1. Introduction
Both natural and planted forests are essential to the pursuit of harmony between man and nature. Therefore, they are worthy of attention and research, including tree counting, shape analysis, and the extracting of structural characteristics. In recent years, laser scanning has become a convenient technology for obtaining data on forests. Although the data consist of a large number of points, named point cloud, in three-dimensional (3D) space and those points do not directly contain adjacency information, all points have accurate 3D position information, which is helpful for forest analysis.
Point cloud segmentation, for example, tree recognition and crown extraction, is the basis of forest analysis. Forest point cloud has become a hot topic in intelligent biological data processing in recent decades. Based on accurate segmentation, each tree crown is obtained and can be reconstructed into a 3D digital geometric model. This geometrical model can be used to extract and analyze geometric parameters of the tree and as the elements of virtual reality scenes (3D animation and movie, etc.).
Regarding forest point cloud segmentation, existing representative methods related to our main contributions can be classified into two categories: individual tree identification and tree crown shape extraction.
Individual tree identification is an important research topic for supporting the collection of automatic field inventory using Light Detection and Ranging (LiDAR) technology [
1]. Common methods or ideas include layer stacking, mean shift or region growth, bottom-up or up-down, clustering, and characteristic analysis based on differential quantity.
The layer stacking method for forest point cloud segmentation is used to slice the forest point cloud at fixed (0.5 m, for example) height intervals and detect trees in each layer, merging the results from all layers to build all tree profiles [
2]. Similar to layer stacking, Hamraz H et al. [
3] proposed a tree segmentation method for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model-based tree segmentation method.
Some of the literature adopted mean shift or region growth methods to segment forest point clouds. Wen X et al. [
4] completed an experimental assessment of the mean shift algorithm for the segmentation of airborne LiDAR data. Ma Z et al. [
5] presented a two-stage method to detect individual trees from LiDAR data and adopted a region-growing algorithm to complete the initial segmentation. Liu Q et al. [
6] presented a trunk-growth method with normal vector directions for single tree point cloud segmentation and applied this method to the primeval forest scenes. Wang D et al. [
7] proposed an automatic data-driven approach to extract individual trees from a large-area terrestrial point cloud based on the point cloud graph by pathfinding.
The bottom-up or up-down methods are explored for forest point cloud segmentation. Lu X et al. [
8] built a bottom-up algorithm using point cloud data’s intensity and 3D structure to segment deciduous forests.
In addition, hybrid clustering techniques are also used for forest segmentation. Chen Q et al. [
9] proposed a hybrid clustering technique by combining DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and K-means for segmenting individual trees from airborne LiDAR point cloud data. For terrestrial backpack LiDAR data, Cebral L et al. [
10] used an individual tree extraction method based on DBSCAN clustering and cylinder voxelization of the volume. To avoid under- and over-segmentation effects, Dersch et al. [
1] presented an integrated single tree segmentation using a graph-cut clustering method that is supported by automatic stem detection. Sebastian D et al. [
11] proposed a method for individual tree stem detection using a graph-cut clustering method.
A forest point cloud can also be segmented by employing characteristic analysis based on differential quantity, including normal vector and principal curvature. For example, given that the directions of the normal vector of the trunk points are in general consistent, trunks in a terrestrial scanning forest point cloud can be detected and then the individual trees can be isolated [
12].
The second category of forest point cloud segmentation mainly emphasizes tree crown segmentation or tree extraction through tree crown segmentation.
Some methods emphasize using a priori knowledge, density analysis, clustering, and region growing. To obtain the accurate tree crown model with a complex structure from airborne LiDAR data for latter feature extraction, the method of Tang et al. [
13] consists of several phases: filtering, transformation to a grayscale image, contrast enhancement, the opening and closing operation, and the watershed segmentation. Wang P et al. [
14] completed the initial canopy segmentation using a normalized cut segmentation with a priori knowledge about the position of each tree. Given that closely located and intersected trees are often clustered together as multi-tree components, Xiao W et al. [
15] suggested a tree-shaped model-based continuously adaptive mean shift algorithm. Minaík et al. [
16] presented an algorithm for individual tree crown delineation that uses the excess green index, the marker-controlled watershed segmentation, the region growing algorithms, and a buffer around a treetop. Sun H et al. [
17] adopted a point cloud density model, a local maximum algorithm with optimal window size, to improve the watershed algorithm for extracting the tree crowns. Shahzad M et al. [
18] adopted the unsupervised mean shift clustering to segment a forest point cloud and then used the 3D ellipsoid model to fit the points of each cluster. By this way, the geometrical tree parameter’s location, height, and crown radius are extracted. Dong Z [
19] built a multi-layered tree extraction method using a graph-based segmentation algorithm for segmenting the canopy and the sliding window detection method for other parts of understory trees.
Other literature put more attention to the geometry shape or topology information of the tree crown. Novotn J [
20] proposed an approach to the tree crown segmentation process that combines the seeded region growing with an active contour to approximate a crown boundary. For correcting deviations caused by topography based on individual tree crown segmentation, Duan Z et al. [
21] took into account the weight with normalized canopy height and the precise Digital Elevation Model (DEM) derived from the point cloud that is classified by a multi-scale curvature classification algorithm. For airborne LiDAR data, Strimbu V et al. [
22] proposed a segmentation method that captures the forest’s topological structure in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This bottom-up segmentation strategy is based on several quantifiable cohesion criteria that measure belief on two crown components.
In addition, a study found that combining airborne laser scanning with multiple and hyperspectral data could improve the accuracy of tree crown segmentation [
23].
These representative methods are very diverse in tree crown reconstruction, a critical step of tree reconstruction [
24]. Pyysalo U [
25] used the obtained vector model to extract features and reconstruct single tree crowns from laser scanner data. Chao Z et al. [
26] proposed an approach for tree crown reconstruction based on improving alpha-shape modeling, where the data are points unevenly distributed in a volume rather than on a surface only. Hyyppa [
27] presented an attempt at combining the mobile mapping mode and a multi-echo-recording laser scanner, as well as a new methodology based on the resulting single-scan point clouds, for enhancing the integrity of individual tree crown reconstruction. To address the challenge from leaf/branch occlusions, mirroring the half-crowns facing the Mobile Laser Scanning (MLS) system to the other sides can be assumed as a solution strategy. Kato A et al. [
28] employed an implicit surface to reconstruct the exact shape of an irregular tree crown of various tree species based on the LiDAR point cloud and visualize their actual crown formation in three-dimensional space.
The existing published literature indicates that the accuracy of tree counting and DBH (diameter at breast height) measuring from forest point cloud data is relatively high, which can meet the practical needs of the current forest inventory. However, the research progress of tree crown reconstruction is relatively small in recent years. Most of the existing tree crown segmentation methods are hard segmentation, and the division between overlapping trees is directly cut by a vertical plane, resulting in a plane shape of the segmentation area, which does not like the natural shape of the tree crown. In addition, the accuracy of crown point cloud segmentation affects the reconstruction effect of the tree crown shape. Therefore, for obtaining the realistic canopy silhouette shape, a soft segmentation algorithm of the forest point cloud is proposed in this study. The technical details are described in the next section.