Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Field Data
2.2. UAV Data
2.3. Calculation of Dense Point Clouds and Image Orthomosaics
2.4. Individual Tree Detection and Spectral Data Features Extraction
2.5. Statistical Analysis
3. Results
3.1. Accuracy Assessment of the 2-Class Classification Model
3.2. Accuracy Assessment of the 4-Class Classification Model
3.3. Spectral Feature Importance
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Research Site | Flight | Date | UAV Platform | Sensor | Number of Images | Overlap, % | GSD, cm |
---|---|---|---|---|---|---|---|
1 | 1 | 10.7.18 | eBee plus RTK | S.O.D.A | 341 | 85/85 | 4.7 |
2 | 10.7.18 | eBee plus RTK | S.O.D.A | 670 | 85/85 | 4.7 | |
3 | 10.7.18 | eBee plus RTK | Sequoia | 864 | 80/80 | 13.7 | |
4 | 10.7.18 | eBee plus RTK | Sequoia | 936 | 80/80 | 13.7 | |
2 | 5 | 10.7.18 | eBee plus RTK | S.O.D.A | 303 | 85/85 | 4.9 |
6 | 10.7.18 | eBee plus RTK | Sequoia | 448 | 80/80 | 13.5 | |
3 | 7 | 10.7.18 | eBee plus RTK | S.O.D.A | 546 | 85/85 | 4.9 |
8 | 10.7.18 | eBee plus RTK | Sequoia | 832 | 80/80 | 14.5 | |
4 | 9 | 10.7.18 | eBee plus RTK | S.O.D.A | 444 | 85/85 | 4.9 |
10 | 10.7.18 | eBee plus RTK | Sequoia | 770 | 80/80 | 14 | |
5 | 11 | 28.6.18 | eBee plus RTK | S.O.D.A | 500 | 85/85 | 4.9 |
12 | 11.7.18 | eBee plus RTK | S.O.D.A | 765 | 85/85 | 4.9 | |
13 | 11.7.18 | eBee plus RTK | S.O.D.A | 875 | 85/85 | 4.9 | |
14 | 11.7.18 | eBee plus RTK | S.O.D.A | 611 | 85/85 | 4.9 | |
15 | 12.7.18 | eBee plus RTK | S.O.D.A | 829 | 85/85 | 4.9 | |
16 | 12.7.18 | eBee plus RTK | S.O.D.A | 735 | 85/85 | 4.9 | |
17 | 11.7.18 | eBee plus RTK | Sequoia | 1063 | 80/80 | 13.7 | |
18 | 11.7.18 | eBee plus RTK | Sequoia | 196 | 80/80 | 13.7 | |
19 | 12.7.18 | eBee plus RTK | Sequoia | 1067 | 80/80 | 13.7 | |
20 | 12.7.18 | eBee plus RTK | Sequoia | 851 | 80/80 | 13.7 | |
6 | 21 | 09.7.18 | Phantom 4 RTK | Phantom | 199 | 80/80 | 4.0 |
22 | 10.7.18 | Matrice 210 | MicaSense | 338 | 88/75 | 9.2 | |
7 | 23 | 10.7.18 | Phantom 4 RTK | Phantom | 517 | 80/80 | 3.9 |
24 | 10.7.18 | Matrice 210 | MicaSense | 359 | 88/75 | 8.6 | |
8 | 25 | 10.7.18 | Phantom 4 RTK | Phantom | 322 | 80/80 | 4.2 |
26 | 10.7.18 | Matrice 210 | MicaSense | 309 | 88/75 | 8.8 | |
9 | 27 | 10.7.18 | Phantom 4 RTK | Phantom | 320 | 80/80 | 4.3 |
28 | 10.7.18 | Matrice 210 | MicaSense | 185 | 88/75 | 8.9 |
Feature | Source | Description |
---|---|---|
Rmean | RGB point cloud | Mean value of the red band |
Gmean | RGB point cloud | Mean value of the green band |
Bmean | RGB point cloud | Mean value of the blue band |
Rsd | RGB point cloud | Standard deviation of the red band |
Gsd | RGB point cloud | Standard deviation of the green band |
Bsd | RGB point cloud | Standard deviation of the blue band |
Red_min | MSP orthomosaic | Minimum value of the red band |
Red_max | MSP orthomosaic | Maximum value of the red band |
Red_mean | MSP orthomosaic | Mean value of the red band |
Red_25 | MSP orthomosaic | 25% percentile of the red band |
Red_50 | MSP orthomosaic | 50% percentile of the red band |
Red_75 | MSP orthomosaic | 75% percentile of the red band |
Green_min | MSP orthomosaic | Minimum value of the green band |
Green_max | MSP orthomosaic | Maximum value of the green band |
Green_mean | MSP orthomosaic | Mean value of the green band |
Green_25 | MSP orthomosaic | 25% percentile of the green band |
Green_50 | MSP orthomosaic | 50% percentile of the green band |
Green_75 | MSP orthomosaic | 75% percentile of the green band |
NIR_min | MSP orthomosaic | Minimum value of the near infrared band |
NIR_max | MSP orthomosaic | Maximum value of the near infrared band |
NIR_mean | MSP orthomosaic | Mean value of the near infrared band |
NIR_25 | MSP orthomosaic | 25% percentile of the near infrared band |
NIR_50 | MSP orthomosaic | 50% percentile of the near infrared band |
NIR_75 | MSP orthomosaic | 75% percentile of the near infrared band |
RE_min | MSP orthomosaic | Minimum value of the red edge band |
RE_max | MSP orthomosaic | Maximum value of the red edge band |
RE_mean | MSP orthomosaic | Mean value of the red edge band |
RE_25 | MSP orthomosaic | 25% percentile of the red edge band |
RE_50 | MSP orthomosaic | 50% percentile of the red edge band |
RE_75 | MSP orthomosaic | 75% percentile of the red edge band |
Blue_min * | MSP orthomosaic | Minimum value of the blue band |
Blue_max * | MSP orthomosaic | Maximum value of the blue band |
Blue_mean * | MSP orthomosaic | Mean value of the blue band |
Blue_25 * | MSP orthomosaic | 25% percentile of the blue band |
Blue_50 * | MSP orthomosaic | 50% percentile of the blue band |
Blue_75 * | MSP orthomosaic | 75% percentile of the blue band |
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Species | MicaSense Dataset | Sequoia Dataset |
---|---|---|
Scots pine (Pinus sylvestris L.) | 75 | 120 |
Norway spruce (Picea abies L.) | 66 | 120 |
Downy birch (Betula pubescens L.) and Silver birch (Betula pendula) | 75 | 107 |
Aspen (Populus tremula L.) | 90 | 118 |
Total: | 306 | 465 |
Camera (Feature Set) | Number of Features | F1-Score (%) | Overall Accuracy (%) | Kappa | |
---|---|---|---|---|---|
Aspen | Other | ||||
MicaSense (RGB) | 6 | 84 | 93 | 90.1 | 0.77 |
MicaSense (MSP) | 30 | 81 | 91 | 87.9 | 0.72 |
MicaSense (RGB+MSP) | 32 | 82 | 93 | 90.1 | 0.76 |
Sequoia (RGB) | 6 | 79 | 93 | 89.9 | 0.73 |
Sequoia (MSP) | 21 | 68 | 90 | 84.2 | 0.57 |
Sequoia (RGB+MSP) | 29 | 77 | 93 | 89.2 | 0.70 |
(a) | |||||
MicaSense (RGB) | Predicted | ||||
Aspen | Other | Total | UA (%) | ||
Observed | Aspen | 23 | 5 | 28 | 82.1 |
Other | 4 | 59 | 63 | 93.7 | |
Total | 27 | 64 | 91 | ||
PA (%) | 85.2 | 92.2 | |||
OA (%) | 90.1 | ||||
(b) | |||||
Sequoia (RGB) | Predicted | ||||
Aspen | Other | Total | UA (%) | ||
Observed | Aspen | 27 | 6 | 33 | 81.8 |
Other | 8 | 98 | 106 | 92.5 | |
Total | 35 | 104 | 139 | ||
PA (%) | 77.1 | 94.2 | |||
OA (%) | 88.9 |
Camera (Feature Set) | Number of Features | F1-Score (%) | OA (%) | Kappa | |||
---|---|---|---|---|---|---|---|
Aspen | Birch | Pine | Spruce | ||||
MicaSense (RGB) | 6 | 82 | 78 | 79 | 76 | 78.9 | 0.72 |
MicaSense (MSP) | 24 | 84 | 76 | 71 | 91 | 81.1 | 0.75 |
MicaSense (RGB+MSP) | 28 | 86 | 78 | 80 | 88 | 83.3 | 0.78 |
Sequoia (RGB) | 6 | 78 | 64 | 89 | 93 | 81.3 | 0.75 |
Sequoia (MSP) | 17 | 63 | 57 | 72 | 79 | 68.3 | 0.58 |
Sequoia (RGB+MSP) | 26 | 80 | 69 | 81 | 94 | 81.3 | 0.75 |
(a) | |||||||
MicaSense (RGB+MSP) | Predicted | UA (%) | |||||
Aspen | Birch | Pine | Spruce | Total | |||
Observed | Aspen | 24 | 3 | 1 | 1 | 29 | 82.8 |
Birch | 1 | 16 | 1 | 1 | 23 | 84.2 | |
Pine | 1 | 3 | 16 | 1 | 16 | 76.2 | |
Spruce | 1 | 0 | 1 | 19 | 22 | 90.5 | |
Total | 27 | 22 | 19 | 22 | 90 | ||
PA (%) | 88.9 | 72.7 | 84.2 | 86.4 | |||
OA (%) | 83.3 | ||||||
(b) | |||||||
Sequoia (RGB+MSP) | Predicted | UA (%) | |||||
Aspen | Birch | Pine | Spruce | Total | |||
Observed | Aspen | 28 | 6 | 1 | 0 | 35 | 80.0 |
Birch | 4 | 22 | 5 | 1 | 32 | 68.8 | |
Pine | 3 | 3 | 30 | 2 | 38 | 78.9 | |
Spruce | 0 | 1 | 0 | 33 | 34 | 97.1 | |
Total | 35 | 32 | 36 | 36 | 139 | ||
PA (%) | 80.0 | 68.8 | 83.3 | 91.7 | |||
OA (%) | 81.3 |
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Kuzmin, A.; Korhonen, L.; Kivinen, S.; Hurskainen, P.; Korpelainen, P.; Tanhuanpää, T.; Maltamo, M.; Vihervaara, P.; Kumpula, T. Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests. Remote Sens. 2021, 13, 1723. https://doi.org/10.3390/rs13091723
Kuzmin A, Korhonen L, Kivinen S, Hurskainen P, Korpelainen P, Tanhuanpää T, Maltamo M, Vihervaara P, Kumpula T. Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests. Remote Sensing. 2021; 13(9):1723. https://doi.org/10.3390/rs13091723
Chicago/Turabian StyleKuzmin, Anton, Lauri Korhonen, Sonja Kivinen, Pekka Hurskainen, Pasi Korpelainen, Topi Tanhuanpää, Matti Maltamo, Petteri Vihervaara, and Timo Kumpula. 2021. "Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests" Remote Sensing 13, no. 9: 1723. https://doi.org/10.3390/rs13091723