VOLVV—A Novel Urban Regional Living Vegetation Volume Quantitative Estimation Model Based on the Voxel Measurement Method and an Octree Data Structure
Abstract
:1. Introduction
2. Principle and Design of a Novel LVV Estimation Model Based on a Voxel Method and an Octree
 (1)
 The 3D bounding box of the vegetation point cloud data is constructed, which will be used as the partition space of the voxel model and octree.
 (2)
 According to the set voxel resolution, the voxel model is constructed; on this basis, the octree index is established for each vegetation point cloud.
 (3)
 The voxel density at each node is calculated and compared with the set threshold.
 (4)
 When the density is lower than this threshold, it is necessary to search the leaf node of the point and infer with the voxel of smaller scale until the voxel of the smallest scale is accessed.
 (5)
 When the density is higher than a certain threshold, the voxel is considered to be completely covered by the vegetation point cloud. At that time, the voxel volume is calculated and accumulated into the results according to the voxel level.
 (6)
 According to the observed original data, depending on the data collection method and vegetation type, parameter fitting is performed, and the corrected LVV calculation value is finally obtained.
3. Implementation of the VOLVV Estimation Model
 (1)
 Obtain the XYZ space coordinate information for all point clouds.
 (2)
 Construct an octree organizational structure for the point cloud collection.
 (3)
 Use the voxel nearest neighbor method to establish a fast search method for point clouds in voxel space.
 (4)
 Establish a method for measuring the volume of voxels at different levels.
4. Experimental Verification of the VOLVV Estimation Model
 (1)
 Through the testing of vegetation point cloud samples, as compared with the wrapped surface approach, our LVV measurement method used in this study enabled faster calculation, and the acceleration ratio generally reached approximately 44:1–102:1, thus indicating good calculation efficiency.
 (2)
 For point cloud data from three different sources, when the same calculation method is used, e.g., the wrapped surface approach or the VOLVV measurement method, the corresponding LVV results can be calculated. However, comparison of the proximity of the results to the true value of LVV indicated that the colocation results from the point cloud data based on 3D reconstruction with the UAV airborne oblique photogrammetry technique were better than those obtained through the other methods (MLS or UAV airborne LiDAR). Therefore, the point cloud data with higher point density appear to improve the LVV calculation accuracy. Meanwhile, the mean value (16.0617) of the first method for the three data sources is closer to the true value (25.7401) than that of the second method (14.0726).
 (3)
 For the most suitable point cloud data source for LVV estimation, i.e., the point cloud data from 3D reconstruction, the absolute value of the measurement error rate (difference between measured value and true value/true value) was 19.9%, thus indicating better measurement accuracy than the 26.8% absolute value of the traditional wrapped surface approach.
 (1)
 The VOLVV estimation method can maintain a linear relationship with a single plant in calculations involving multiple plants. Therefore, this method is suitable for simultaneous calculation scenarios of single trees and multiple plants. In contrast, the wrapped surface approach and formula measurement method can perform calculations for only single trees, and their calculation efficiencies are quite low.
 (2)
 Similar to the second item in the above analysis from Table 3.
 (3)
 The VOLVV estimation method still maintained a good acceleration ratio, as compared with that of the wrapped surface approach, reaching approximately 98:1–230:1.
 (4)
 For the most suitable point cloud data source for LVV estimation, i.e., the point cloud data from 3D reconstruction, the absolute error rate of the VOLVV method was 14.5%, whereas the absolute error rate of the wrapped surface approach was 15.2%.
5. Discussion and Conclusions
 The VOLVV estimation method, compared with a traditional method, i.e., the wrapped surface approach, enabled substantial efficiency improvement and higher calculation accuracy. For the most suitable point cloud data source (i.e., the point cloud data from 3D reconstruction) for LVV estimation, the maximum calculation acceleration ratio reached 230X, and the best absolute error acceleration rate of the VOLVV method was 6.9%.
 Furthermore, the new method can be simultaneously applied to scenarios of single plants and multiple plants, and can be used for the calculation of LVV in areas with various vegetation types in cities.
 However, this method also has some limitations in the following aspects:
 In this method, the voxel resolution and density threshold strongly influence the LVV calculation results. Due to the lack of theoretical analysis on voxel scale division in this algorithm, it is unable to process data with low point density.
 This method uses parameter fitting to optimize the lack of information in the original point cloud data, and it does not analyze the spatial distribution of point cloud data; therefore, errors may be present in the estimation results.
 The number of verification data will be increased to verify the adaptability of the algorithm in different canopy structures.
 For low point density in the original point cloud data, the commonly used point cloud upsampling algorithm [33] could be introduced.
 For the lack of information due to the point cloud acquisition method, point cloud completion technology based on deep learning [34] could be used to complete the canopy data and improve the calculation accuracy.
 Among different types of vegetation in cities, the lushness of branches and leaves greatly varies. Other measurement methods for specific tree species could be introduced and combined with the results of this study to yield an adaptive LVV highprecision calculation scheme. Through indepth improvement of the method reported herein, a rapid estimation model of urban regional LVV could be established, which would enable urban greening measurements to be quickly obtained and analyzed.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A

Appendix B

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Type  iScanSZ (MLS)  Zenmuse L1 (Airborne LiDAR) 

Limit measurement distance  119 m  450 m 
Laser emission frequency  1.01 × 10^{7} p/s  2.4 × 10^{6} p/s (single echo), 4.8 × 10^{6} p/s (multiple echo) 
Ranging accuracy  0.9 mm (at 50 m)  3 cm (at 100 m) 
Angular resolution  0.0088°  0.01° 
System accuracy  5 cm (at 100 m)  10 cm (at 50 m) 
Point Cloud Source  Data Format  Point Cloud Quantity  Point Cloud Density, Resolution  Data Details 

MLS  .las  23,762  8000 points/m^{3}, 5 cm  Missing part of the crown dorsal information 
UAV Airborne LiDAR  .las  50,398  8000 points/m^{3}, 5 cm  Missing the trunk and crown bottom 
UAV Airborne oblique photogrammetry  .las  58,7005  1,000,000 points/m^{3}, 1 cm  Missing the trunk and crown bottom 
Calculation Method  Measurement Data  Calculated LVV (m^{3})  Calculation Time (s) 

Wrapped surface approach  MLS  16.1555  0.308 
Airborne LiDAR  13.1832  1.673  
3D reconstruction point cloud  18.8464  7.660  
VOLVV method (voxel resolution: 0.2 m, density threshold: 1000/m^{3})  MLS  11.3601  0.007 
Airborne LiDAR  10.2427  0.024  
3D reconstruction point cloud  20.6150  0.075  
True value (based diameterheight correlation equation)  Crown width  4.355 m  / 
Crown height  2.592 m  
LVV in theory  25.7401 m^{3} 
Calculation Method  Measurement Data  Calculated LVV (m^{3})  Calculation Time (s) 

Wrapped surface approach  MLS  58.1347  2.291 
Airborne LiDAR  39.9395  6.199  
3D reconstruction point cloud  67.7621  26.942  
VOLVV method (voxel resolution: 0.2 m, density threshold: 1000/m^{3})  MLS  39.2171  0.016 
Airborne LiDAR  39.1926  0.063  
3D reconstruction point cloud  68.3091  0.117  
True value (based on diameterheight correlation equation)  LVV in theory  79.8619  / 
Point Cloud Source  Data Format  Point Cloud Quantity  Point Cloud Density, Resolution  Data Details 

MLS  .las  301,462  8000 points/m^{3}, 5 cm  Missing part of the crown dorsal information 
Airborne LiDAR  .las  723,622  8000 points/m^{3}, 5 cm  Missing the trunk and crown bottom 
UAV Airborne oblique photogrammetry  .las  7,137,059  1,000,000 points/m^{3}, 1 cm  Missing the trunk and crown bottom 
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Huang, F.; Peng, S.; Chen, S.; Cao, H.; Ma, N. VOLVV—A Novel Urban Regional Living Vegetation Volume Quantitative Estimation Model Based on the Voxel Measurement Method and an Octree Data Structure. Remote Sens. 2022, 14, 855. https://doi.org/10.3390/rs14040855
Huang F, Peng S, Chen S, Cao H, Ma N. VOLVV—A Novel Urban Regional Living Vegetation Volume Quantitative Estimation Model Based on the Voxel Measurement Method and an Octree Data Structure. Remote Sensing. 2022; 14(4):855. https://doi.org/10.3390/rs14040855
Chicago/Turabian StyleHuang, Fang, Shuying Peng, Shengyi Chen, Hongxia Cao, and Ning Ma. 2022. "VOLVV—A Novel Urban Regional Living Vegetation Volume Quantitative Estimation Model Based on the Voxel Measurement Method and an Octree Data Structure" Remote Sensing 14, no. 4: 855. https://doi.org/10.3390/rs14040855