Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging
Abstract
:1. Introduction
- Quantify thaw slump dynamics, estimate patterns, and volumes of downslope sediment transfer over daily, monthly, and annual time-scales, including an assessment of features that influence road embankment integrity;
- Image permafrost exposures along slump headwalls and construct high resolution stratigraphic models;
- Monitor uplift and settlement caused by the development and degradation of near-surface injection ice adjacent to roads; and
- Track thaw-related evolution at borrow pits developed in ice-rich permafrost terrain.
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition
- (A)
- Description of mega-thaw slumps dynamics at “FM2” [9], involving estimates of volumetric displacement and investigating the processes of thaw-driven sediment transfer;
- (B)
- Quantifying daily and annual flow dynamics associated with slope instability adjacent to the road embankment (site “KM 27 D1”, Dempster Highway).
- (C)
- Describing terrain uplift and settlement in response to injection ice development and degradation adjacent to road embankments (site “KM 213 Caribou Creek”, Dempster Highway).
- (D)
- Development of terrain and stratigraphic models to describe ground-ice conditions and headwall morphometry of large permafrost exposures at slumps “FM2” and “FM3”; and
- (E)
- Differencing UAV-derived digital terrain models to track the thaw-related evolution of anthropogenically disturbed terrain (Site “PW10”, borrow pit).
2.2.1. Unmanned Aerial Vehicle Equipment and Surveys
2.2.2. Global Navigation Satellite System (GNSS) Surveys
2.2.3. Airborne Laser Scanning (ALS)
2.3. Data Processing
2.3.1. GNSS Datasets
2.3.2. UAV Datasets
2.3.3. ALS Dataset and Reconstruction of Disturbed Terrain
2.3.4. Change Detection
2.4. Reference Data
3. Results
3.1. UAV Accuracy Assessments
3.2. Thaw Slump Dynamics
3.2.1. Mega Slumps
3.2.2. Thaw Slumps and Road Infrastructure
3.3. Deriving Digital Stratigraphic Models from Permafrost Headwall Exposures
3.4. Monitoring Injection Ice Development and Degradation Adjacent to Road Embankments
3.5. Terrain Models to Track Thaw Related Evolution of Anthropogenically Disturbed Terrain
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
RMSE of CPs (m) | RMSE of CPs (GSD) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Flight ID | Site | Area (ha) | CPs | Elevation Range (m) | Res (m) | X | Y | Z | X | Y | Z |
7/10 | FM2/FM3 | 365 | 28 | 240 | 0.033 | 0.042 | 0.036 | 0.130 | 1.3 | 1.1 | 3.9 |
18 | PW10 | 56 | 11 | 52 | 0.031 | 0.016 | 0.024 | 0.022 | 0.5 | 0.8 | 0.7 |
19 | PW10 | 59 | 4 | 51 | 0.034 | 0.010 | 0.009 | 0.030 | 0.3 | 0.3 | 0.9 |
22 | Pit 174 | 50 | 5 | 32 | 0.028 | 0.027 | 0.021 | 0.071 | 1.0 | 0.8 | 2.5 |
24 | Husky | 101 | 7 | 103 | 0.034 | 0.014 | 0.038 | 0.083 | 0.4 | 1.1 | 2.4 |
26 | I401A | 83 | 12 | 62 | 0.030 | 0.029 | 0.027 | 0.046 | 1.0 | 0.9 | 1.5 |
28 | I401A | 80 | 6 | 64 | 0.028 | 0.027 | 0.011 | 0.023 | 1.0 | 0.4 | 0.8 |
Total | 792 | 73 | |||||||||
Average | 86 | 0.031 | 0.024 | 0.024 | 0.041 | 0.8 | 0.8 | 1.8 | |||
Stdev | 71 | 0.003 | 0.011 | 0.011 | 0.027 | 0.4 | 0.3 | 1.2 |
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Year | Platform | Sensor | ||||
---|---|---|---|---|---|---|
Model * | Make | Flight Planning & Control Station | Camera & Lens | Size | MP | |
2015 | Spyder PX8 Plus | Bradatech | Mission Planner v1.3 | Sony a6000 & f/2.8 Sony 20 mm pancake | APS-C | 24 |
2016 | RX4-S Surveyor | Bradatech | Mission Planner v1.3 | Sony RX100 III & f/1.8 Zeiss 24 mm | 1 in | 20 |
Inspire 1 Pro | DJI | Litchi for DJI app | Zenmuse X5 (FC550) & f/1.7 MFT 15 mm | Micro 4/3 | 16 | |
2017 | eBee Plus RTK/PPK | Sensefly | eMotion 3 | Sensefly S.O.D.A & f/2.8 Sensefly 28 mm Sensefly ThermoMAP thermal camera | 1 in n/a | 20 0.3 |
Phantom 4 Pro | DJI | DJI GS Pro app | DJI FC6310 & f/2.8 DJI 24 mm | 1 in | 20 |
ID | Site | Date | UAV | Flights | AGL (m) | Res (cm) | Area (ha) | Photos (no.) | Overlap forward/Side (%) | GCPs/CPs | Notes |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | D1 | 28 July 2015 | PX8 | 1 | 70 | 1.5 | 4.3 | 295 | 90/83 | 4/0 | |
2 | D1 | 29 July 2015 | PX8 | 1 | 70 | 1.5 | 3.8 | 291 | 90/83 | 4/0 | |
3 | D1 | 3 August 2016 | Inspire | 1 | 55 | 1.3 | 1.7 | 79 | 83/75 | 4/0 | |
4 | D1 | 27 July 2017 | P4P | 1 | 40 | 1.2 | 6.3 | 316 | 90/85 | 5/10 | |
5 | FM3 | 29 July 2015 | PX8 | 2 | 90 | 1.9 | 28.3 | 658 | 83/75 | 4/0 | |
6 | FM3 | 2 August 2016 | Inspire | 3 | 90 | 2.4 | 34.3 | 583 | 83/75 | 8/49 * | |
7 | FM3 | 26/28 July 2017 | eBee | 5 | 120 | 3.3 | 365.0 | 3499 | 80/80 | 6/22 | |
8 | FM3 | 28 July 2017 | P4P | 1 | 6 + 15 | 0.4 | 0.35 | 208 | Every 2 s | - | Headwall |
9 | FM2 | 2 August 2016 | Inspire | 8 | 90 | 2.4 | 83.6 | 1516 | 83/75 | 17/0 | |
10 | FM2 | 26/28 July 2017 | eBee | 5 | 120 | 3.3 | 365.0 | 3499 | 80/80 | 6/22 | Part of ID:7 |
11 | FM2 | 26 July 2017 | eBee | 1 | 120 | 28.5 | 92.0 | 1894 | 90/75 | 0/0 | Thermal |
12 | FM2 | 26 July 2017 | P4P | 1 | 20 + 30 | 1.25 | 1.0 | 113 | Every 2 | - | Headwall |
13 | KM213 | 7 August 2016 | RX4 | 1 | 40 | 1.0 | 2.0 | 85 | 85/70 | 4/0 | |
14 | KM213 | 29 July 2017 | P4P | 1 | 50 | 1.3 | 4.5 | 459 | 90/85 | 7/57 * | |
15 | KM213 | 5 September 2017 | P4P | 1 | 50 | 1.3 | 4.5 | 489 | 90/85 | 7/25 * | |
16 | PW10 | 9 August 2016 | RX4S | 1 | 90 | 2.6 | 10.4 | 194 | 93/80 | 5/0 | |
17 | PW10 | 28 September 2016 | RX4S | 2 | 90 | 2.8 | 32.6 | 488 | 90/65 | 5/0 | |
18 | PW10 | 13 June 2017 | eBee | 1 | 120 | 3.1 | 55.5 | 511 | 80/80 | 4/7 | |
19 | PW10 | 10 September 2017 | eBee | 1 | 120 | 3.4 | 58.6 | 580 | 80/80 | 4/0 | |
20 | Pit 174 | 9 August 2016 | RX4S | 2 | 90 | 2.6 | 34.6 | 663 | 93/65 | 4/0 | |
21 | Pit 174 | 28 September 2016 | RX4S | 2 | 90 | 2.5 | 37.8 | 541 | 90/65 | 5/0 | |
22 | Pit 174 | 10 September 2017 | eBee | 1 | 98 | 2.8 | 50.0 | 467 | 80/70 | 5/0 | |
23 | Husky | 3 August 2016 | Inspire | 2 | 75 | 1.7 | 36.8 | 773 | 83/75 | 4/0 | |
24 | Husky | 28 July 2017 | eBee | 1 | 119 | 3.4 | 100.5 | 723 | 80/70 | 3/4 | |
25 | Husky | 28 July 2017 | eBee | 1 | 120 | 29.0 | 110.8 | 2124 | 90/75 | 0/0 | Thermal |
26 | I401A | 10 June 2017 | eBee | 1 | 119 | 3.0 | 82.8 | 675 | 80/75 | 4/8 | |
27 | I401A | 10 June 2017 | eBee | 1 | 117 | 26.5 | 93.1 | 844 | 80/75 | 0/0 | Thermal |
28 | I401A | 30 July 2017 | eBee | 1 | 119 | 2.8 | 79.5 | 682 | 80/75 | 4/2 | |
29 | CRB | 28 July 2015 | PX8 | 2 | 70 | 1.5 | 12.1 | 711 | 83/75 | 4/0 | |
Total | 47 | 1427 | 20,461 | 121/184 |
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Van der Sluijs, J.; Kokelj, S.V.; Fraser, R.H.; Tunnicliffe, J.; Lacelle, D. Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging. Remote Sens. 2018, 10, 1734. https://doi.org/10.3390/rs10111734
Van der Sluijs J, Kokelj SV, Fraser RH, Tunnicliffe J, Lacelle D. Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging. Remote Sensing. 2018; 10(11):1734. https://doi.org/10.3390/rs10111734
Chicago/Turabian StyleVan der Sluijs, Jurjen, Steven V. Kokelj, Robert H. Fraser, Jon Tunnicliffe, and Denis Lacelle. 2018. "Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging" Remote Sensing 10, no. 11: 1734. https://doi.org/10.3390/rs10111734
APA StyleVan der Sluijs, J., Kokelj, S. V., Fraser, R. H., Tunnicliffe, J., & Lacelle, D. (2018). Permafrost Terrain Dynamics and Infrastructure Impacts Revealed by UAV Photogrammetry and Thermal Imaging. Remote Sensing, 10(11), 1734. https://doi.org/10.3390/rs10111734