1. Introduction
Forests are the largest carbon reservoir and ecosystem on land, providing not only vital ecological services but also enormous economic benefits in the process of human development [
1]. The acquisition of forestry parameters, such as tree height, crown width, species, biomass, etc., is critical in the process of investigation and monitoring. The monitoring of forest resources provides an effective scientific methodology for offground density estimations, change trend analysis, forest growth detection, harvest prediction, and so on [
2,
3,
4]. Traditional forest resource monitoring is usually time-consuming and labor-intensive due to manual field collection, which is unsuitable for large-scale research. In addition, accuracy is limited by factors such as measurement instrument errors [
5], positional errors caused by GPS signal obstruction, and coarse errors in manual measurements. However, manual measurements are still the most commonly used method in forestry surveys. Therefore, there is a need to explore new approaches for rapid and reliable data validation that can meet the requirements of forestry production and ecological construction [
6]. Since the characteristics of remote sensing technology include wide monitoring range, quick data acquisition, and low cost, the application of remote sensing technology for rapid and large-scale validation and updating of forestry parameters is of both theoretical significance and practical importance.
Passive optical remote sensing, such as multispectral remote sensing, hyperspectral remote sensing, and high-resolution remote sensing, have been widely used for estimating forest parameters with notable progress and outcomes. The spectral information of passive optical remote sensing data from visible to near infrared reflects the physical structure parameters of the forest, and forestry parameters, such as vegetation index and texture information, can then be the derived. Ouma [
7] used semivariance functions on QuickBird images to investigate the relationship between forest biomass and spectral variables in Kenya. Marshall and Thenkabail [
8] compared the response of hyperspectral data EO-1 Hyperion and multispectral data on biomass generation, determining that hyperspectral data were superior. Mohammadi et al. [
9] developed a model for forest stock estimation in northern Iraq using Landsat ETM+ data. Franklin [
10] et al. estimated the canopy density of spruce using TM data with an accuracy of 80%. According to the findings of the preceding studies, passive optical remote sensing data are mostly used to invert the horizontal structural parameters of forests and are rarely utilized to estimate the vertical structure (e.g., tree height) of forests. This is mainly attributed to the low signal penetration of optical remote sensing data, which makes obtaining information in the vertical direction challenging. However, some researchers, such as Brown et al. [
11], have tried to use high resolution overlapping stereo images to achieve crown height estimation, but the elevation accuracy of the undertree surface still cannot meet the requirements.
Synthetic Aperture Radar (SAR), the active remote sensing technology, has the ability to penetrate forest vegetation crowns and observe the ground in all weather conditions. SAR can also interact with treetops and trunks to capture the vertical structure of forests. Cloude and Papathanassiou [
12] used polarization coherence tomography to reconstruct low-frequency three-dimensional (3D) images and provided a method for optimal interferometric baseline selection to estimate forest vertical structure. Blomberg et al. [
13] used L-band SAR data from Argentina’s observation satellite SAOCOM to accurately invert forest biomass in northern Europe. Matasci et al. [
14] approximated the aboveground biomass of forests with root mean square deviation (RMSD) error of less than 20% using European Space Agency (ESA) P-band radar data. Although SAR is sensitive to forest vertical structure, backscatter signal saturation often occurs when the forest biomass is large. For example, Luckman et al. [
15] used JERS-1 SAR data to estimate tropical forest biomass and discovered that the backscatter coefficient saturated when the biomass reached 6 kg/m
2, affecting the accuracy of forest biomass estimation.
Light Detection and Ranging (LiDAR) has advantages such as high angle resolution, distance resolution, and anti-interference ability, which make it possible to gather high precision three-dimensional surface information while avoiding signal saturation in high biomass areas [
16]. Particularly in the field of forestry survey application, LiDAR has significant advantages over other remote sensing technologies with respect to forest height measurement and vertical structure acquisition in forest stands. LiDAR can provide highly accurate horizontal and vertical information of forests depending on the sampling method and configuration, but the optical sensors can only be used to provide detailed information on the horizontal distribution of forests. Therefore, this study will use airborne LiDAR data to identify the critical indicators of the forest resources present in the sample area.
LiDAR in forestry surveys can be divided into the area-based approach (ABA) and individual tree detection (IDT), depending on the survey scale. ABA uses statistical modeling to estimate forest characteristics by metrics derived from point clouds within a predefined plot or grid [
17]. Lefsky et al. [
18] laid the theoretical foundation for the ABA method and applied it to plot-level mean diameter at breast height (DBH). The strong correlation between tree height and DBH [
19,
20] is widely used in forestry inventories to estimate forest parameters such as stock volume and biomass [
21,
22].
This study focuses on the method of individual tree detection, i.e., detecting and delineating individual trees and estimating their parameters from point clouds based on geometric features. The basis for estimating forestry parameters is accurate segmentation of tree point clouds. Tree crown segmentation methods based on LiDAR data are mainly divided into the following two categories: raster-based tree segmentation and direct point cloud segmentation. By interpolating the three-dimensional point cloud, the raster-based tree segmentation first develops a digital surface model (DSM) and a crown height model (CHM) by normalizing the tree height. Then, based on the height undulations in the CHM, local maximum [
23,
24] or variable windows [
25,
26] are used to search for local maximum as initial treetop locations, and finally, edge detection or feature extraction methods are employed to identify tree crowns. Watershed segmentation algorithms [
27,
28,
29] and flow tracking algorithms [
30] are two examples of raster-based tree segmentation algorithms. The CHM-based segmentation method is quick and effective, but it can identify the wrong segment and omit details. This problem is partially addressed by a Fishing Net Dragging (FiND) method proposed by Liu et al. [
31], with an overall accuracy of 82.4%. However, the segmentation accuracy is still influenced by the CHM resolution, and CHM only represents crown surface information without describing the crown’s vertical structure. With the development of LiDAR technology, the density and accuracy of point clouds have rapidly developed, and many researchers directly use the point cloud data to segment tree crowns [
32,
33]. Wang et al. [
34] first proposed voxel segmentation of raw point cloud data with the vertical crown structure of the forest, dividing the crown areas of different heights based on the elevation distribution within the voxels and performing tree segmentation. Morsdorf et al. [
35] used local maxima search as seed points for k-mean clustering of three-dimensional point clouds. Li et al. [
36] proposed a top–to–bottom area growth algorithm relying on the relative distance between trees, and this method achieved 90% segmentation accuracy for coniferous forests. However, its applicability was not transferrable to dense forest areas with overlapping crowns. Compared with the traditional raster-based tree segmentation method, the direct segment processing of point cloud data can more accurately reflect the three-dimensional structure of trees. Unfortunately, the majority of segmentation studies on tree segmentation using LiDAR data prefer low-density stands, and most of them are not ideal for complex forest environments with overlapping crowns and a variety of tree species. Additionally, the single segmentation method is not universal and is challenging to apply to trees of different scales. Chen et al. [
37] utilized the PointNet algorithm for direct segmentation of point cloud data. However, the segmentation results are significantly influenced by the appropriate voxel size, which limits its performance in the segmentation of trees at multiple scales in large areas. Yan et al. [
38] proposed a self-adaptive bandwidth estimation method to estimate the optimal kernel bandwidth automatically without any prior knowledge of crown size, but it has not yet been applied to complex natural forest areas. This study builds upon previous work [
39] and employs a rotation profile algorithm for tree crown segmentation. The algorithm dynamically captures the contour of the point cloud profile to determine the edges of the tree crown.
For the study of tree species classification and identification based on LiDAR data, Holmgren and Persson [
40] used a supervised classification method to distinguish Norway spruce and Scots pine with 95% accuracy. Othmani et al. [
41] used terrestrial laser scanning (TLS) data to distinguish five tree species using wavelet transform with an overall accuracy of 88%. Lin and Hyyppä [
42] used a support vector machine approach to classify the tree species by extracting point cloud distribution, crown-internal and tree-external features, and achieved an overall accuracy of 85%. Kim et al. [
43] extracted crown structure parameters for tree species classification using leaf-on and leaf-off LiDAR data in the growing and deciduous seasons; the results indicated that tree species identification from both data was superior to single season data. In addition, some other scholars have made full use of point cloud intensity information and introduced it into tree species classification studies, such as Ørka et al. [
44], who combined structural and intensity features to classify Norway spruce and birch, and their results proved that the classification accuracy was better than using structural or intensity features alone. The primary benefit of LiDAR intensity is related to the reflectance of surface features; there are several intensity-related confounding variables, such as parameters connected to the feature’s environment, the sensor hardware system, and the data gathering geometry [
45]. As a result, algorithmic parametric models based on intensity information are usually limited to a single location. Qin et al. [
46] combined structural, spectral, and textural information to achieve an overall accuracy of 91.8% in tree species classification. However, vegetation spectral and textural information often vary over time, making it less universally applicable across seasons. As demonstrated above, accurate crown structure information is the most reliable feature for tree species classification. In this paper, deep belief network (DBN) is utilized to learn the shape of crown profiles of known tree species in sample plots. The method is suitable for tree species with different crown shapes and can be used in dense forests.
LiDAR has been successfully applied in forestry parameter extraction for a long time. Solodukhin et al. [
47] used LiDAR point cloud data for tree height extraction, and the RMSE between their estimated tree height and photogrammetry results was 14 cm. The parameters that can be directly obtained from the segmented tree crowns are generated from the LiDAR data. Information such as tree height and crown width or height can be easily obtained, but the crown width diameter at breast height (DBH) and tree species cannot be directly obtained. Although LiDAR data cannot directly estimate the diameter at breast height of forest trees, some existing studies use measured data to establish relationships and indirectly infer tree diameter at breast height parameters from LiDAR data. For example, Shrestha and Wynne [
48] estimated the diameter at breast height of trees in urban areas of central Oklahoma, USA, using the Optech ALTM 2050 system with an R
2 of 0.89. As parameters derived from LiDAR coordinate information, crown structure parameters are widely used in forest biomass inversion. They are usually calculated from the vegetation echoes after elevation normalization, including 25%, 50%, and 75% percentile height, maximum tree height, mean tree height, and forest crown height. Bortolot and Wynne [
49] established a regression analysis based on the 25%, 50%, and 75% percentile height and biomass and obtained correlation coefficients between predicted and actual measurements ranging from 0.59 to 0.82, with RMSE ranging from 13.6 to 140.4 t/ha. Wang et al. [
50] estimated the aboveground biomass based on an Unmanned Aerial Vehicle (UAV) LiDAR system and the results showed that the mean height of trees was the most reasonable parameter to predict aboveground biomass. Several researchers have recognized the importance of LiDAR intensity data and applied it to biomass inversion, such as García et al. [
51], who estimated biomass in a Mediterranean forest in central Spain using height parameters derived from airborne LiDAR point cloud data and distance-corrected intensity parameters; their results showed that intensity correction could improve the accuracy of forest biomass estimation. Numerous research studies have demonstrated that parameter estimation considering tree species classification is more accurate. Donoghue et al. [
52] discovered that LiDAR-based tree height and biomass estimation algorithms for coniferous forests were not applicable to mixed forests. Jin et al. [
53] introduced tree species as a dummy variable into the regression model when point cloud feature regression modeling was performed to estimate the stocking volume using the peak forest site in Guangxi, with an elevated coefficient of determination R
2 of the model estimation results. Pang and Li [
54] divided temperate forests in the Xiaoxing’an Mountains into coniferous, broadleaf, and mixed forests for biomass inversion, and the findings revealed that differentiated biomass modeling can further improve biomass estimation accuracy. Therefore, in this paper we will use existing tree species to verify and update the wrong tree species information in the sample plots, as well as correct the diameter at breast height, aboveground biomass, storage volume, and other parameters of trees in the sample plots based on accurate tree species information.
In summary, this paper focuses on the urgent needs of current forestry surveys and uses LiDAR point cloud data to verify and update the error information of manually collected data. The paper addresses the following issues: (1) To solve the segmentation problem of staggered crowns for the complex growing condition of the northeastern primeval forest, the rotating profile segmentation method is used to obtain the crown edge points and the segmentation point cloud, which is consistent with our previous work. (2) This paper utilizes the geometric structure information of tree crowns to identify species. The structures of individual tree species are very different, so this paper attempts to use the segmentation of the shape of the crown section. The deep belief network (DBN) method is used to establish the tree species recognition model and update the sample tree species error information. The expected contribution of this study is to extract profile information as one-dimensional arrays instead of fitting shapes for classification. Additionally, the application of DBN enables high-precision identification of tree species in complex natural forest areas. (3) Finally, the forest parameters are estimated and updated based on the corrected tree species information. And the superiority of LiDAR data in forest parameters extraction is verified to validate its feasibility for large-scale application in forest resource survey.
This paper is organized as follows:
Section 1 discusses the significance and advantages of LiDAR point cloud data in forestry resource surveying as well as the current status and limitations, which leads to the method proposed in this paper.
Section 2 includes an overview of the study area’s location and characteristics as well as an introduction to the experimental data. It also describes the paper’s research methods and processes.
Section 3 contains the results of tree crown segmentation, species identification, and parameter extraction, while
Section 4 has a full analysis and explanation of the findings.
Section 5 summarizes the research findings and provides an outlook on future research work.