UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Sites
2.2. ALS, UAV, and Airborne Imagery Data Acquisition
2.3. Processing of Input Remote Sensing Data
2.4. Normalized Digital Surface Model (NDSM) Calculation
2.5. Woody Plant Structure Analysis
2.6. Statistical Analysis
3. Results
3.1. The Number of Detected Woody Plant Individuals
3.2. Woody Vegetation Height Detection
4. Discussion
4.1. The Number of Detected Woody Plant Individuals
4.2. Woody Vegetation Height Detection
4.3. Tree vs. Shrub Height Accuracy and Detection Success
4.4. UAV-Based DSM and ALS-Based DTM Fusion
4.5. Landscape/Vegetation Patterns Classification
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
ALSHD | AImg | AImg-ALSLD | AImg-ALSHD | UAV | UAV-ALSLD | UAV-ALSHD | ||
---|---|---|---|---|---|---|---|---|
Site 01 | Detected | 1183 | 596 | 799 | 802 | 1164 | 1018 | 1138 |
Reference | 1214 | 1214 | 1214 | 1214 | 1214 | 1214 | 1214 | |
True | 1128 | 499 | 612 | 610 | 840 | 790 | 833 | |
Omission | 86 | 715 | 602 | 604 | 374 | 424 | 381 | |
Commission | 55 | 97 | 187 | 192 | 324 | 228 | 305 | |
Accuracy (%) | 92.9 | 41.1 | 50.4 | 50.3 | 69.2 | 65.1 | 68.6 | |
Site 02 | Detected | 976 | 508 | 653 | 636 | 815 | 849 | 828 |
Reference | 946 | 946 | 946 | 946 | 946 | 946 | 946 | |
True | 898 | 444 | 468 | 447 | 617 | 656 | 661 | |
Omission | 48 | 502 | 478 | 499 | 329 | 290 | 285 | |
Commission | 78 | 64 | 185 | 189 | 198 | 193 | 167 | |
Accuracy (%) | 94.9 | 46.9 | 49.5 | 47.2 | 65.2 | 69.3 | 69.9 | |
Site 03 | Detected | 1256 | 369 | 949 | 886 | 1096 | 1001 | 1224 |
Reference | 1215 | 1215 | 1215 | 1215 | 1215 | 1215 | 1215 | |
True | 1200 | 301 | 653 | 628 | 787 | 780 | 914 | |
Omission | 15 | 914 | 562 | 587 | 428 | 435 | 301 | |
Commission | 56 | 68 | 296 | 258 | 309 | 221 | 310 | |
Accuracy (%) | 98.8 | 24.8 | 53.7 | 51.7 | 64.8 | 64.2 | 75.2 | |
Site 04 | Detected | 1016 | 603 | 799 | 851 | 1133 | 1582 | 1304 |
Reference | 1023 | 1023 | 1023 | 1023 | 1023 | 1023 | 1023 | |
True | 972 | 430 | 551 | 552 | 688 | 705 | 669 | |
Omission | 51 | 593 | 472 | 471 | 335 | 318 | 354 | |
Commission | 44 | 173 | 248 | 299 | 445 | 877 | 635 | |
Accuracy (%) | 95.0 | 42.0 | 53.9 | 54.0 | 67.3 | 68.9 | 65.4 | |
Site 05 | Detected | 842 | 464 | 775 | 633 | 975 | 851 | 791 |
Reference | 825 | 825 | 825 | 825 | 825 | 825 | 825 | |
True | 794 | 370 | 472 | 460 | 669 | 636 | 629 | |
Omission | 31 | 455 | 353 | 365 | 156 | 189 | 196 | |
Commission | 48 | 94 | 303 | 173 | 306 | 215 | 162 | |
Accuracy (%) | 96.2 | 44.8 | 57.2 | 55.76 | 81.1 | 77.1 | 76.2 | |
Site 06 | Detected | 853 | 436 | 442 | 631 | 852 | 959 | 943 |
Reference | 831 | 831 | 831 | 831 | 831 | 831 | 831 | |
True | 767 | 383 | 368 | 415 | 555 | 588 | 584 | |
Omission | 64 | 448 | 463 | 416 | 276 | 243 | 247 | |
Commission | 86 | 53 | 74 | 216 | 297 | 371 | 359 | |
Accuracy (%) | 92.3 | 46.1 | 44.2 | 49.9 | 66.8 | 70.8 | 70.3 | |
Overall | Detected | 6126 | 2976 | 4237 | 4439 | 6035 | 6260 | 6228 |
Reference | 6054 | 6054 | 6054 | 6054 | 6054 | 6054 | 6054 | |
True | 5759 | 2427 | 3124 | 3112 | 4156 | 4155 | 4209 | |
Omission | 295 | 3627 | 2988 | 2942 | 1898 | 1899 | 1764 | |
Commission | 367 | 549 | 1171 | 1327 | 1879 | 2105 | 1938 | |
Accuracy (%) | 95.1 | 40.1 | 51.6 | 51.4 | 68.7 | 68.6 | 70.9 |
Appendix B
Appendix C
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Site | Elevation (m amsl) * | Mean Slope (°) | Woody Vegetation Cover (%) ** | Woody Vegetation Height (m) *** | Total Area (ha) |
---|---|---|---|---|---|
Site 01 | 555–645 | 9.6 (6.6) | 33.2 | 7.5 (6.4) | 66.5 |
Site 02 | 579–635 | 6.8 (5.4) | 28.5 | 5.6 (4.6) | 36.7 |
Site 03 | 513–587 | 9.6 (5.2) | 20.9 | 3.8 (2.1) | 31.9 |
Site 04 | 423–492 | 7.8 (7.0) | 27.8 | 8.4 (6.4) | 62.6 |
Site 05 | 684–742 | 6.2 (3.4) | 26.4 | 5.9 (4.2) | 68.4 |
Site 06 | 653–745 | 7.2 (3.5) | 33.9 | 9.4 (6.0) | 75.4 |
Remote Sensing Data | Date of Acquisition | Resolution | Data Type | Data Extent (km2) |
---|---|---|---|---|
Airborne Laser Scanning—HD | 17 September 2016 | 0.21 m 20 pts/m2 | Elevation Raster Point Cloud | 216 |
Airborne Imagery | 17 September 2016 | 0.94 m 40 pts/m2 | High resolution images Point Cloud | 216 |
Airborne Laser Scanning—LD | March 2011; available since 2016 | 2 m 1–2 pts/m2 | Elevation Raster Point Cloud | 78,000 |
Unmanned Aerial Vehicle | 27 June 2016 | 0.15 m 260 pts/m2 | Very high resolution images Point Cloud | 3.5 |
Name of NDSM | DSM | DTM | List of Acronyms |
---|---|---|---|
ALSHD | ALSHD | ALSHD | ALSHD: Airborne Laser Scanning—High Density ALSLD: Airborne Laser Scanning—Low Density AImg: Airborne Imagery UAV: Unmanned Aerial Vehicle |
AImg | AImg | AImg | |
AImg-ALSLD | AImg | ALSLD | |
AImg-ALSHD | AImg | ALSHD | |
UAV | UAV | UAV | |
UAV-ALSLD | UAV | ALSLD | |
UAV-ALSHD | UAV | ALSHD |
Site | ALSHD | AImg | AImg-ALSLD | AImg-ALSHD | UAV | UAV-ALSLD | UAV-ALSHD | |
---|---|---|---|---|---|---|---|---|
Site 01 | Apparent success rate | 97.5 | 49.1 | 65.8 | 66.1 | 95.9 | 83.9 | 93.7 |
Adjusted success rate | 92.9 | 41.1 | 50.4 | 50.3 | 69.2 | 65.1 | 68.6 | |
Site 02 | Apparent success rate | 103.2 | 53.7 | 69 | 67.2 | 86.2 | 89.8 | 87.5 |
Adjusted success rate | 94.9 | 46.9 | 49.5 | 47.3 | 65.2 | 69.3 | 69.9 | |
Site 03 | Apparent success rate | 103.4 | 30.4 | 78.1 | 72.9 | 90.2 | 82.4 | 100.7 |
Adjusted success rate | 98.8 | 24.8 | 53.7 | 51.7 | 64.8 | 64.2 | 75.2 | |
Site 04 | Apparent success rate | 99.3 | 58.9 | 78.1 | 83.2 | 110.8 | 154.7 | 127.5 |
Adjusted success rate | 95 | 42 | 53.9 | 54 | 67.3 | 68.9 | 65.4 | |
Site 05 | Apparent success rate | 102.1 | 56.2 | 93.9 | 76.7 | 118.2 | 103.2 | 95.9 |
Adjusted success rate | 96.2 | 44.9 | 57.2 | 55.8 | 81.1 | 77.1 | 76.2 | |
Site 06 | Apparent success rate | 102.7 | 52.5 | 53.2 | 75.9 | 102.5 | 115.4 | 113.5 |
Adjusted success rate | 92.3 | 46.1 | 44.3 | 49.9 | 66.8 | 70.8 | 70.3 | |
Overall | Apparent success rate | 101.2 | 49.2 | 73 | 73.3 | 99.7 | 103.4 | 102.87 |
Adjusted success rate | 95.1 | 40.1 | 51.6 | 51.4 | 68.7 | 68.6 | 70.9 |
Site | AImg | AImg-ALSLD | AImg-ALSHD | UAV | UAV-ALSLD | UAV-ALSHD |
---|---|---|---|---|---|---|
Site 01 | 2.2/28.9 | 1.3/16.8 | 1.3/16.9 | 1.6/20.8 | 0.8/10.7 | 0.8/10.6 |
Site 02 | 2.2/35.2 | 1.2/18.3 | 1.2/18.5 | 1.3/20.5 | 0.6/9.8 | 0.6/9.4 |
Site 03 | 2.9/47.4 | 1.6/26.3 | 1.2/25.0 | 1.5/23.7 | 0.8/12.6 | 0.7/11.6 |
Site 04 | 3.6/31.3 | 1.5/13.3 | 1.5/13.2 | 2.2/18.7 | 1.5/13.3 | 1.6/13.5 |
Site 05 | 1.9/25.0 | 1.0/12.9 | 0.9/12.2 | 0.9/12.2 | 0.9/12.1 | 0.9/11.4 |
Site 06 | 2.2/21.4 | 1.8/17.5 | 1.7/16.3 | 2.1/20.8 | 1.4/13.2 | 1.3/12.9 |
Overall | 2.5/31.5 | 1.4/17.5 | 1.4/17.0 | 1.6/19.5 | 1.0/11.9 | 1.0/11.5 |
AImg | AImg-ALSLD | AImg-ALSHD | UAV | UAV-ALSLD | UAV-ALSHD | |
---|---|---|---|---|---|---|
The number of detected trees and shrubs (mean, min–max in individual sites; %) | ||||||
Trees | 57.4 (50.9–71.1) | 61.8 (59.0–74.6) | 64.9 (59.7–76.3) | 80.0 (74.0–90.9) | 75.6 (71.6–85.6) | 76.3 (71.0–85.6) |
Shrubs | 30.2 (20.4–48.1) | 47.4 (33.9–54.4) | 45.9 (37.8–52.1) | 67.3 (51.7–81.9) | 65.8 (60.4–75.5) | 68.7 (56.2–75.0) |
Overall | 40.1 (24.8–46.9) | 51.6 (44.3–57.2) | 51.4 (47.3–55.8) | 68.7 (64.8–81.1) | 68.7 (64.2–77.1) | 70.9 (65.4–76.2) |
The %MAE of tree and shrub heights (mean, min–max in sites) | ||||||
Trees | 26.9 (18.2–36.1) | 14.4 (10.0–20.1) | 13.9 (9.3–19.2) | 18.1 (9.8–20.6) | 10.9 (8.8–12.5) | 10.7 (8.3–12.1) |
Shrubs | 46.2 (39.1–57.5) | 27.8 (21.4–32.8) | 27.2 (21.0–30.9) | 25.1 (19.4–29.8) | 17.6 (13.4–26.7) | 10.0 (12.8–26.9) |
Overall | 31.5 (21.4–47.4) | 17.5 (12.9–26.3) | 17.0 (12.2–25.0) | 19.5 (12.2–23.7) | 11.9 (9.8–13.3) | 11.5 (9.4–13.5) |
Area of Woody Vegetation (%) | |||
---|---|---|---|
Trees | Shrubs | Overall | |
ALSHD | 12.9 | 20.5 | 33.4 |
UAV-ALSLD | 11.3 | 22.5 | 33.8 |
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Klouček, T.; Klápště, P.; Marešová, J.; Komárek, J. UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation. Remote Sens. 2022, 14, 2287. https://doi.org/10.3390/rs14092287
Klouček T, Klápště P, Marešová J, Komárek J. UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation. Remote Sensing. 2022; 14(9):2287. https://doi.org/10.3390/rs14092287
Chicago/Turabian StyleKlouček, Tomáš, Petr Klápště, Jana Marešová, and Jan Komárek. 2022. "UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation" Remote Sensing 14, no. 9: 2287. https://doi.org/10.3390/rs14092287