## 5 Discussion  
Timely planning and execution of thinnings are crucial for maintaining a healthy forest, but minimal research has been performed to derive the need for thinning directly from remote sensing data. Compared with detecting the necessity of thinnings, the estimation of inventory data [16; 17; 18] or the classification tree species [12; 13; 14; 15] has received much more attention. Accordingly, this study demonstrates the potential of predicting the necessity of thinning with state-of-the-art deep learning architectures solely from high-altitude remote sensing data. Using multispectral orthophotos, canopy height data (CHM), a digital terrain model (DTM), slope, and the reference data collected in the field by experts, two datasets were created to investigate the research question: can the necessity for thinning be reliably predicted? Using the DeepLabv3+ DCNN architecture for semantic segmentation, we achieved an \(F_{1}\) score of 82.2% and demonstrated that deep learning algorithms can be applied to accurately detect forests in need of thinning. Additionally, by employing the masked dataset to restrict the analysis to exclusively the commercial forest, the approach improved the \(F_{1}\) score to 85.3%. From these results, one can conclude that the DCNN was able to extract critical information about the density of the forest from the remote sensing data and thus to assess the need for thinning. In comparison, [PERSON] [19] used machine learning algorithms on satellite imagery to find forest stands needing thinning within the next 10 years and achieved an overall accuracy of 64.1%. However, their achievements are based on Landsat TM satellite imagery with a lower spatial resolution of 30 m compared with 0.2 m and a standwise classification of forest operations in contrast to semantic segmentation. Thus, the results are hardly comparable with those in this study. Another study from [20] used exclusively ALS data to predict the necessity of thinnings, with an overall accuracy of 79%. There too, the necessity of thinnings was determined through a stand-wise classification of forest operations. The necessity for thinning is determined mainly by two criteria: tree height and the standing density (basal area) of a forest stand, and the tree heights are already part of the input data. Thus, it appears that the model was capable of inferring the crown density and not the actual basal area. Therefore, the crown density is sufficient for the target thinning type, which is crown thinning. For other thinning types, such as low thinning, the DCNN might struggle to produce comparably good results. The model might provide moderate performance since only suppressed and sub-dominant trees are removed in low thinning, which have no impact on the canopy. Besides creating a model that detects forests in need of thinning, we additionally explored predicting the urgency of thinning. In this case, the trained models struggled to distinguish between urgent (51.48%) and not urgent (49.57%) thinning even though we focused just on the commercially utilized forests. (see Table 10). The poorer performance is possibly due to inconsistency in the data and missing crucial information that is not contained in the input data. Consequently, adding additional data such as yield class or age could improve the results. An ablation study explored the importance of the individual input features, showing that orthophotos contain the most critical information for the model that assesses thinning. Adding the CHM further improved the performance of predicting thinning. On the other hand, the input features DTM and slope seem not to contain any additional helpful information. The proposed model can be seen as a cost-efficient and scalable solution for reducing the costs of creating new forest management plans. With this approach, the necessity of sending expensive forestry personnel into the field can be greatly reduced.