Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Sample Plots
2.2. Instrumentation
- (i)
- 3D Scanner App 1.8.1 [75] (Laan Labs, New York, NY, USA) was used in High Res mode, with the following settings: max depth = 5 m; resolution = 10 mm; confidence = high; masking = off. The scans were colorless, and no postprocessing was required.
- (ii)
- Polycam 1.2.7 [76] (Polycam Inc., San Francisco, CA, USA) did not offer any setting options for the scan and was thus used in standard mode. Postprocessing of the scans was necessary and performed within the app, using the standard quality setting. As raw scan data were saved, postprocessing could be conducted any time after the completion of the scans.
- (iii)
- SiteScape 1.0.2 [77] (SiteScape Inc., New York, NY, USA) was used with the following settings: scan mode = max area; point density = low; point size = low. The scan was automatically post-processed in the app directly after scanning.
2.3. Data Collection
2.4. Point Cloud Processing, Clustering, Detection of Tree Positions, and dbh Measurement
2.5. Evaluation of Point Cloud Quality and Diameter Fitting on Cylindrical Reference Objects
2.6. Reference Data
2.7. Accuracy of Tree Detection, dbh Measurement, and DTM
3. Results
3.1. Acquisition Time and Number of Points
3.2. Digital Terrain Model (DTM)
3.3. Detection of Tree Positions
3.4. Estimation of dbh
3.5. Tree Location
3.6. Cylindrical Reference Objects under In Vitro Conditions
4. Discussion
4.1. DTM Modelling, Stem Detection and DBH Estimation
4.2. Point Cloud Quality and Field Experience
4.3. Comparison with Other Studies
4.4. Outlook into the Future
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Plot | Stand Class | Regeneration | dbh Range (cm) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 1 | 27.4 | 4 | 96 | 0 | 0 | 0 | 24.4 | 5.5–54.6 | 40.2 | 859 | 826 | 0.74 | 0.57 | 0.5 | 0.16 |
2 | 3 | 2 | 22.4 | 8 | 77 | 0 | 8 | 8 | 32.5 | 9.2–43.3 | 34.3 | 414 | 630 | 0.36 | 0.41 | 0.44 | 0.79 |
3 | 3 | 4 | 32.5 | 20 | 60 | 0 | 0 | 20 | 46.8 | 33.0–54.7 | 27.4 | 159 | 435 | 0.18 | 0.18 | 0.73 | 0.95 |
4 | 2 | 1 | 24.0 | 67 | 33 | 0 | 0 | 0 | 33.3 | 5.0–50.2 | 58.2 | 668 | 1058 | 0.56 | 0.39 | 0.55 | 0.64 |
5 | 3 | 0 | 17.5 | 67 | 17 | 0 | 0 | 17 | 30.1 | 9.3–47.0 | 45.4 | 637 | 859 | 0.42 | 0.36 | 0.46 | 0.87 |
6 | 3 | 1 | 19.4 | 25 | 75 | 0 | 0 | 0 | 42.2 | 29.2–51.5 | 35.6 | 255 | 590 | 0.18 | 0.22 | 0.41 | 0.56 |
7 | 3 | 3 | 22.4 | 33 | 56 | 11 | 0 | 0 | 38.1 | 6.0–59.9 | 36.3 | 318 | 627 | 0.62 | 0.52 | 0.46 | 0.94 |
8 | 1 | 1 | 10.5 | 86 | 8 | 0 | 0 | 3 | 19.5 | 5.2–32.9 | 35.3 | 1178 | 793 | 0.4 | 0.33 | 0.5 | 0.52 |
9 | 3 | 2 | 23.1 | 50 | 25 | 12 | 0 | 12 | 49.6 | 33.3–59.2 | 49.3 | 255 | 766 | 0.19 | 0.21 | 0.59 | 1.21 |
10 | 2 | 0 | 47.1 | 0 | 100 | 0 | 0 | 0 | 22.6 | 5.3–43.4 | 28.2 | 700 | 598 | 0.66 | 0.57 | 0.5 | 0 |
11 | 3 | 3 | 24.0 | 55 | 45 | 0 | 0 | 0 | 43.8 | 5.3–58.0 | 52.7 | 350 | 860 | 0.36 | 0.3 | 0.7 | 0.69 |
12 | 3 | 1 | 14.2 | 64 | 36 | 0 | 0 | 0 | 37.7 | 14.6–50.7 | 39 | 350 | 676 | 0.31 | 0.28 | 0.48 | 0.66 |
13 | 2 | 0 | 34.2 | 44 | 48 | 0 | 0 | 0 | 24.9 | 5.1–42.6 | 41.7 | 859 | 852 | 0.73 | 0.5 | 0.47 | 0.96 |
14 | 2 | 0 | 41.4 | 50 | 36 | 7 | 0 | 7 | 33.3 | 9.8–51.4 | 38.9 | 446 | 707 | 0.38 | 0.36 | 0.6 | 1.09 |
15 | 2 | 0 | 25.7 | 39 | 56 | 0 | 0 | 0 | 21.8 | 5.0–46.5 | 48.8 | 1305 | 1049 | 0.78 | 0.49 | 0.42 | 0.84 |
16 | 2 | 0 | 36.4 | 0 | 11 | 0 | 0 | 0 | 28.0 | 6.0–42.6 | 29.4 | 477 | 572 | 0.53 | 0.43 | 0.46 | 0.35 |
17 | 3 | 2 | 20.7 | 14 | 43 | 0 | 24 | 19 | 36.5 | 6.0–59.2 | 69.8 | 668 | 1226 | 0.52 | 0.51 | 0.45 | 1.3 |
18 | 2 | 1 | 30.8 | 0 | 79 | 0 | 0 | 21 | 29.9 | 5.6–56.2 | 53.7 | 764 | 1019 | 0.66 | 0.51 | 0.46 | 0.51 |
19 | 2 | 0 | 32.5 | 0 | 100 | 0 | 0 | 0 | 20.8 | 5.3–38.9 | 37.8 | 1114 | 828 | 0.76 | 0.52 | 0.43 | 0 |
20 | 2 | 0 | 34.4 | 0 | 86 | 0 | 0 | 0 | 20.8 | 5.0–44.0 | 37.8 | 1114 | 828 | 0.58 | 0.5 | 0.43 | 0.51 |
21 | 3 | 0 | 28.7 | 0 | 75 | 0 | 0 | 0 | 26.0 | 6.0–49.3 | 32.2 | 605 | 646 | 0.68 | 0.5 | 0.36 | 0.56 |
Step No. | Step/Substep | Software | Package/Function | Parameters | |||
---|---|---|---|---|---|---|---|
1 | Registration of point cloud | GeoSLAM Hub | |||||
2 | Export in .las format | 100% of points time stamp: scan point color: none | |||||
1 | Export in .ply format | 3D Scanner App, Polycam, SiteScape | |||||
2a | Import, sphere fit, cloud transformation | Cloud-Compare | fit sphere, align | ||||
2b | Export in .las format | save | |||||
3 | Import data | R | lidR | readLAS() | filter = “-keep_circle 0 0 8” | ||
4 | Classify into ground points and non-ground points | lasground() | csf(class_threshold = 0.05, cloth_resolution = 0.2, rigidness = 1) | ||||
5 | Create DTM | grid_terrain() | res = 0.2, knnidw(k = 2000, p = 0.5) | ||||
6 | Normalize relative to DTM | lasnormalize() | |||||
7 | Remove ground points | lasfilter() | Classification = 1 | ||||
8 | Sample random point per voxel | TreeLS | tlsSample() | voxelize(spacing = 0.02) voxelize(spacing = 0.01) | |||
9a | Clustering 2D | calculate reachability of each point | dbscan | optics() | eps = 0.025 eps = 0.030 minPts = 90 minPts = 90 | ||
9b | DBSCAN clustering | extractDBSCAN() | eps_cl = 0.025 eps_cl = 0.030 | ||||
10 | Filter clusters | various functions in base | nr. of points ≥ 500 vertical extent ≥ 1.3 m vertical extent ≥ 1.0 m | ||||
11a | If (extension ≥ 0.22 m2) Clustering 3D | calculate reachability of each point | dbscan | optics() | eps = 0.025 minPts = 20 | ||
11b | DBSCAN clustering | extractDBSCAN() | eps_cl = 0.02 | ||||
11a | if (extension < 0.22 m2) Clustering 3D | calculate reachability of each point | dbscan | optics() | eps = 0.025 minPts = 18 | ||
11b | DBSCAN clustering | extractDBSCAN() | eps_cl = 0.023 | ||||
12 | Filter clusters | various functions in base | nr. of points ≥ 500 vertical extent ≥ 1.3 m 80% quantile intensity > 7900 vertical extent ≥ 1.0 m | ||||
13 | Stratification into 14 vertical layers | various functions in base | from 1 m to 2.625 m from 0.2 m to 1.825 m vertical extent = 0.15 m overlap = 0.025 m | ||||
14a | Preparing layers for diameter estimation | edci | circMclust() | nx = 25 ny = 25 nr = 5 | |||
14b | l | conicfit | EllipseDirectFit() | ||||
14c | if(diam. < 0.3 m) add buffer | various functions in base | + 0.06 m | ||||
14d | if(diam. ≥ 0.3 m) add buffer | various functions in base | + 0.09 m | ||||
15a | diameter estimation | edci | circMclust() | nx = 25 ny = 25 nr = 5 | |||
15b | conicfit | EllipseDirectFit() | |||||
15c | conicfit | LMcircleFit() | |||||
15d | mgcv | gam() predict() | s(angle, bs = “cc”) | ||||
spatstat | area.owin() | ||||||
15e | mgcv | gam() predict() | te(angle, Z, bs = c(“cc”,”tp”)) Z = 1.3 m | ||||
15f | spatstat | area.owin() | |||||
16a | Check criteria for diameters for 6 out of 14 layers | sd XY position ( and ) | various functions in base | ≤0.01 m | |||
16b | sd diameter ( and ) | ≤0.0185 m | |||||
17 | Calculate final position at 1.3 m (from or ) | base | lm() | ||||
18 | Calculate final dbh at 1.3 m for all diameter fits | base | lm() | ||||
19 | Affine transformation of tree positions | vec2dtransf | AffineTransformation() | ||||
20 | Assign tree positions | spatstat | pppdist() | cutoff = 0.3 |
Appendix B
dbh | PLS | 3D | Poly | Sites-Cape | PLS | 3D | Poly | Sites-Cape | PLS | 3D | Poly | Sites-Cape |
---|---|---|---|---|---|---|---|---|---|---|---|---|
≥5 cm | 98.10 | 84.49 | 76.67 | 81.41 | 2.11 | 2.47 | 0.53 | 1.35 | 95.27 | 81.80 | 76.39 | 80.21 |
≥10 cm | 99.52 | 97.33 | 90.65 | 95.06 | 2.58 | 2.55 | 0.60 | 1.40 | 95.71 | 94.37 | 90.18 | 93.62 |
≥15 cm | 100.00 | 98.06 | 94.68 | 97.26 | 3.10 | 2.80 | 0.68 | 1.87 | 95.07 | 94.76 | 93.89 | 95.11 |
RMSE (cm)/RMSE (%) | Bias (cm)/Bias (%) | ||||||||
---|---|---|---|---|---|---|---|---|---|
dbh | Method | PLS | 3D | Poly | Sitescape | PLS | 3D | Poly | Sitescape |
≥5 cm | gam | 1.85 (6.98) | 3.12 (10.36) | 4.08 (13.76) | 3.20 (9.94) | −0.51 (−1.36) | −1.01 (−2.94) | 0.39 (3.04) | −1.31 (−3.26) |
tegam | 1.78 (6.80) | 3.10 (10.26) | 4.70 (16.26) | 3.18 (9.93) | −0.21 (−0.26) | −1.06 (−3.11) | 0.58 (3.96) | −1.20 (−2.93) | |
circ | 1.64 (5.98) | 3.11 (10.10) | 3.85 (13.23) | 3.39 (10.63) | −0.73 (−2.29) | −0.39 (−1.45) | 0.44 (2.96) | −0.90 (−1.94) | |
circ2 | 1.73 (6.49) | 3.19 (10.61) | 4.05 (13.69) | 3.21 (9.97) | −0.54 (−1.50) | −1.04 (−2.92) | 0.36 (2.93) | −1.17 (−2.78) | |
ell | 1.92 (7.46) | 3.11 (10.10) | 4.20 (13.92) | 3.20 (9.93) | −0.042 (0.58) | −1.03 (−3.00) | 0.28 (2.80) | −1.03 (−2.43) | |
≥10 cm | gam | 1.65 (5.06) | 3.18 (9.93) | 3.84 (12.15) | 3.20 (9.46) | −1.05 (−3.23) | −1.17 (−3.47) | 0.08 (1.52) | −1.28 (−3.16) |
tegam | 1.50 (4.64) | 3.17 (9.91) | 3.80 (11.99) | 3.21 (9.56) | −0.73 (−2.28) | −1.18 (−3.43) | 0.04 (1.48) | −1.17 (−2.84) | |
circ | 1.65 (5.01) | 3.18 (9.70) | 3.55 (11.38) | 3.41 (10.21) | −1.13 (−3.46) | −0.52 (−1.86) | 0.12 (1.39) | −0.83 (−1.77) | |
circ2 | 1.64 (5.01) | 3.24 (10.14) | 3.80 (12.02) | 3.22 (9.55) | −1.01 (−3.10) | −1.20 (−3.46) | 0.05 (1.41) | −1.16 (−2.78) | |
ell | 1.52 (4.68) | 3.18 (9.77) | 3.96 (12.39) | 3.20 (9.49) | −0.66 (−1.96) | −1.15 (−3.35) | 0.01 (1.48) | −1.03 (−2.46) | |
≥15 cm | gam | 1.78 (4.83) | 3.38 (9.40) | 3.09 (8.51) | 3.26 (8.80) | −1.28 (−3.49) | −1.52 (−4.17) | −0.87 (−1.79) | −1.38 (−3.32) |
tegam | 1.56 (4.23) | 3.36 (9.34) | 3.14 (8.59) | 3.28 (8.89) | −1.00 (−2.74) | −1.52 (−4.13) | −0.83 (−1.62) | −1.28 (−3.05) | |
circ | 1.77 (4.76) | 3.37 (9.17) | 2.88 (8.03) | 3.48 (9.48) | −1.32 (−3.60) | −0.79 (−2.38) | −0.80 (−1.78) | −0.89 (−1.96) | |
circ2 | 1.77 (4.78) | 3.40 (9.44) | 3.05 (8.39) | 3.28 (8.87) | −1.24 (−3.38) | −1.56 (−4.26) | −0.90 (−1.88) | −1.26 (−2.99) | |
ell | 1.60 (4.31) | 3.36 (9.22) | 3.29 (9.02) | 3.21 (8.63) | −0.98 (−2.64) | −1.46 (−3.93) | −0.92 (−1.73) | −1.08 (−2.44) |
Plot | pos (m) | time (min) | ||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PLS | 3D | Poly | Site | PLS | 3D | Poly | Site | PLS | 3D | Poly | Site | PLS | 3D | Poly | Site | PLS | 3D | Poly | Site | 3D | Poly | Site | 3D | Poly | Site | PLS | 3D | Poly | Site | |
1 | 100.0 | 90.0 | 70.0 | 80.0 | 0.0 | 0.0 | 12.5 | 11.1 | 100.0 | 90.0 | 60.00 | 70.0 | 1.53 | 3.04 | 4.58 | 2.73 | −0.96 | −1.10 | −0.98 | −1.63 | 0.07 | 0.06 | 0.05 | 0.14 | 0.19 | 0.15 | 4.5 | 8.2 | 9.1 | 7.9 |
2 | 100.0 | 100.0 | 100.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 100.0 | 100.0 | 1.02 | 2.04 | 3.50 | 4.09 | −0.75 | 1.50 | 2.25 | −0.44 | 0.05 | 0.04 | 0.05 | 0.24 | 0.17 | 0.06 | 3.6 | 8.3 | 6.8 | 7.4 |
3 | 100.0 | 100.0 | 100.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 100.0 | 100.0 | 2.69 | 2.36 | 2.56 | 3.49 | −2.40 | −1.83 | −1.91 | −3.46 | 0.05 | 0.05 | 0.05 | 0.04 | 0.12 | 0.09 | 7.2 | 6.9 | 6.3 | 6.0 |
4 | 100.0 | 100.0 | 100.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 100.0 | 100.0 | 1.93 | 4.14 | 1.70 | 3.24 | −1.43 | 0.87 | −0.17 | 0.50 | 0.05 | 0.05 | 0.07 | 0.15 | 0.12 | 0.20 | 3.8 | 7.3 | 7.7 | 7.6 |
5 | 100.0 | 92.3 | 100.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 92.3 | 100.0 | 100.0 | 0.69 | 9.18 | 4.00 | 2.09 | −0.17 | −2.24 | −0.38 | −0.57 | 0.03 | 0.03 | 0.04 | 0.13 | 0.17 | 0.22 | 3.5 | 9.7 | 8.7 | 7.8 |
6 | 100.0 | 100.0 | 83.3 | 100.0 | 0.0 | 14.3 | 0.0 | 0.0 | 100.0 | 83.3 | 83.3 | 100.0 | 1.44 | 3.63 | 1.08 | 4.00 | −0.86 | −3.05 | −0.28 | −1.82 | 0.07 | 0.04 | 0.09 | 0.13 | 0.12 | 0.26 | 3.3 | 7.2 | 6.5 | 6.9 |
7 | 100.0 | 100.0 | 100.0 | 100.0 | 37.5 | 0.0 | 0.0 | 0.0 | 40.0 | 100.0 | 100.0 | 100.0 | 2.54 | 2.20 | 4.41 | 1.67 | −1.76 | −0.77 | 2.57 | −0.41 | 0.04 | 0.04 | 0.06 | 0.09 | 0.15 | 0.24 | 3.7 | 6.6 | 6.5 | 6.8 |
8 | 100.0 | 94.1 | 88.2 | 94.1 | 0.0 | 0.0 | 0.0 | 5.9 | 100.0 | 94.1 | 88.2 | 88.2 | 1.10 | 1.15 | 4.45 | 1.57 | −0.94 | 0.16 | 3.63 | 0.67 | 0.04 | 0.04 | 0.04 | 0.06 | 0.10 | 0.17 | 2.9 | 9.2 | 7.1 | 8.2 |
9 | 100.0 | 100.0 | 80.0 | 80.0 | 0.0 | 16.7 | 0.0 | 0.0 | 100.0 | 80.0 | 80.0 | 80.0 | 2.54 | 1.85 | 3.46 | 4.58 | −2.01 | −1.43 | −2.64 | −4.38 | 0.06 | 0.07 | 0.10 | 0.07 | 0.08 | 0.45 | 3.3 | 5.5 | 6.1 | 6.2 |
10 | 100.0 | 100.0 | 100.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 100.0 | 100.0 | 1.62 | 2.14 | 4.28 | 2.04 | −1.44 | −0.44 | 1.74 | −0.22 | 0.08 | 0.04 | 0.15 | 0.15 | 0.07 | 0.34 | 4.0 | 9.2 | 8.9 | 9.6 |
11 | 100.0 | 100.0 | 66.7 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 66.7 | 100.0 | 2.35 | 3.77 | 6.67 | 3.68 | −1.16 | −0.67 | −4.13 | −1.91 | 0.12 | 0.09 | 0.06 | 0.10 | 0.02 | 0.07 | 3.5 | 7.3 | 7.1 | 6.9 |
12 | 100.0 | 80.0 | 100.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 80.0 | 100.0 | 100.0 | 1.17 | 6.44 | 6.64 | 6.82 | 0.17 | −4.58 | −4.57 | −5.50 | 0.06 | 0.05 | 0.06 | 0.15 | 0.21 | 0.23 | 3.1 | 7.1 | 6.9 | 7.7 |
13 | 100.0 | 87.5 | 100.0 | 100.0 | 0.0 | 0.0 | 0.00 | 0.0 | 100.0 | 87.5 | 100.0 | 100.0 | 1.19 | 3.13 | 4.01 | 3.84 | −0.90 | −2.18 | −1.79 | −1.09 | 0.05 | 0.06 | 0.11 | 0.05 | 0.15 | 0.26 | 3.8 | 8.9 | 8.1 | 8.9 |
14 | 100.0 | 100.0 | 100.0 | 83.3 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 100.0 | 83.3 | 1.69 | 2.41 | 2.22 | 2.97 | −1.20 | −0.97 | 1.13 | 0.58 | 0.05 | 0.07 | 0.10 | 0.07 | 0.05 | 0.31 | 3.6 | 5.8 | 5.6 | 6.4 |
15 | 100.0 | 100.0 | 100.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 100.0 | 100.0 | 1.76 | 1.48 | 3.34 | 3.49 | −1.52 | −0.23 | 0.94 | 1.68 | 0.04 | 0.05 | 0.05 | 0.09 | 0.09 | 0.18 | 3.2 | 8.3 | 8.2 | 7.8 |
16 | 100.0 | 100.0 | 90.0 | 90.0 | 16.7 | 0.0 | 0.0 | 0.0 | 80.0 | 100.0 | 90.0 | 90.0 | 2.06 | 2.23 | 3.16 | 4.12 | −1.40 | −1.25 | 2.26 | 2.96 | 0.04 | 0.05 | 0.13 | 0.07 | 0.12 | 0.59 | 2.9 | 6.9 | 7.4 | 6.5 |
17 | 100.0 | 100.0 | 85.7 | 100.0 | 0.0 | 12.5 | 0.0 | 12.5 | 100.0 | 85.7 | 85.7 | 85.7 | 1.96 | 3.74 | 7.11 | 4.25 | −1.69 | −1.84 | −3.78 | −3.35 | 0.04 | 0.04 | 0.08 | 0.11 | 0.10 | 0.18 | 3.5 | 6.9 | 6.2 | 6.97 |
18 | 100.0 | 100.0 | 88.9 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 88.9 | 100.0 | 1.01 | 4.26 | 4.50 | 1.27 | −0.71 | −2.76 | 1.27 | −0.79 | 0.06 | 0.04 | 0.10 | 0.12 | 0.15 | 0.13 | 4.2 | 7.5 | 6.5 | 7.75 |
19 | 100.0 | 100.0 | 100.0 | 88.9 | 0.0 | 10.0 | 0.0 | 0.0 | 100.0 | 88.9 | 100.0 | 88.9 | 1.30 | 1.40 | 3.40 | 2.74 | −0.88 | −0.21 | 1.49 | −2.06 | 0.06 | 0.06 | 0.05 | 0.18 | 0.17 | 0.16 | 3.7 | 8.6 | 8.5 | 8.08 |
20 | 90.0 | 100.0 | 60.0 | 80.0 | 0.0 | 0.0 | 0.0 | 0.0 | 90.0 | 100.0 | 60.0 | 80.0 | 1.23 | 3.28 | 2.72 | 2.05 | −0.94 | −1.77 | 0.25 | −0.41 | 0.04 | 0.04 | 0.06 | 0.08 | 0.09 | 0.16 | 4.2 | 9.1 | 6.8 | 7.67 |
21 | 100.0 | 100.0 | 90.9 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 100.0 | 90.9 | 100.0 | 1.82 | 2.87 | 5.45 | 2.55 | −0.79 | 0.67 | 3.25 | −0.01 | 0.04 | 0.07 | 0.05 | 0.09 | 0.07 | 0.13 | 3.6 | 8.7 | 8.7 | 7.27 |
Appendix C
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# of Sample Plots | 21 | ||||||||
---|---|---|---|---|---|---|---|---|---|
# of Trees | 424 | ||||||||
# of Trees/Sample Plot | 20.2 | ||||||||
dbh Range (cm) | 5.0–59.9 | ||||||||
Mean | SD | Min | Max | q (0.05) | q (0.25) | q (0.5) | q (0.75) | q (0.95) | |
(%) | 27.1 | 8.9 | 10.5 | 47.1 | 14.2 | 22.4 | 25.7 | 32.5 | 41.4 |
(cm) | 31.6 | 9.0 | 19.5 | 49.6 | 20.8 | 24.4 | 30.1 | 37.7 | 46.8 |
(m2/ha) | 41.5 | 10.7 | 27.4 | 69.8 | 28.2 | 35.3 | 38.9 | 48.8 | 58.2 |
(trees/ha) | 643 | 333 | 159 | 1305 | 255 | 350 | 637 | 859 | 1178 |
(trees/ha) | 783 | 193 | 435 | 1226 | 572 | 630 | 793 | 859 | 1058 |
0.50 | 0.20 | 0.18 | 0.78 | 0.18 | 0.36 | 0.53 | 0.66 | 0.76 | |
0.41 | 0.12 | 0.18 | 0.57 | 0.21 | 0.33 | 0.43 | 0.51 | 0.57 | |
0.50 | 0.09 | 0.36 | 0.73 | 0.41 | 0.44 | 0.46 | 0.50 | 0.70 | |
0.67 | 0.36 | 0 | 1.30 | 0 | 0.51 | 0.66 | 0.94 | 1.21 |
Shape | Material | Diameter (cm) | |
---|---|---|---|
Object 1 | cylinder | plastic | 49.9 |
Object 2 | cylinder | metal | 40.0 |
Object 3 | cylinder | metal | 26.4 |
Object 4 | cylinder | plastic | 16.0 |
Object 5 | cylinder | metal | 11.5 |
Object 6 | cylinder | plastic | 6.5 |
Object | Reference Diameter (cm) | 3D Scanner App | Polycam | SiteScape | PLS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Scan Time: 4.7 min | Scan Time: 4.3 min | Scan Time: 3.9 min | Scan Time: 1 min | ||||||||||
circ2 (cm) | gam (cm) | circ2 (cm) | gam (cm) | circ2 (cm) | gam (cm) | circ2 (cm) | gam (cm) | ||||||
Object 1 | 49.90 | −5.48 | −5.61 | 0.76 | 4.98 | 4.93 | 0.43 | −0.49 | −0.98 | 1.60 | −0.70 | −0.69 | 0.95 |
Object 2 | 40.00 | 0.44 | 0.44 | 0.15 | −4.51 | −4.62 | 0.18 | 0.48 | 0.37 | 1.16 | −0.24 | −0.23 | 1.09 |
Object 3 | 26.40 | −1.59 | −1.56 | 0.13 | −1.87 | −1.99 | 0.16 | −5.36 | −5.42 | 1.52 | −0.33 | −0.34 | 0.86 |
Object 4 | 16.00 | −2.28 | −2.27 | 0.95 | 0.25 | 0.27 | 0.10 | −5.47 | −5.51 | 1.04 | 0.05 | 0.04 | 0.11 |
Object 5 | 11.50 | −0.32 | −0.33 | 0.05 | 0.85 | 0.80 | 0.11 | −4.81 | −4.81 | 1.16 | −0.40 | −0.40 | 0.86 |
Object 6 | 6.50 | 0.74 | 0.73 | 0.09 | −1.71 | −1.81 | 0.28 | −1.41 | −1.65 | 1.16 | −0.44 | −0.52 | 0.83 |
Reference | Method/Technology | Single/Multiple Trees | Detection Rate (%) | Commission Rate (%) | dbh RMSE (cm) | dbh Bias (cm) | Tree Location RMSE (cm) | DTM RMSE (cm) |
---|---|---|---|---|---|---|---|---|
This study | iPad/3D Scanner App | multiple | 97.33 | 2.55 | 3.64 | −0.87 | 10.9 | 5.3 |
iPad/Polycam | multiple | 90.65 | 0.60 | 4.51 | 1.03 | 11.9 | 5.1 | |
iPad/SiteScape | multiple | 94.68 | 1.40 | 3.13 | −0.58 | 21.8 | 7.2 | |
PLS/GeoSLAM ZEB HORIZON | multiple | 99.52 | 2.58 | 1.59 | 0.22 | |||
Tomaštík et al. [10] | Tango/Lenovo Phab 2 Pro | multiple | 1.15 | 20.0 | ||||
CRP/Canon EOS 5D Mark II | multiple | 1.83 | 4.0 | |||||
Hyyppä et al. [52] | Tango/Lenovo Phab 2 Pro | single | 0.73 | 0.33 | ||||
Kinect/regular computer | single | 1.90 | 0.54 | |||||
Brouwer [57] | Kinect/regular laptop | multiple | 83.75 | 12.25 | 1.30 | |||
TLS/Riegl VZ-400 | multiple | 91.75 | 5.25 | 0.74 | ||||
McGlade et al. [95] | Azure Kinect/regular laptop | single | 8.43 | 2.05 | ||||
Fan et al. [97] | RTAB-Map/Lenovo Phab 2 Pro | multiple | 47.7 | |||||
Trunk-based Backend/Lenovo Phab 2 Pro | multiple | 8.3 | ||||||
Mokroš et al. [96] | CRP/Canon 70D non-fisheye lens | single | 0.42–0.71 | |||||
CRP/Canon 70D fisheye lens | single | 0.39–0.60 | ||||||
Piermattei et al. [8] | CRP/Nikon D800 | multiple | 84.25 | 9.17 | 3.09 | −1.11 | 4.07 | |
TLS/Riegl VZ-2000 | multiple | 93.75 | 14.40 | 1.78 | −0.70 |
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Gollob, C.; Ritter, T.; Kraßnitzer, R.; Tockner, A.; Nothdurft, A. Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology. Remote Sens. 2021, 13, 3129. https://doi.org/10.3390/rs13163129
Gollob C, Ritter T, Kraßnitzer R, Tockner A, Nothdurft A. Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology. Remote Sensing. 2021; 13(16):3129. https://doi.org/10.3390/rs13163129
Chicago/Turabian StyleGollob, Christoph, Tim Ritter, Ralf Kraßnitzer, Andreas Tockner, and Arne Nothdurft. 2021. "Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology" Remote Sensing 13, no. 16: 3129. https://doi.org/10.3390/rs13163129