Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry
"> Figure 1
<p>(<b>a</b>) Location of the study site in the Jigongshan Natural Reserve, Xinyang, Henan Province, China; and (<b>b</b>) true color map of the study site (red dots indicate the centres of ground plots).</p> "> Figure 2
<p>Minimum holes left by the constrained neighbor (CN) interpolation algorithm with different threshold values (<span class="html-italic">Q</span>) (void cells are white, and effective cells are grey): (<b>a</b>) <span class="html-italic">Q</span> = 5; (<b>b</b>) <span class="html-italic">Q</span> = 6; (<b>c</b>) <span class="html-italic">Q</span> = 7; (<b>d</b>) <span class="html-italic">Q</span> = 8; and (<b>e</b>) <span class="html-italic">Q</span> ≥ 5.</p> "> Figure 3
<p>Original canopy height models (CHMs) and interpolated CHMs by the constrained neighbor (CN) and ordinary neighbor (ON) algorithms from the LiDAR and photogrammetry data sets with a spatial resolution of 0.1 m (no data areas are white): (<b>a</b>) original CHM of L-D55; (<b>b</b>) CHM of L-D55 interpolated by the CN algorithm; (<b>c</b>) CHM of L-D55 interpolated by the ON algorithm; (<b>d</b>) original CHM of P1-D300; (<b>e</b>) CHM of P1-D300 interpolated by the CN algorithm; (<b>f</b>) CHM of P1-D300 interpolated by the ON algorithm; (<b>g</b>) original CHM of P3-D150; (<b>h</b>) CHM of P3-D150 interpolated by the CN algorithm; (<b>i</b>) CHM of P3-D150 interpolated by the ON algorithm; (<b>j</b>) original CHM of P1-D80; (<b>k</b>) CHM of P1-D80 interpolated by the CN algorithm; and (<b>l</b>) CHM of P1-D80 interpolated by the ON algorithm. The color describes the lowest to highest height from blue to green. The CHMs of P2-D150 and P4-D150 are similar to that of P3-D150 at the same flying height of 150 m and thus are not shown here.</p> "> Figure 4
<p>Effective cell ratios (ECRs), point cloud homogeneities (PCHs), and point cloud redundancies (PCRs) of the original CHMs and interpolated CHMs by the constrained neighbor (CN) algorithm from LiDAR and photogrammetry data sets: (<b>a</b>) ECRs of the original CHMs; (<b>b</b>) ECRs of the CHMs interpolated by the CN algorithm; (<b>c</b>) PCHs of the original CHMs; (<b>d</b>) PCHs of the CHMs interpolated by the CN algorithm; (<b>e</b>) PCRs of the original CHMs; and (<b>f</b>) PCRs of the CHMs interpolated by the CN algorithm.</p> "> Figure 5
<p>Stratified mean differences between the original LiDAR CHM and photogrammetry CHMs within 1.0 m bins of the canopy heights: (<b>a</b>) mean differences between CHMs at a spatial resolution of 0.1 m; (<b>b</b>) mean differences between CHMs at a spatial resolution of 0.2 m; (<b>c</b>) mean differences between CHMs at a spatial resolution of 0.5 m; and (<b>d</b>) mean differences between CHMs at a spatial resolution of 1.0 m.</p> "> Figure 6
<p>Coefficient of determination (<math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math>), <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math>, and relative <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> between the CHMs from LiDAR and photogrammetry data sets: (<b>a</b>) <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> between the original CHMs; (<b>b</b>) <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> between the CHMs interpolated by the CN algorithm; (<b>c</b>) <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> between the CHMs interpolated by the ON algorithm; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> between the original CHMs; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> between the CHMs interpolated by the CN algorithm; (<b>f</b>) <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> between the CHMs interpolated by the ON algorithm; (<b>g</b>) relative <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> between the original CHMs; (<b>h</b>) relative <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> between the CHMs interpolated by the CN algorithm; and (<b>i</b>) relative <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> between the CHMs interpolated by the ON algorithm.</p> "> Figure A1
<p>Point density of the 1 m resolution grid (no data areas are white; the grid is stretched with a percent clip of 0.5): (<b>a</b>) L-D55 point cloud with a mean of 168 points m<sup>−2</sup>; (<b>b</b>) P1-D300 point cloud with a mean of 303 points m<sup>−2</sup>; (<b>c</b>) P2-D150 point cloud with a mean of 1319 points m<sup>−2</sup>; (<b>d</b>) P3-D150 point cloud with a mean of 1191 points m<sup>−2</sup>; (<b>e</b>) P4-D150 point cloud with a mean of 1050 points m<sup>−2</sup>; (<b>f</b>) P5-D80 point cloud with a mean of 2529 points m<sup>−2</sup>.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. UAV Data Sets
2.3. Data Processing
2.4. Descriptors of Spatial Distribution
2.5. Constrained Neighbor Interpolation
2.6. Analysis of Spatial Resolution
2.7. Data Set Assessment
3. Results
3.1. CHMs of LiDAR and Photogrammetry
3.2. Spatial Distribution of Point Clouds
3.3. Differences between L-CHMs and P-CHMs
3.4. Correlation between L-CHMs and P-CHMs
4. Discussion
4.1. Spatial Distribution of Point Clouds
4.2. Effects of the CN and ON Algorithms on the CHM
4.3. Distinguishing Small and Large Holes within a CHM
4.4. Optimal Spatial Resolution of the CHM
4.5. Effects of Flying Configuration on the CHM
4.6. Effects of Gaps within and between Crowns on the CHM
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Plot Parameters | Mean | Stand Deviation | Minimum | Maximum |
---|---|---|---|---|
Number of stems | 63 | 21 | 30 | 96 |
Basal area (m2) | 2.4 | 0.9 | 1.0 | 4.4 |
Lorey’s height (m) | 23.8 | 4.4 | 15.7 | 31.2 |
Mean crown width (m) | 5.4 | 0.6 | 4.3 | 6.8 |
Data Set | Date(y/m/d) | Local Time (h:m) | Flying Height (m) 1 | Interval (m)/Overlap (%) | Flying Speed (m s−1) | Footprint Size/GSD (cm) | |
---|---|---|---|---|---|---|---|
Along-Track | Across-Track | ||||||
L-D55 | 2017/8/10 | 11:40–11:50 | 55 | / | 55 | 4.8 | 17 |
2017/8/11 | 10:13–10:24 | 50 2 | / | 55 | 4.8 | 15 | |
P1-D300 | 2017/8/9 | 13:38–13:56 | 300 | 40/80% | 50/84% | 4.8 | 4 |
P2-D150 | 2017/8/14 | 15:14–15:32 | 150 | 20/80% | 30/80% | 4.8 | 2 |
P3-D150 | 2017/8/14 | 16:39–16:51 | 150 | 20/80% | 30/80% | 8.0 | 2 |
P4-D150 | 2017/8/15 | 11:08–11:20 | 150 | 20/80% | 50/68% | 4.8 | 2 |
P5-D80 | 2017/8/15 | 15:33–15:51 | 80 | 10/81% | 30/64% | 4.8 | 1 |
Data Set | L-D55 | ||||||
---|---|---|---|---|---|---|---|
Original CHM | Interpolated CHM by the CN Algorithm 1 | Interpolated CHM by the ON Algorithm 2 | Spatial Resolution (m) | ||||
P1-D300 | −0.1 | 1.0 | −0.2 | 1.1 | −0.3 | 1.3 | 0.1 |
P2-D150 | −0.4 | 0.9 | −0.5 | 1.0 | −0.6 | 1.1 | |
P3-D150 | −0.5 | 0.9 | −0.5 | 1.0 | −0.6 | 1.1 | |
P4-D150 | −0.3 | 1.0 | −0.4 | 1.0 | −0.5 | 1.2 | |
P5-D80 | −0.1 | 1.1 | −0.1 | 1.2 | −0.3 | 1.4 | |
P1-D300 | 0.2 | 0.8 | 0.1 | 0.9 | 0.0 | 1.0 | 0.2 |
P2-D150 | −0.1 | 0.7 | −0.1 | 0.7 | −0.2 | 0.8 | |
P3-D150 | −0.1 | 0.7 | −0.2 | 0.8 | −0.3 | 0.9 | |
P4-D150 | 0.0 | 0.8 | −0.1 | 0.8 | −0.2 | 0.9 | |
P5-D80 | 0.2 | 0.9 | 0.1 | 1.0 | 0.1 | 1.1 | |
P1-D300 | 0.4 | 0.8 | 0.4 | 0.8 | 0.3 | 0.8 | 0.5 |
P2-D150 | 0.1 | 0.6 | 0.1 | 0.6 | 0.1 | 0.6 | |
P3-D150 | 0.1 | 0.6 | 0.0 | 0.6 | 0.0 | 0.6 | |
P4-D150 | 0.1 | 0.7 | 0.1 | 0.7 | 0.1 | 0.7 | |
P5-D80 | 0.3 | 0.8 | 0.3 | 0.9 | 0.3 | 0.9 | |
P1-D300 | 0.5 | 0.7 | 0.5 | 0.7 | 0.5 | 0.8 | 1.0 |
P2-D150 | 0.2 | 0.5 | 0.1 | 0.5 | 0.1 | 0.5 | |
P3-D150 | 0.1 | 0.5 | 0.1 | 0.5 | 0.1 | 0.5 | |
P4-D150 | 0.2 | 0.6 | 0.2 | 0.6 | 0.2 | 0.6 | |
P5-D80 | 0.3 | 0.7 | 0.3 | 0.7 | 0.3 | 0.8 |
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LiDAR | |||
---|---|---|---|
UAV model | GV1300 | Rotor | 8 |
LiDAR model | VLP-16 | PRF | 300 kHz |
Laser wavelength | 905 nm | Laser divergence | 3 mrad |
Scan pattern | Rotate Mirror | Scan FOV | 30° × 360° |
Echoes | 2 | Max Scan frequency | 20 Hz |
Range | 2 m–120 m | Vertical Accuracy | <5 cm |
Photogrammetry | |||
UAV model | GV1500 | Rotor | 6 |
Camera model | EOS 5DS | Pixels | 50,320,896 |
CMOS size | 36.0 × 24.0 mm | Image size | 8688 × 5792 pixels |
FOV | 54.4° × 37.8° | Focal length | 35 mm |
Pixel unit | 4.1 × 4.1 µm | Bands | R/G/B |
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Liu, Q.; Fu, L.; Chen, Q.; Wang, G.; Luo, P.; Sharma, R.P.; He, P.; Li, M.; Wang, M.; Duan, G. Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry. Remote Sens. 2020, 12, 2884. https://doi.org/10.3390/rs12182884
Liu Q, Fu L, Chen Q, Wang G, Luo P, Sharma RP, He P, Li M, Wang M, Duan G. Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry. Remote Sensing. 2020; 12(18):2884. https://doi.org/10.3390/rs12182884
Chicago/Turabian StyleLiu, Qingwang, Liyong Fu, Qiao Chen, Guangxing Wang, Peng Luo, Ram P. Sharma, Peng He, Mei Li, Mengxi Wang, and Guangshuang Duan. 2020. "Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry" Remote Sensing 12, no. 18: 2884. https://doi.org/10.3390/rs12182884