Feasibility of terrestrial laser scanning for quantification of vegetation structure parameters of restored sandy land in the southern Qinghai–Tibeten Plateau

Abstract

The efficiency of ecosystem restoration of degraded land is typically assessed using vegetation structure parameters (VSP). However, the field measurement of VSP, particularly in the Qinghai–Tibet Plateau (QTP), which is a problematic area for ecological restoration because of its harsh environment, is generally time‐consuming and labour intensive. Therefore, new methods for VSP measurement should be developed. In this study, VSP were quantified at the individual scale, including their number, height, and crown width. Furthermore, the quadrat scale, such as vegetation coverage (VC) and leaf area index (LAI) on a restored sandy plot (100 m × 100 m) in the middle reaches of the Yarlung Zangbo River basin, is quantified using terrestrial laser scanning. Thereafter, the TLS‐derived VSP are validated by field measurements. To extract the VSP of low‐height vegetation (shrubs) at the individual scale, three approaches are tested: the manual measurement of VSP from TLS data, individual vegetation segmentation (IVS) without specific root locations, and IVS with specific root locations. The results indicate that the VSP of shrubs can be manually derived from the TLS data and are consistent with field measurements. By specifying the root locations before performing the IVS, the identification rate of the number of shrubs can be improved significantly from 40.3% to 100%. The height extracted by the IVS corresponds better with the manual measurement (R2 > 0.99) than with the crown width (R2 < 0.65). At the quadrat scale, the optimal resolution is analyzed to extract the VC. The analysis suggests that the optimal resolution for extracting the VC based on the canopy height model is in the 1‐2 cm range. In extracting the LAI using the voxel‐based method, it is discovered that the appropriate voxel size is linearly dependent on the mean point spacing (y = 2.143x, R2= 0.826). This study provides useful guidance for monitoring the effectiveness of ecological conservation and restoration on the QTP.

Quality assessment of shrub observation data based on TLS: A case of revegetated shrubland, Southern Qinghai‐Tibetan Plateau

“…The DS 1‐(r) and DS 2‐(r) represent the reference data set collected by 5 scanning spots of the sample plot #1 and #2, the DS 1‐(n) and DS 2‐(n) represent the experimental data set collected by the n'th scanning spot, and the DS 1‐(n‐) and DS 2‐(n‐) represent the experimental data set collected by all the scanning spots except the n'th scanning spot (ID of the scanning spot could be found in Figure 1c). In previous research (Tian et al, 2020), we have evaluated the extraction accuracy of the H and CW between the reference data set (5 scanning spots) and the field‐measured data ( R 2 = 0.944 and 0.970, respectively), which verified the integrity of reference data set based on TLS.…”

“…The point cloud data collected at each scanning spot was freely com- (ID of the scanning spot could be found in Figure 1c). In previous research (Tian et al, 2020), we have evaluated the extraction accuracy of the H and CW between the reference data set (5 scanning spots) and the field-measured data (R 2 = 0.944 and 0.970, respectively), which verified the integrity of reference data set based on TLS.…”

“…Firstly, the statistical outlier removal filter was utilized in the step of denoising. Secondly, the ground point was separated based on the progressive triangulated irregular network densification filter (Zhao et al, 2016), and the digital elevation model (DEM) at a resolution of 0.2 m (Tian et al, 2020) was obtained by the kriging interpolation (Guo et al, 2010). Finally, the normalized point cloud data was In order to evaluate the contribution of each scanning spot to vegetation information observation in sample plot #2, the BPPs of all shrubs were extracted from the DS 2-(n-) and were compared with the DS 2-(r) .…”

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

“…In comparison, the application of LiDAR technology in grassland ecosystems is still finite due to the complexity of grassland (low height and high density) and the small size of individual plants. Although grassland canopy height can be estimated accurately based on terrestrial laser scanning (TLS) data ( Guimarães-Steinicke et al., 2019 ; Tian et al., 2020 ; Xu et al., 2020 ), it is limited to the sample plot scale. On the contrary, space-borne LiDAR is too coarse to detect grassland canopy height because of low point density or large laser footprint, even if it can cover large regional or global areas ( Malambo and Popescu, 2021 ).…”

Optimization&nbsp;of Scanning Scheme for Low-height Vegetation Survey Based on Terrestrial Laser Scanning - A Case Study on the Restored Sand Land, Southern Qinghai-Tibetan Plateau

“…The DS 1−(r) and DS 2−(r) represent the reference data set collected by 5 scanning spots of the sample plot #1 and #2, the DS 1−(n) and DS 2−(n) represent the experimental data set collected by the n'th scanning spot, and the DS 1−(n−) and DS 2−(n−) represent the experimental data set collected by all the scanning spots except the n'th scanning spot (the speci c number of the scanning spots could be found in Fig.1c). In previous research(Tian et al, 2020), we have evaluated the extraction accuracy of the H and CW between the reference data set (5 scanning spots) and the eld-measured data (R 2 = 0.944 and 0.970, respectively), which veri ed the integrity of reference data set based on TLS.…”