A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology
Abstract
:1. Introduction
- (A)
- Development of a new systematic framework for optimization of multi-temporal terrestrial LiDAR surveys;
- (B)
- Application and validation of performance of the developed systematic framework over chosen complex gully morphology;
- (C)
- Use of data collected through carried multi-temporal TLS surveys for detection of soil erosion induced STCs.
2. Materials and Methods
2.1. Study Area
2.2. Systematic Optimization of Multi-Temporal TLS Surveys
- Survey planning phase
- 2.
- Field preparation phase
- 3.
- Multi-temporal field LiDAR surveys
- 4.
- Data processing and model creation
2.3. Validation of Performance of Developed Framework in Comparison to Non-Systematic TLS Survey Approach
2.4. Detection of Spatio-Temporal Changes within Gully Headcut
3. Results
3.1. Comparison between Planned and Achieved Coverage of Study Area with Optimized TLS Surveys
3.2. Comparison with Coverage Achieved through the Non-Systematic TLS Survey Approach
3.3. Detected Spatio-Temporal Changes within Gully Headcut (SA-2)
4. Discussion
4.1. Advantages of the Developed Framework over the Non-Systematic TLS Survey Approach
4.2. Geomorphic Implications of the Detected Erosion Induced STCs
5. Conclusions
- (1)
- The developed systematic framework significantly facilitated the implementation of multi-temporal TLS surveys over complex gully morphology. Although initially the systematic framework requires detailed planning and field preparation, later implementation of actual field TLS surveys is considerably facilitated.
- (2)
- Deviation of achieved coverage of multi-temporal TLS surveys from planned coverage was minimal within both the whole gully and the chosen part of the complex gully headcut, thus confirming the accuracy of the created survey plan and achieved repeatability of multi-temporal surveys.
- (3)
- A small increase in achieved deviation between planned coverage and achieved coverage was noticed between the two carried out surveys, which could be related to the rapid erosion induced STCs.
- (4)
- Most intense erosion induced STCs were detected within a one-year observed period within the gully headcut, where mass wasting resulted in the gradual uphill retreat of the headcut.
- (5)
- The developed systematic framework is applicable to all studies that require multi-temporal TLS surveys for monitoring of STCs over complex geomorphic features.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Survey Positions 1 | Gully Coverage (m2) | Percentage (%) | Gully Headcut Coverage (m2) | Percentage (%) |
---|---|---|---|---|
TLS position 1 | 566.96 | 46.21 | 167.14 | 83.89 |
TLS position 2 | 578.94 | 47.18 | 157.37 | 78.99 |
TLS position 3 | 410.50 | 33.46 | 99.93 | 50.16 |
TLS position 4 | 547.85 | 44.65 | 80.23 | 40.27 |
TLS position 5 | 521.01 | 42.46 | - | - |
TLS position 6 | 382.25 | 31.15 | - | - |
TLS position 7 | 540.96 | 44.09 | - | - |
TLS position 8 | 381.63 | 31.10 | - | - |
Total coverage | 1190.04 | 96.99 | 193.49 | 97.12 |
Survey Positions | Planned Coverage | Achieved Coverage | ||
---|---|---|---|---|
Gully Coverage (m2) | Percentage (%) | Gully Coverage (m2) | Percentage (%) | |
TLS position 1 | 566.96 | 46.21 | 540.47 | 44.05 |
TLS position 2 | 578.94 | 47.18 | 498.81 | 40.65 |
TLS position 3 | 410.50 | 33.46 | 407.18 | 33.19 |
TLS position 4 | 547.85 | 44.65 | 471.72 | 38.45 |
TLS position 5 | 521.01 | 42.46 | 439.18 | 35.79 |
TLS position 6 | 382.25 | 31.15 | 361.94 | 29.50 |
TLS position 7 | 540.96 | 44.09 | 414.11 | 33.75 |
TLS position 8 | 381.63 | 31.10 | 311.86 | 25.42 |
Total coverage | 1190.04 | 96.99 | 1181.07 | 96.26 |
Gully Headcut | TLS Positions | Total Area (m2) | No of Points | Covered Area (m2) | Covered Area (%) |
---|---|---|---|---|---|
Planned coverage | P1–P4 | 199.23 | - | 197.43 | 99.10 |
TLS survey 1 | P1–P4 | 199.23 | 25,875,206 | 195.70 | 98.23 |
TLS survey 2 | P1–P4 | 199.23 | 2,665,029 | 193.49 | 97.12 |
Survey | Point Cloud Characteristics | |||
---|---|---|---|---|
Total Number of Points | Average Point Density (No/m2) | SD (RMSE) | Mean TLS Survey Error | |
TLS-2019 | 25,875,206 | 92,411.45 | 0.4 (0.7) cm | 1.29 cm |
TLS-2020 | 2,665,029 | 95,179.63 | 1.5 (1.8) cm | 1.37 cm |
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Domazetović, F.; Šiljeg, A.; Marić, I.; Panđa, L. A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology. Remote Sens. 2022, 14, 3366. https://doi.org/10.3390/rs14143366
Domazetović F, Šiljeg A, Marić I, Panđa L. A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology. Remote Sensing. 2022; 14(14):3366. https://doi.org/10.3390/rs14143366
Chicago/Turabian StyleDomazetović, Fran, Ante Šiljeg, Ivan Marić, and Lovre Panđa. 2022. "A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology" Remote Sensing 14, no. 14: 3366. https://doi.org/10.3390/rs14143366