the four channels were stitched together using the monolithic stitching method [58]. The second product that was derived from the aerial photos is the canopy height model (CHM). To obtain the CHM, first, the digital surface model (DSM) was calculated using photogrammetry from overlapping air images. Then, the CHM was calculated by subtracting the DSM from the DTM. For this study, the Federal Forest Office (BFW) calculated the CHM.  
Acquisition of the utilized digital terrain model (DTM) was performed via airborne laser scanning (ALS) in 2013 as part of the EU project INTERREG. The slope was calculated from the DTM using the Horn algorithm [59]. The Austrian Federal Forests provided all data.  
Reference data collection was performed by forest engineers from May 2017 until November 2017 and subsequently digitalized by April 2018. This data acquisition was carried out as part of the forest management plan update by the Austrian Federal Forests.  
Figure 1: Study area of Lungau, Austria. The Austrian Federal Forest area is colored in orange and includes commercial and protective forest as well as non-forest areas. Shown in red are the tiles that are used to create the dataset that is used in this paper. The background image is a true-color orthophoto from airborne photography [57] of the region in Austria that was used. The region in the main image is the same as the red square shown in the full map of Austria shown in the upper left.  
Forest engineers assessed every forest stand by measuring the basal area and tree heights to derive the most important key figures such as tree species composition, yield class, and stocking volume. Another part of the assessment was the planning of thinnings that need to be executed to ensure optimal growth of the trees and to maintain healthy forest stands with high-quality wood. At the Austrian Federal Forests, thinnings are planned in three urgency levels, i.e., the forest stand:  
* [noitemsep]
* Urgency 1--needs thinning during the next 0 to 3 years;
* Urgency 2--needs thinning during the next 3 to 10 years;
* Urgency 3--can be thinned after 10 years or can be postponed until the next management plan.  
The terminology 'urgency level' is an internal annotation used by the Austrian Federal Forests as a practical approach to recording the urgency of use by a forest manager. However, just the first two (urgency 1 and urgency 2) are relevant, since by the time urgency 3 would be executed, a new management plan will have been made. Therefore, it is used very rarely and will not be considered in this study. The acquired data were populated into a database (See Table 2), and the spatial information about the forest stands was drawn in a GIS. Subsequently, both data sources were spatially registered.