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

Abstract

Abstract: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.

Retrieval of Forest Structural Parameters from Terrestrial Laser Scanning: A Romanian Case Study

“…Multiple studies have focused on the detection of biophysical parameters at both stand and individual scale, with major findings. Heinzel and Huber [16] reached close to 100% identification of stems position by focusing on close range segmentation; similar results are reiterated in several other papers [17] [14]. With similarly good results, studies on biophysical parameters estimation covered both diameter [18] [19][20] and heights evaluation [21] [22].…”

“…The increase in the proportion of identified stems between single and multiple scans (10%) observed by Xi et al- [15] was confirmed but with higher values (16 to 29%). Literature provide overall detection percentages to exceed 97% [16,17], with a slight decrease for smaller diameters. Although depended on the structure and number of scans [24], the~10% decrease is similar to our results.…”

Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots

“…For multiple scan mode, the literature describes various scanner location patterns. In principle, the following setup patterns are distinguished: (i) the scanner positions are located at the edges of the sample plot [12,[42][43][44][45][46], (ii) the scanner positions are outside the sample plot [13,26,46,47], or (iii) one scanner position is in the center and the rest are more or less evenly distributed around it [6,39,46,[48][49][50][51][52][53][54]. Abegg et al [46] showed, in a comprehensive simulation study on the impact of scanner location on occlusion, that an intuitive distribution of scanner positions within the sample point with similar distances between the positions and edges of the sample plot ensures the best overall visibility of the stand.…”

“…Major findings of selected recent studies are described as follows. Heinzel and Huber [53] were able to detect 97.4% of tree positions with a diameter at breast height (dbh) greater or equal to 12 cm with five scanner positions on plots with 7.98 m radius. Further, 97.3% of tree positions were detected for dbhs greater or equal to 36 cm on plots with a 12.62 m radius.…”

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

“…As a result of these limitations, most tree detection algorithms have been applied and tested on similar forest types with little exploration of how the algorithms generalize to other natural settings. Therefore, despite the intense work in airborne tree detection over the last decade (Coomes et al, 2017; Heinzel and Huber, 2018; Jakubowski et al, 2013; Li et al, 2012; Williams et al, 2019), there remains no clear consensus on best practices (Aubry-Kientz et al, 2019).…”