Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Field (Ground-Truth) Data
2.3. UAS Data
2.4. Digital Terrain Model (DTM) Data
3. Methods
3.1. UAS Image Orientation
- The InSO method based on image tie-points and 5 irregularly distributed GCPs;
- The GNSS-SO1 method based on tie-points, 5 GCPs and non-PPK single-frequency carrier-phase GNSS data (absolute positioning);
- The GNSS-SO2 method based on tie-points, 5 GCPs and using PPK dual-frequency carrier-phase GNSS data (relative positioning).
3.2. UAS Point Cloud Generation
3.3. Extraction and Calculation of Point Cloud (PC) Metrics
3.4. Generation and Validation of Lorey’s Mean Height (HL) Models
- The validation over the independent validation dataset of 33 plots (HV), which was not used to derive the models. The HL estimates from the developed models were compared with corresponding field data and evaluated by means of adjusted coefficients of determination (R2adj), mean error (ME) (Equation (2)), relative mean error (ME%) (Equation (3)), root mean square error (RMSE) (Equation (4)), and relative root mean square error (RMSE%) (Equation (5)):
- The LOOCV statistical method [46,47], based on 66 sample plots, was used for the model’s development. LOOCV is the iterative procedure of n iterations, where n is the number of all measurements (field plots). In n iterations (n = 66), one measurement was removed from the dataset and the selected model wasfitted using the remaining n-1 measurements. The model was then validated using the removed measurement. After the process of n-1 iterations was done, the model accuracy was estimated by averaging validation results (residuals) from all iterations.
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Forest Attribute | Minimum | Maximum | Mean | Standard Deviation |
---|---|---|---|---|
Age (years) | 43 | 148 | 86 | 41 |
Mean dbh (cm) | 17.0 | 69.4 | 32.8 | 14.0 |
Lorey’s mean height (m) | 18.2 | 37.9 | 26.3 | 5.2 |
Stem density (trees∙ha−1) | 56 | 1840 | 534 | 375 |
Basal area (m2∙ha−1) | 13.7 | 56.4 | 29.9 | 7.6 |
Volume (m3∙ha−1) | 158.0 | 963.5 | 398.8 | 149.3 |
UAS | Trimble UX5 HP |
---|---|
Type | Fixed wing |
Weight | 2.4 kg |
Wingspan | 1 m |
Battery | 14.8 V, 6600 mAh |
Endurance | 40 min |
Camera | Sony ILCE-7R |
Sensor size | Full Frame (35.9 × 24 mm) |
Field of view | W 55°, H 37° |
Image size | 7360 × 4912 |
Focal length | 35 mm |
GNSS receiver | Dual-frequency L1/L2 (GPS, Glonass, Beidou, Galileo ready) |
Flight | Mean Error | Mean Absolute Error | Median Absolute Deviation | ||||||
---|---|---|---|---|---|---|---|---|---|
E (m) | N (m) | h (m) | E (m) | N (m) | h (m) | E (m) | N (m) | h (m) | |
First | −0.45 | −1.35 | 0.80 | 3.28 | 1.35 | 0.87 | 2.93 | 0.13 | 0.40 |
Second | −1.06 | 0.23 | 3.04 | 2.94 | 0.26 | 3.04 | 2.58 | 0.12 | 0.47 |
Variable Group | Variable (Abbreviation and Description) |
---|---|
Height metrics | hmin—minimum height; hmax—maximum height; hmean—mean height; hmode—mode height |
Height variability metrics | SD—standard deviation; VAR—variance; CV—coefficient of variation; IQ—interquartile distance; Skew—skewness; Kurt—kurtosis; AAD—average absolute deviation; MADmed—Median of the absolute deviations from the overall median; MADmode—Median of the absolute deviations from the overall mode; CRR—Canopy relief ratio ((mean − min)/(max − min)); SQRTmeanSQ—Generalized mean for the 2nd power (Elevation quadratic mean); CURTmeanCUBE—Generalized mean for the 3nd power (Elevation cubic mean); L1, L2, L3, L4—L moments; LCV—moment coefficient of variation; Lskew—moment skewness; Lkurt—moment kurtosis |
Height percentiles | Ph (h = 1st, 5th, 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th, 95th, 99th percentiles) |
Canopy cover metrics | CCh—percentage of points above h (h = 2 m, 5 m, 10 m, 15 m, 20 m, 25 m, 30 m, mean, mode) |
Orientation Method | Model |
---|---|
InSO | HL = 3.085 + 0.485·hmax + 0.641·AAD + 0.291·SQRTmeanSQ |
GNSS-SO1 | HL = 2.365 + 0.529·hmax + 0.511·CURTmeanCUBE + (−6.202)·Lkurt + (−0.180)·P5 |
GNSS-SO2 | HL = 0.864 + 0.298·SD + 0.880·P95 |
Dataset | Orientation Method | R2adj | RMSE (m) | RMSE% (%) | ME (m) | ME% (%) |
---|---|---|---|---|---|---|
Modeling dataset | InSO | 0.943 | 1.182 | 4.488 | < 0.001 | < 0.001 |
GNSS-SO1 | 0.953 | 1.067 | 4.050 | < 0.001 | < 0.001 | |
GNSS-SO2 | 0.958 | 1.024 | 3.888 | < 0.001 | < 0.001 | |
Validation (HV) | InSO | 0.921 | 1.458 | 5.551 | 0.169 | 0.643 |
GNSS-SO1 | 0.925 | 1.403 | 5.344 | 0.167 | 0.637 | |
GNSS-SO2 | 0.935 | 1.361 | 5.183 | 0.068 | 0.259 | |
Validation (LOOCV) | InSO | 0.937 | 1.276 | 4.843 | −0.015 | −0.056 |
GNSS-SO1 | 0.948 | 1.150 | 4.365 | −0.007 | −0.026 | |
GNSS-SO2 | 0.955 | 1.069 | 4.057 | 0.001 | 0.004 |
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Jurjević, L.; Gašparović, M.; Milas, A.S.; Balenović, I. Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes. Remote Sens. 2020, 12, 404. https://doi.org/10.3390/rs12030404
Jurjević L, Gašparović M, Milas AS, Balenović I. Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes. Remote Sensing. 2020; 12(3):404. https://doi.org/10.3390/rs12030404
Chicago/Turabian StyleJurjević, Luka, Mateo Gašparović, Anita Simic Milas, and Ivan Balenović. 2020. "Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes" Remote Sensing 12, no. 3: 404. https://doi.org/10.3390/rs12030404
APA StyleJurjević, L., Gašparović, M., Milas, A. S., & Balenović, I. (2020). Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes. Remote Sensing, 12(3), 404. https://doi.org/10.3390/rs12030404