## 5 Conclusions  
We performed a co-registration of 133 BWI sampling plots to ALS-derived individual tree detections of a study area in Rhineland-Palatinate, Germany as a preparatory step for a forest characterization on the individual tree level. As erroneous tree pair assignments result in a reduction of model quality (e.g., biomass estimation or tree species classification), we searched for a method for co-registration and tree matching which also rates the reliability of a match. Since existing methods seemed to be unsuitable for this task, we developed a hybrid co-registration and tree matching algorithm. We trained the machine-learning-based method using synthetic individual tree detections and inventory data, whose representativity has been tested empirically. The method reached an overall accuracy of 89.7% for tree matching and a user's accuracy for co-registration of 82.7% using simulated datasets. The method has been applied to the study area, and was able to successfully relocate 60% of the BWI plots, which makes these usable for a further analysis on the individual tree level. As machine-learning methods have been proven powerful to identify patterns, a similar performance of the algorithm compared to a human expert can be expected. Since the interpreter might be biased to the proposed solution, a manual post-processing is not expedient. Figure 11: Effect of the number of detections per hectare on the matching probability using the simulated datasets. The numbers in brackets correspond to the number of plots. The whiskers extend to ten times the interquartile range. Outliers are marked by circles. The derived feature importance depends on forest characteristics and the assumed GNSS error distribution. The feature importance and its effect on the matching probability has shown to be consistent with prior expectations. Thus, the machine-learning approach seems to be an appropriate alternative to heuristic methods, with the additional advantages of an automated parametrization and a robust estimation of the reliability of a single co-registration and tree matching result. We found that with five or more linked tree pairs, at least 90% of the co-registration results can be expected to be correct. If the number of terrestrial trees is limited, heuristic methods typically cannot comply with this requirement. We found that the highest probabilities for a correct co-registration were achieved in heterogeneous stands (mixed species, differing tree heights, presence of tall trees). Stands dominated by conifers achieved significantly better co-registration results than stands dominated by deciduous trees. These findings support the results of previous studies. To achieve better results, the local-maxima-based tree detection method used in this study should be replaced by a more advanced method (e.g., by tree stem detection). Since more accurate tree positions result in a better agreement of the point patterns, the pre-trained algorithm can directly be applied to these more reliable positions without losing explanatory power. To transfer the method to forests with different characteristics or a different sampling design, only an adaption of the (simulated) training datasets is required. The probability estimations provided by the algorithm--as an objective indicator of the reliability of a specific result--lead to a clear added value compared to existing methods. Since it can serve as a weighting-factor for model training, for example, the proposed method is a relevant tool for gaining further knowledge in the field of forest characterization on small scales or even on the individual tree level. The authors wish to thank the state forest service of Rhineland-Palatinate for providing the ALS data and the Thunen Institute for providing the BWI data. The study was embedded in the TriCSS-project (Trier Center for sustainable Systems) which was funded by the research initiative of Rhineland-Palatinate.