Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China

Abstract

This paper proposes a method to classify individual tree species groups based on individual tree segmentation and crown-level spectrum extraction (“crown-based ITC” for abbr.) in a natural mixed forest of Northeast China, and compares with the pixel-based classification and segment summarization results (“pixel-based ITC” for abbr.). Tree species is a basic factor in forest management, and it is traditionally identified by field survey. This paper aims to explore the potential of individual tree classification in a natural, needle-leaved and broadleaved mixed forest. First, individual trees were isolated, and the spectra of individual trees were then extracted. The support vector machine (SVM) and spectrum angle mapper (SAM) classifiers were applied to classify the trees species. The pixel-based classification results from hyperspectral data and LiDAR derived individual tree isolation were compared. The results showed that the crown-based ITC classified broadleaved trees better than pixel-based ITC, while the classes distribution of the crown-based ITC was closer to the survey data. This indicated that crown-based ITC performed better than pixel-based ITC. Crown-based ITC efficiently identified the classes of the dominant and sub-dominant species. Regardless of whether SVM or SAM was used, the identification consistency relative to the field observations for the class of the dominant species was greater than 90%. In contrast, the consistencies of the classes of the sub-dominant species were approximately 60%, and the overall consistency of both the SVM and SAM was greater than 70%.

Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China

“…However, it was found that the combination of hyperspectral and LiDAR features could achieve an improvement in accuracy over HSI, and this finding is consistent with the conclusions of independent studies (Dalponte et al, 2012;Hartling et al, 2021). Zhao et al (2020) has pointed out that the average spectrum of the tree canopy can Average spectral curves for different tree species.…”

Correlation between the Spectrometric Parameters of Coniferous Seeds and the Molecular Indicators of Seedlings: Is It Possible to Apply It in Practice?

“…When implementing the forest restoration method, including group of site preparation operations [53], group of FRM preparation operations , group of seeding [47,54,55] or planting [56,57] on site, it additionally includes group of monitoring operations [58,59]. Alternative rapid analysis [16,31] and encapsulation [60] operations carried out in field conditions using mobile equipment [26,[49][50][51] are additionally introduced into a group of FRM preparation operations.…”

Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images

“…Airborne light detection and ranging (LiDAR) technology obtains horizontal and vertical structural information of ground objects by actively transmitting lasers to targets and receiving point cloud, echo information data [26,27]. In most studies, LiDAR data were often combined with hyperspectral data to improve the classification accuracy [28][29][30][31]. However, the cost of airborne LiDAR data was high, and the processing of airborne LiDAR data was complex, which was not suitable for large-scale forest classification [32].…”