Automatic Forest Mapping at Individual Tree Levels from Terrestrial Laser Scanning Point Clouds with a Hierarchical Minimum Cut Method

Abstract

Abstract:Laser scanning technology plays an important role in forest inventory, as it enables accurate 3D information capturing in a fast and environmentally-friendly manner. The goal of this study is to develop methods for detecting and discriminating individual trees from TLS point clouds of five plots in a boreal coniferous forest. The proposed hierarchical minimum cut method adopts the detected trunk points that are recognized according to pole like shape segmentation as foreground seed points and other points as background seed points, respectively. It constructs the undirected weighted graph of the foreground and background seed points to deduce a cost function for tree crown point segmentation with the decreasing ranking of tree trunk heights. The intermediate results lead to global optimization segmentation of individual trees in a hierarchical order. Finally, the structure metrics of the detected individual trees are calculated and checked with field observations. Plots with different attributes were selected to verify the proposed method, and the experimental studies show that the proposed method is efficient and robust for extracting individual trees from TLS point clouds in terms of the recall of 90.42%.

Automatic Assessment of Crown Projection Area on Single Trees and Stand-Level, Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning

“…Principally, a rapid and automated measurement of objects in the three-dimensional space and the creation of dense 3D-point clouds at a millimeter-resolution are possible using terrestrial laser scanning (TLS) [14][15][16][17]. Different algorithms for the automatic detection of individual trees in 3D-point clouds have recently been developed and provide detection rates clearly exceeding 90% [10,16,18,19].…”

“…Different algorithms for the automatic detection of individual trees in 3D-point clouds have recently been developed and provide detection rates clearly exceeding 90% [10,16,18,19]. Existing studies on automatic DBH measurement from TLS point-clouds have reported root mean squared deviations (RMSD) between measured and fitted DBH values ranging from 0.7 to 7.0 cm [10,18,[20][21][22].…”

Automatic Mapping of Forest Stands Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning

“…However, those results are based on one single 0.07 ha sample plot, containing only 14 trees (all of them 140 year old beeches), representing a very low tree density of only 200 trees ha −1 . Other studies reported overall accuracies (the metric is called "completeness" in that reference) between 86.9% and 89.0% for dense but homogeneous bamboo stands [45] and detection rates of 90.4% in boreal coniferous forests [26] (the metric is called "recall" in that reference). Comparison of our multi-scan TLS based overall accuracies with measures obtained by single-scan TLS or airborne laser scanning (ALS) may be criticized as unfair, because the latter data are associated with lower measurement intensity.…”

Precise Measurement of Stem Diameter by Simulating the Path of Diameter Tape from Terrestrial Laser Scanning Data

“…Compared with airborne laser scanning and field measurements, TLS can obtain accurate understory information, including a digital record of the three-dimensional structure of forests [3]. Many studies have demonstrated the high potential of TLS in forest inventory, including tree locations [1,[4][5][6], heights [2,3,7], diameters [2,3,6,[8][9][10][11][12][13][14][15], volumes [16][17][18], biomass [17,[19][20][21][22][23] and others forestry parameters [24][25][26][27]. In addition, the influence of scanner parameters on forestry parameter derivation has been studied [18,28].…”

“…In recent years, retrieving forestry parameters from terrestrial laser scanning (TLS) data has become an active area of research [1,2]. As an active remote sensing technology, TLS can provide a fast and efficient tool for obtaining a dense 3D point cloud dataset containing an immense number of three-dimensional points reflected from scanned objects.…”