Constrained Spectral Clustering of Individual Trees in Dense Forest Using Terrestrial Laser Scanning Data

The present study introduces an advanced method for 3D segmentation of terrestrial laser scanning data into single tree clusters. It intentionally tackled difficult forest situations with dense and structured tree formations, which inventory practitioners are often faced with. The strongly interlocking tree crowns of different sizes and in different layers characterized the test conditions of close to nature forest plots. Volumetric 3D images of the plots were derived from the original point cloud data. A clustering method with automatically derived priors focused on the segmentation of these images by global optimization. Therefore, each image was segmented as a whole and partitioned into individual tree objects using a combination of state-of-the-art techniques. Multiple steps were combined in a workflow that included a morphological detection of the tree stems, the construction of a similarity graph from the image data, the computation of the eigenspectrum which was weighted with the tree stem priors and the final labelling of the transformed data points in a Markov Random Field framework. The detected trees were verified by number and position which allowed for comparison with other studies. Additionally, for a subset of the data, we provided a detailed verification of the three-dimensional extent of the complete trees. The detection rate by number and position was 97.40% for major trees with a stem diameter at breast height (DBH) 12 cm and 84.62% for regeneration trees with a DBH < 12 cm. The three-dimensional extent of the detected trees resulted in an average producer's accuracy of 93.66% and a user's accuracy of 94.06%. Overall, these numbers confirm the capacity of the method for accurate segmentation of strongly layered and understory trees. Future studies could test the method on wider areas with large scale data and different forest types in order to determine its general transferability.