Empirical Evaluation and Simulation of GNSS Solutions on UAS-SfM Accuracy for Shoreline Mapping
Abstract
:1. Introduction
2. Study Purpose and Objectives
- Evaluation of PPK and brief experimentation with RTN and PPP GNSS trajectory solutions as alternatives to GCPs for the direct georeferencing of UAS-SfM image locations and derived mapping products;
- Assessment of UAS-SfM data product vertical accuracy as a result of PPK GNSS sample rate, fix percentage, and baseline distance;
- Comparison of UAS-SfM-derived products against ground control data acquired using other surveying techniques (i.e., RTN GNSS, total station, and geodetic-grade terrestrial light detection and ranging (lidar));
- Application of Monte Carlo simulation techniques to examine the impact of GNSS quality on the vertical accuracy of UAS-SfM-derived point clouds.
3. Study Sites
4. Methodology
4.1. Mustang Island State Park Field Experiment
4.1.1. Hardware
4.1.2. Software
4.1.3. UAS Flight Designs
4.1.4. Ground Truth
4.1.5. Data Processing and GNSS Evaluation
- Test 1: UAS-SfM vertical accuracy assessment when using RTN GNSS to correct the location of Phantom 4 imagery (flown in RTK mode).
- Test 2: Examination of UAS-SfM vertical accuracies when using PPK GNSS to correct the location of images from the Phantom 4 (flown in PPK mode) and the two Wingtra flights (i.e., 75 m AGL and 120 m AGL).
- Test 2.1: Influence of PPK base station distance on UAS-SfM vertical accuracy, using imagery from the Wingtra at 120 m AGL.
- Test 2.2: Effects of different GNSS sample rates on PPK corrections (i.e., 1 s, 5 s, 15 s, and 30 s), using imagery from the Wingtra at 120 m AGL.
- Test 2.3: Influence of the PPK fix percentage on the accuracy of UAS-SfM mapping products, using imagery from the Wingtra at 120 m AGL.
- Test 3: Assessment of UAS-SfM vertical accuracies when using PPP GNSS to correct image geotags, using imagery from the two Wingtra flights.
- Test 4: Comparison between UAS-SfM-derived DSMs (using the Wingtra-generated DSM at 75 m AGL) and point heights extracted from cross-shore transects measured using RTN GNSS.
- Test 5: Comparison between DSMs generated using UAS-SfM (i.e., Wingtra at 75 m AGL) versus DSMs generated using the geodetic-grade Reigl VZ-2000i TLS.
- Test 6: Supplementary processing of Wingtra data without additional corrections of image geotags for comparison against RTN, PPK, and PPP GNSS.
4.2. McNary Field and Neptune State Scenic Area Field Experiments
4.2.1. Hardware
4.2.2. Software
4.2.3. UAS Flight Designs
4.2.4. Ground Truth
4.2.5. Data Processing and GNSS Analyses
4.3. Simulated Tests (simUAS)
4.4. Accuracy Evaluation and SfM Processing
5. Results
5.1. GNSS Evaluation Results—Mustang Island State Park
5.2. GNSS Evaluation Results—McNary Field and Neptune State Scenic Area Field Experiments
5.3. Simulation Results (simUAS)—Monte Carlo
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Platform | Wingtra | Wingtra | Phantom 4 | Phantom 4 |
---|---|---|---|---|
Flight mode | PPK #1 | PPK #2 | RTN | PPK |
Number of photos | 271 | 120 | 610 | 610 |
Flying height AGL (m) | 75 | 120 | 59 | 59 |
GSD (cm/px) | 1.0 | 1.6 | 1.6 | 1.6 |
Flight | 80% sidelap | 80% sidelap | Double | Double |
design | 70% endlap | 70% endlap | grid | grid |
Min (cm) | Max (cm) | Mean (cm) | Median (cm) | Sigma (cm) | |
---|---|---|---|---|---|
1 s | 2.44 | 22.16 | 8.35 | 4.31 | 7.23 |
5 s | 2.73 | 8.79 | 4.77 | 3.78 | 2.44 |
15 s | 1.38 | 8.81 | 4.54 | 4.89 | 2.79 |
30 s | 1.87 | 8.36 | 4.37 | 3.21 | 2.43 |
Min (cm) | Max (cm) | Mean (cm) | Median (cm) | Sigma (cm) | |
---|---|---|---|---|---|
1 s | 2.44 | 128.42 | 40.91 | 26.44 | 37.32 |
5 s | 2.73 | 68.29 | 28.17 | 24.83 | 20.05 |
15 s | 1.38 | 121.36 | 29.49 | 20.55 | 27.51 |
30 s | 1.87 | 79.83 | 24.18 | 16.93 | 21.48 |
Survey Mode | PPK Sample Rate (s) | Mean (cm) | Sigma (cm) | RMSEz (cm) |
---|---|---|---|---|
Total station | 1 | −8.74 | 1.42 | 8.85 |
RTN GNSS | 1 | −8.32 | 2.42 | 8.66 |
Total station | 30 | −8.26 | 1.31 | 8.36 |
RTN GNSS | 30 | −7.84 | 2.27 | 8.16 |
Transect 1 | Transect 2 | Transect 3 | Transect 4 | |
---|---|---|---|---|
Mean of differences (m) | 0.080 | 0.055 | 0.072 | 0.093 |
Std. dev. of differences (m) | 0.018 | 0.047 | 0.021 | 0.014 |
RMSEz (m) | 0.082 | 0.073 | 0.075 | 0.094 |
Processing Mode | Dataset | RMSEz × 1.96 (cm) | Result |
---|---|---|---|
RTN | Phantom 4 (RTN mode) | 14.25 | Pass |
PPK (Local Base, 1 s) | Phantom 4 (PPK mode) | 16.54 | Pass |
PPK (Local Base, 1 s) | WingtraOne (75 m AGL) | 10.37 | Pass |
PPK (Local Base, 1 s) | Wingtra (120 m AGL) | 17.35 | Pass |
PPP | Wingtra (75 m AGL) | 64.41 | Fail |
PPP | Wingtra (120 m AGL) | 177.97 | Fail |
Autonomous | Wingtra (75 m AGL) | 1864.76 | Fail |
Autonomous | Wingtra (120 m AGL) | 3070.38 | Fail |
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Pilartes-Congo, J.A.; Simpson, C.; Starek, M.J.; Berryhill, J.; Parrish, C.E.; Slocum, R.K. Empirical Evaluation and Simulation of GNSS Solutions on UAS-SfM Accuracy for Shoreline Mapping. Drones 2024, 8, 646. https://doi.org/10.3390/drones8110646
Pilartes-Congo JA, Simpson C, Starek MJ, Berryhill J, Parrish CE, Slocum RK. Empirical Evaluation and Simulation of GNSS Solutions on UAS-SfM Accuracy for Shoreline Mapping. Drones. 2024; 8(11):646. https://doi.org/10.3390/drones8110646
Chicago/Turabian StylePilartes-Congo, José A., Chase Simpson, Michael J. Starek, Jacob Berryhill, Christopher E. Parrish, and Richard K. Slocum. 2024. "Empirical Evaluation and Simulation of GNSS Solutions on UAS-SfM Accuracy for Shoreline Mapping" Drones 8, no. 11: 646. https://doi.org/10.3390/drones8110646
APA StylePilartes-Congo, J. A., Simpson, C., Starek, M. J., Berryhill, J., Parrish, C. E., & Slocum, R. K. (2024). Empirical Evaluation and Simulation of GNSS Solutions on UAS-SfM Accuracy for Shoreline Mapping. Drones, 8(11), 646. https://doi.org/10.3390/drones8110646