1. Introduction
Forests are essential in preserving the biodiversity and ecological balance in terrestrial ecosystems, serving as a source of fundamental materials for human life and production [
1,
2,
3]. Conducting forest resource surveys is crucial for sustainable development. It not only establishes a foundational dataset for forestry scientific research but also provides crucial evidence for the management and conservation of ecological environments [
4,
5,
6]. Traditional forest inventories rely on field surveys, typically facilitated by measuring tools to acquire forest structural features, including altimeters for tree height, total stations for position [
7], calipers for DBH and so on. Although this method can obtain detailed and accurate information on individual trees, it is time-consuming, labor-intensive and difficult to apply in large areas [
8,
9]. Traditional remote sensing technologies acquire optical images of forests (hyperspectral, multispectral and radar images, etc.) by satellite or aerial photography, and these images are interpreted to extract information about individual trees [
10]. This method can effectively provide forest information over large areas, but two-dimensional (2D) images lack information from below the canopy and hence fail to fully exhibit the three-dimensional (3D) structural features of forests [
11,
12]. Close-range photogrammetry based on structure from motion algorithms (SFM) can be used to produce a 3D forest model using photographic information and generate a large number of point clouds [
13]. While this method can provide 3D information about individual trees, its accuracy depends on the quality of the photographs, with the computational efficiency decreasing as the volume of data increases [
14].
With the emergence of remote sensing and unmanned aerial vehicle (UAV) technologies, UAV-LiDAR is becoming increasingly popular in forest surveys due to its low cost and easy operation [
15,
16]. UAV-LiDAR, as an active remote sensing technique, generates point clouds with 3D information about the forest by emitting laser pulses into the leaves and branches [
17]. This method not only provides high-precision 3D information on large-area forests but also has the capability to penetrate vegetation gaps to obtain understory information [
18,
19,
20].
Individual tree segmentation is a key step in obtaining the structural features of trees. Existing individual tree segmentation algorithms can be categorized into raster-based methods, point cloud-based methods and hybrid methods. Raster-based methods segment individual trees using image processing approaches on a digital surface model (DSM) or canopy height model (CHM). Common methods include the watershed algorithm [
21], marker-controlled watershed algorithm [
22], region growing [
23], template matching [
24] and valley following [
25]. The CHM is derived by subtracting the digital elevation model (DEM) from the DSM [
26]. Chen et al. (2006) proposed a variable window local maximum method to detect treetops and the marker-controlled watershed algorithm was employed to achieve canopy segmentation in sparse grassland forests [
27]. Koch et al. (2006) integrated a pouring approach with knowledge based on tree shapes to detect the crown edges for individual crown segmentation [
5]. Wu et al. (2016) developed a graph-theory-based localized contour tree method, combining geometric and topological features to extract the hierarchical structures of tree crowns for individual tree segmentation [
28]. Compared to complex point clouds, raster data have a simple structure. Therefore, individual tree segmentation methods based on raster data have high computational efficiency and accuracy in simple forest environments [
29]. However, the accuracy of individual tree segmentation depends significantly on the spatial resolution of the raster data [
30]. Additionally, the transformation of point clouds into a raster can lead to the loss of 3D structural information and some forest details, posing challenges in detecting understory vegetation and the boundaries of intertwining trees in complex forests [
31].
Point-cloud-based methods cluster the LiDAR points of individual trees based on the normalized point cloud using the spatial structure features of the point cloud [
32]. There are several popular methods, such as region growing [
12,
33], mean shift [
3], K-means [
34], normalized cut [
35] and the density-based spatial clustering of applications with noise (DBSCAN) [
36]. Li et al. (2012) proposed a top-down region growing algorithm, which took into account the crown shape and the distance relationship between trees to achieve individual tree segmentation [
33]. Yan et al. (2020) presented a self-adaptive mean shift tree segmentation method that can automatically estimate the optimal kernel bandwidth without prior knowledge of the tree crown size [
37]. Hao et al. (2022) developed a hierarchical region-merging algorithm that initially performed over-segmentation based on the local density, followed by a stepwise optimal merging process to achieve the final segmentation [
4]. Point-cloud-based methods can detect secondary layers in complex forests using the 3D structural information of forests. Compared with raster-based methods, this reduces under-segmentation and can obtain finer individual tree parameters [
38]. However, the method is time-consuming due to the large number of point clouds, making it more demanding in terms of computer performance.
Hybrid methods combine point-cloud-based methods or raster-based methods with the spatial characteristics of the point cloud to improve the accuracy of individual tree segmentation. Paris et al. (2016) proposed an algorithm that combined CHM analysis with point cloud space analysis [
39]. This approach successfully detects crowns missed by the CHM method by analyzing the horizontal profile of the forest, thereby improving the detection rate of secondary layers. Yang et al. (2020) presented an individual tree segmentation algorithm based on the watershed algorithm and 3D spatial distribution analysis [
40]. The trees were initially segmented by the marker-controlled watershed algorithm, followed by a multidirectional 3D contour analysis of the single tree to detect the positions of potential treetops. Finally, fine segmentation of the forest has been achieved using K-means. Ma et al. (2020) proposed an individual tree segmentation algorithm based on a region growing algorithm and crown morphological structures [
41]. The initial segmentation was performed using the region growing algorithm, and under-segmentation was detected by contour analysis. This combined approach significantly enhanced the detection rate of individual trees compared to using the region growing algorithm alone. Compared to point-cloud-based methods and raster-based methods, these methods improve the detection rate of individual trees—especially the secondary layers that are obscured by the upper canopy. However, they retain the limitations of the single method.
Although numerous methods have been proposed in recent years to improve the accuracy of individual tree segmentation, the process remains challenging. The inaccurate seed points and unclear crown boundaries can often lead to over-segmentation and under-segmentation, especially in natural forests. The accuracy of seed point detection can have an influence on individual tree segmentation. The traditional methods (e.g., local maximum method) are strongly dependent on the window size [
42,
43]. If the window is too large, the treetops of small trees may be overlooked, and if it is too small, the tops of elongated branches are likely to be misidentified as seed points. In this study, we propose an individual tree segmentation method based on seed point detection using an adaptive crown shaped algorithm. Our method is not dependent on the window size. It performs a profile analysis of a circle centered on the local maximum to delineate and mark initial crowns, which can effectively reduce the impact of higher points on seed point detection.
6. Conclusions
This study presented an individual tree segmentation method based on seed points detected by an adaptive crown shaped algorithm. It realized seed point detection and individual tree segmentation in four experimental plots with varied forest types and topographies. The results showed the following.
(1) The proposed method performed well in seed point detection and individual tree segmentation. The crown structure and distance between trees were considered in the adaptive crown shaped algorithm to detect seed points accurately. (2) The tree height, crown diameter and crown projection area were extracted based on the segmented individual trees. Compared with the measured data, the estimated crown parameters showed high accuracy, especially in the natural coniferous forest. (3) Compared with the other algorithms for seed point detection and individual tree segmentation, our method exhibited superior performance across the four plots, with significant improvements in the overall accuracy.
The proposed method was applied to forests of various tree species with varying terrains, demonstrating its capability to obtain seed points and segment individual trees with high accuracy. This lays the foundation for the accurate estimation of forest structural parameters, thereby significantly contributing to the improved accuracy of forest inventory.