Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition
2.3. Vegetation Indices (VIs)
- (1)
- Multispectral VIs
- (2)
- RGB VIs
Image | VI Name | Abbreviation | Equation and Derivation |
---|---|---|---|
Normalized difference vegetation index [61,62,63] | NDVI | (NIR − Red)/(NIR + Red) | |
Multispectral imagery | Red-edge NDVI [64] | NDVIre | (NIR − RE)/(NIR + RE) |
Green normalized difference vegetation index [65] | GNDVI | (NIR−Green)/(NIR + Green) | |
Normalized green red difference Index [61] | NGRDI | (Green − Red)/(Green + Red) | |
RGB imagery | Normalized green blue difference index [66] | NGBDI | (Green − Blue)/(Green + Blue) |
Visible-band Difference Vegetation Index [59] | VDVI | (2 × Green − Blue − Red)/ (2 × Green + Blue + Red) |
2.4. Classification Method
2.5. Classification Accuracy Assessment
3. Results
3.1. Model Accuracy in Different Months
3.2. Comparison of the Multispectral and RGB Models
3.3. Model Accuracy with Added VIs
4. Discussion
4.1. Model Performance in Different Months
4.2. Differences in the Capabilities of Multispectral and RGB Imagery
4.3. Potential of Vegetation Indices in Tree Species Classification Using CNN
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Image Acquisition Equipment | DJI P4M |
---|---|
Image sensor | 1/2.9-inch CMOS |
Blue bands | 450 ± 16 nm |
Green bands | 560 ± 16 nm |
Red bands | 650 ± 16 nm |
Red edge band | 730 ± 16 nm |
Near-infrared band | 840 ± 26 nm |
Acquisition Mode | Snapshot |
Optics | f/2.2 |
FOV | 62.7° |
Flight Date | Flight Time | Flight Height | Total Images | Spatial Resolution (m) | Flight Area (ha) |
---|---|---|---|---|---|
April 21 | 14:23 | 500 m | 223 × 6 | 0.21 | 194.24 |
May 23 | 14:31 | 300 m | 121 × 6 | 0.13 | 40.08 |
June 17 | 13:07 | 400 m | 145 × 6 | 0.17 | 101.80 |
August 27 | 16:07 | 400 m | 145 × 6 | 0.17 | 99.19 |
September 27 | 16:51 | 400 m | 145 × 6 | 0.17 | 99.04 |
October 27 | 14:17 | 400 m | 145 × 6 | 0.17 | 94.47 |
Month | April 21 | May 23 | June 17 | August 27 | September 27 | October 27 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Input Data | MS | RGB | MS | RGB | MS | RGB | MS | RGB | MS | RGB | MS | RGB |
Precision | 90.45% | 91.24% | 89.77% | 88.21% | 89.82% | 87.75% | 90.06% | 88.27% | 91.04% | 90.40% | 90.06% | 90.68% |
Recall | 90.24% | 90.87% | 91.20% | 90.06% | 89.38% | 86.08% | 89.85% | 87.70% | 91.25% | 90.65% | 89.63% | 90.35% |
F1 | 90.34% | 91.06% | 90.48% | 89.12% | 89.60% | 86.91% | 89.95% | 87.98% | 91.15% | 90.52% | 89.84% | 90.52% |
IoU | 82.50% | 83.72% | 82.56% | 80.21% | 82.31% | 76.81% | 81.69% | 78.52% | 84.82% | 82.66% | 81.56% | 82.67% |
Month | April 21 | May 23 | June 17 | August 27 | September 27 | October 27 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Input Data | MS_VI | RGB_VI | MS_VI | RGB_VI | MS_VI | RGB_VI | MS_VI | RGB_VI | MS_VI | RGB_VI | MS_VI | RGB_VI |
Precision | 90.26% | 91.33% | 90.02% | 89.50% | 89.61% | 88.95% | 89.78% | 89.43% | 91.09% | 90.50% | 90.13% | 90.54% |
Recall | 90.32% | 91.50% | 91.55% | 91.10% | 89.86% | 87.32% | 89.39% | 88.33% | 91.02% | 90.29% | 89.84% | 90.00% |
F1 | 90.29% | 91.41% | 90.78% | 90.29% | 89.73% | 88.13% | 89.59% | 88.88% | 91.05% | 90.40% | 89.99% | 90.27% |
IoU | 82.36% | 84.33% | 83.06% | 82.16% | 81.27% | 78.61% | 81.11% | 79.93% | 83.57% | 82.37% | 81.79% | 82.24% |
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Shi, W.; Liao, X.; Sun, J.; Zhang, Z.; Wang, D.; Wang, S.; Qu, W.; He, H.; Ye, H.; Yue, H.; et al. Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery. Remote Sens. 2023, 15, 2205. https://doi.org/10.3390/rs15082205
Shi W, Liao X, Sun J, Zhang Z, Wang D, Wang S, Qu W, He H, Ye H, Yue H, et al. Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery. Remote Sensing. 2023; 15(8):2205. https://doi.org/10.3390/rs15082205
Chicago/Turabian StyleShi, Weibo, Xiaohan Liao, Jia Sun, Zhengjian Zhang, Dongliang Wang, Shaoqiang Wang, Wenqiu Qu, Hongbo He, Huping Ye, Huanyin Yue, and et al. 2023. "Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery" Remote Sensing 15, no. 8: 2205. https://doi.org/10.3390/rs15082205