Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Data Collection
2.2.1. Field Data
2.2.2. LiDAR Data
2.2.3. UAV Imagery
UAV Equipment and Payload
UAV Imagery Collection: Planning and Implementation
2.3. Data Analysis
2.3.1. Photogrammetric Processing
2.3.2. Generation and Comparison of LiDAR and UAV-SfM CHMs
Generation of LiDAR Canopy Height
Generation of UAV-SfM Canopy Height
Comparison of LiDARCHM and UAV-SfMCHM
2.3.3. Extraction and Comparison of Forest Structural Metrics
- Maximum height (MaxH)
- Mean height (MeanH)
- Standard deviation of heights, also known as the rugosity index [24] (SD of H)
- Coefficient of variation (CV of H)
- Skewness and Kurtosis
- Percentile heights of 10%, 25%, 50%, 75% and 95% (P10, P25, P50, P75 and P95)
- Canopy cover above 2 m height calculated as the proportion of points above 2 m height to the total number of points (d0)
- Density of points at 1st, 2nd, …, 9th height fractions (d1, d2, …, d9)
- Canopy cover above mean height calculated as the proportion of points above mean height to the total number of points (dmean)
- Surface area ratio that is the proportion of 3D canopy surface area to the ground surface area. Also known as “rumple index” [24].
2.3.4. Evaluation of the Utility of UAV-SfM-Derived Point Cloud Products and Plot-Level Validation of Canopy Height
2.3.5. Identification of Factors that Affect the Performance of UAV-SfMCHM
3. Results
3.1. Comparison of LiDAR and UAV-SfM Outputs
3.1.1. LiDAR and UAV-SfM Point Cloud Properties
3.1.2. LiDAR and UAV-SfM CHMs
3.2. Comparison of Structural Metrics Derived from Photogrametric Products
3.3. Regression Modelling and Plot-Level Validation of Forest Structural Attributes
3.4. Factors that Affect the Performance of UAV-SfMCHM
4. Discussion
4.1. Characterisation of Forest Canopy Using the UAV-SfM Technique
4.2. Estimation and Plot-Level Validation of Forest Structural Attributes
4.3. Influence of Forest Structural Properties and Topographic Conditions on the Performance of Canopy Height Models
4.4. General Considerations for Forestry Applications
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Compartment | 43 | 48 |
---|---|---|
Total area (ha) | 335 | 340 |
Altitude range (m a.s.l.) | 425 to 810 | 397 to 833 |
Date of ground survey | March, 2016 | March, 2017 |
Total number of sample plots (plot size) | 59 (0.25 ha) | 46 (40 of 0.25 ha and 6 of 0.125 ha) |
Compartment 43 | Compartment 48 | ||
---|---|---|---|
Average (SD) Values | Average (SD) Values | ||
Dominant Height (m) | 25.5 (3.5) | 22.0 (4.3) | |
For trees with DBH ≥ 14 cm | Mean DBH (cm) | 32.3 (4.6) | 14.9 (4.1) |
Gross volume (m3/ha) | 286.5 (91.6) | 207.5 (105.0) | |
BA (m2/ha) | 32.2 (9.6) | 24.4 (110.0) | |
Stem density (trees/ha) | 324 (93) | 366 (102) | |
Proportion of conifer stems | 0.46 (0.16) | 0.26 (0.20) | |
Only for canopy trees | Mean DBH (cm) | 36.5 (14.9) | 34.3 (11.0) |
Gross volume (m3/ha) | 211.2 (61.7) | 177.0 (82.9) | |
BA (m2/ha) | 21.6 (5.7) | 19.8 (7.9) | |
Stem density (trees/ha) | 124 (57) | 218 (105) | |
Proportion of conifer stems | 0.55 (0.20) | 0.30 (0.23) |
Parameter | Description |
---|---|
Nominal flying height | 600 m |
Flying speed | 140 km/h |
Course overlap | 50% |
Pulse rate | 100 kHz |
Scan angle | ±20° |
Beam divergence | 0.16 mrad |
Point density | 8.4 pts./m2 |
Structural Metrics | Compartment 43 | Compartment 48 | ||||||
---|---|---|---|---|---|---|---|---|
LiDAR Mean (SD) | UAV-SfM Mean (SD) | MD (SD of Difference) | RMSD | LiDAR Mean (SD) | UAV-SfM Mean (SD) | MD (SD of Difference) | RMSD | |
MaxH (m) | 30.00 (3.72) | 27.05 (3.49) | 2.96 (1.14) | 2.69 | 25.41 (4.54) | 24.37 (4.36) | 1.05 (1.84) | 2.24 |
MeanH (m) | 16.78 (3.21) | 17.07 (3.53) | −0.29 (0.93) | 1.12 | 13.90 (3.43) | 15.42 (4.31) | −1.52 (1.25) | 2.34 |
SD of H (m) | 5.19 (0.99) | 3.86 (1.15) | 1.34 (0.78) | 1.47 | 4.18 (0.92) | 3.16 (0.87) | 1.02 (0.77) | 1.24 |
CV of H (m) | 0.32 (0.06) | 0.24 (0.09) | 0.08 (0.10) | 0.13 | 0.31 (0.06) | 0.22 (0.10) | 0.09 (0.06) | 0.08 |
Skewness | −0.46 (0.56) | −0.35 (0.68) | −0.12 (0.42) | 0.69 | −0.22 (0.53) | −0.15 (0.73) | −0.07 (0.52) | 0.70 |
Kurtosis | 3.26 (0.88) | 3.64 (1.27) | −0.38 (0.98) | 1.49 | 3.23 (0.73) | 3.65 (1.55) | −0.42 (1.36) | 2.41 |
P10 (m) | 9.46 (2.53) | 11.97 (3.83) | −2.51 (2.11) | 3.39 | 8.30 (2.53) | 11.33 (4.22) | −3.03 (2.12) | 3.82 |
P25 (m) | 13.68 (3.28) | 14.71 (3.68) | −1.03 (1.40) | 2.07 | 11.30 (3.30) | 13.41 (4.45) | −2.11 (1.56) | 2.79 |
P50 (m) | 17.44 (3.73) | 17.41 (3.83) | 0.03 (0.74) | 0.80 | 14.18 (3.80) | 15.48 (4.55) | −1.30 (1.24) | 2.13 |
P75 (m) | 20.39 (3.91) | 19.69 (3.84) | 0.70 (0.62) | 0.91 | 16.74 (4.07) | 17.52 (4.52) | 1.07 (0.07) | 1.74 |
P95 (m) | 24.25 (3.53) | 22.87 (3.50) | 1.38 (0.57) | 1.21 | 20.41 (4.05) | 20.46 (4.44) | 1.40 (0.40) | 1.95 |
d0 | 0.94 (0.05) | 0.99 (0.04) | −0.05 (0.03) | 0.05 | 0.91 (0.15) | 1.00 (0.00) | −0.09 (0.15) | 0.34 |
d1 | 0.92 (0.06) | 0.97 (0.07) | −0.05 (0.04) | 0.06 | 0.89 (0.16) | 0.98 (0.04) | −0.10 (0.12) | 0.28 |
d2 | 0.89 (0.08) | 0.95 (0.10) | −0.06 (0.04) | 0.07 | 0.85 (0.18) | 0.94 (0.14) | −0.10 (0.06) | 0.12 |
d3 | 0.85 (0.11) | 0.93 (0.14) | −0.07 (0.06) | 0.09 | 0.79 (0.21) | 0.91 (0.18) | −0.12 (0.06) | 0.14 |
d4 | 0.78 (0.16) | 0.87 (0.21) | −0.09 (0.07) | 0.10 | 0.70 (0.24) | 0.84 (0.23) | −0.15 (0.06) | 0.15 |
d5 | 0.71 (0.21) | 0.80 (0.27) | −0.09 (0.07) | 0.11 | 0.58 (0.27) | 0.73 (0.30) | −0.15 (0.08) | 0.16 |
d6 | 0.63 (0.23) | 0.73 (0.29) | −0.10 (0.08) | 0.14 | 0.46 (0.29) | 0.59 (0.36) | −0.13 (0.10) | 0.16 |
d7 | 0.55 (0.23) | 0.64 (0.29) | −0.08 (0.09) | 0.14 | 0.34 (0.26) | 0.47 (0.37) | −0.12 (0.13) | 0.22 |
d8 | 0.45 (0.21) | 0.50 (0.26) | −0.05 (0.09) | 0.12 | 0.24 (0.22) | 0.35 (0.32) | −0.11 (0.14) | 0.25 |
d9 | 0.33 (0.18) | 0.33 (0.21) | 0.00 (0.06) | 0.07 | 0.14 (0.15) | 0.22 (0.25) | −0.08 (0.14) | 0.24 |
dmean | 0.51 (0.06) | 0.52 (0.06) | −0.01 (0.04) | 0.05 | 0.48 (0.10) | 0.51 (0.06) | −0.03 (0.07) | 0.11 |
Surface area ratio | 5.27 (0.54) | 3.67 (0.28) | 1.60 (0.43) | 1.15 | 4.74 (0.44) | 3.48 (0.19) | 1.26 (0.40) | 0.97 |
Explanatory Variable a | RMSE b | RMSE% c | |
---|---|---|---|
Dominant height (hdom) | |||
LiDAR model | P95, d2 | 1.50 m | 6.26 |
UAV-SfM model | P75, SD of H, d1 | 1.78 m | 7.43 |
Basal area (BA) | |||
LiDAR model | MaxH, d6 | 4.58 m2/ha | 15.82 |
UAV-SfM model | SD of H, P95, dmean | 5.42 m2/ha | 18.74 |
Quadratic mean DBH (Dq) | |||
LiDAR model | MaxH, P10, d1 | 3.75 cm | 11.54 |
UAV-SfM model | P95, d1 | 3.92 cm | 12.07 |
Stem density (N) | |||
LiDAR model | P10, d1, d8 | 76 trees/ha | 22.26 |
UAV-SfM model | SD of H, d1, d8, dmean | 78 trees/ha | 22.67 |
Variable | Coefficient | Standard Error | t Value | p Value |
---|---|---|---|---|
(Intercept) | −0.48 | 1.68 | −0.28 | 0.778 |
Altitude | 0.00 | 0.00 | −0.52 | 0.605 |
Aspect (North) | 0.26 | 0.44 | 0.58 | 0.565 |
Aspect Northeast) | 0.04 | 0.29 | 0.15 | 0.881 |
Aspect (Northwest) | −0.42 | 0.34 | −1.26 | 0.210 |
Aspect (South) | −0.16 | 0.30 | −0.54 | 0.588 |
Aspect Southeast) | 0.14 | 0.29 | 0.49 | 0.625 |
Aspect (Southwest) | −0.18 | 0.28 | −0.65 | 0.515 |
Aspect (West) | −0.14 | 0.31 | −0.47 | 0.643 |
Slope | 0.01 | 0.01 | 1.10 | 0.273 |
MeanH | 0.14 | 0.04 | 3.99 | 0.000 *** |
Canopy cover | −2.85 | 0.90 | −3.17 | 0.002 ** |
Surface area ratio | 0.91 | 0.20 | 4.57 | 0.000 *** |
Compartment 48 | 0.99 | 0.21 | 4.82 | 0.000 *** |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Jayathunga, S.; Owari, T.; Tsuyuki, S. Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning. Remote Sens. 2018, 10, 187. https://doi.org/10.3390/rs10020187
Jayathunga S, Owari T, Tsuyuki S. Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning. Remote Sensing. 2018; 10(2):187. https://doi.org/10.3390/rs10020187
Chicago/Turabian StyleJayathunga, Sadeepa, Toshiaki Owari, and Satoshi Tsuyuki. 2018. "Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning" Remote Sensing 10, no. 2: 187. https://doi.org/10.3390/rs10020187
APA StyleJayathunga, S., Owari, T., & Tsuyuki, S. (2018). Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning. Remote Sensing, 10(2), 187. https://doi.org/10.3390/rs10020187