Terrestrial Videogrammetry for Deriving Key Forest Inventory Data: A Case Study in Plantation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Experimental Instruments
2.3. Study Methods
2.4. Acquisition of the Observational Data
2.4.1. Spiral Collection of Video Data
2.4.2. TLS Data Acquisition
2.5. Data Processing
2.5.1. Camera Calibration
2.5.2. Video Data Processing
2.5.3. Establishment of the Point Cloud Model
2.6. Extraction of DBH Data
2.7. TLS Data Processing
2.8. Estimation of AGB
2.9. Precision Evaluation
3. Results
3.1. Point Cloud Data
3.2. Acquisition of DBH
3.2.1. Accuracies of Cylinder Fitting and Circular Fitting
3.2.2. Accuracies of Videogrammetry
3.3. Estimation of Above-Ground Biomass
3.4. Comparison of Work Efficiency
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Parameter | Type | Parameter |
---|---|---|---|
Model | OT110 | Lens | FOV 80° F20 |
Size | 121.9 × 36.9 × 28.6 mm | Battery life | 140 min |
Weight | 116 g | Video resolution | 4K Ultra HD: 3840 × 2160 |
Cradle head jitter suppression | ±0.005° | ISO range | Video: 100–3200 |
No. | Attribute | Parameter |
---|---|---|
1 | Center x 0 | 1346.4 |
2 | Center y 0 | 10,262,000 |
4 | Radial distortion factor K1 | 0.0173 |
5 | Radial distortion factor K2 | 0.0099 |
6 | Radial distortion factor K3 | 00061 |
7 | Tangential distortion coefficient P1 | 0.0021 |
8 | Tangential distortion coefficient P2 | 0.00070178 |
Species | Model Source | Model | R2 |
---|---|---|---|
Sophora japonica | Wang et al. [33] | Wagb = 0.171D2.112 | - |
Koelreuteria paniculata | Zhang et al. [34] | Wagb = 0.12238D2.13793 | 0.95 |
Salix matsudana | Zeng [35] | Wagb = 0.1323D7/3 | - |
Ginkgo biloba | Liu et al. [36] | Wagb = e(−2.56+2.40lnD) | 0.98 |
Fraxinus pennsylvanica | Li et al. [32,37] | Wagb = 0.0495502(D2H)0.952453 | - |
Robinia pseudoacacia | Zeng [35] | Wagb = 0.2022D7/3 | - |
Populus tomentosa | Zeng [38] | Wagb = 0.09198D2.4490 | - |
Juniperus chinensis | SFAC [39] | Wagb = 0.2479D2.0333 | - |
Metasequoia glyptostroboides | Zhuang et al. [40] | Wagb = 0.06291D2.4841 | 0.972 |
Pinus tabulaeformis | LY [41] | Wagb = 0.086112D2.46157 | 0.954 |
Sample No. | Video Duration (min) | Number of Selected Frames | Number of 3D Points | Processing Time (min) |
---|---|---|---|---|
1 | 4.6 | 701 | 3,466,149 | 201 |
2 | 3.7 | 630 | 3,214,592 | 206 |
3 | 4.3 | 583 | 4,965,126 | 254 |
4 | 3.9 | 519 | 3,158,812 | 218 |
5 | 4.1 | 625 | 4,215,669 | 221 |
6 | 3.8 | 532 | 3,120,035 | 237 |
7 | 3.5 | 571 | 3,694,914 | 212 |
8 | 4.7 | 679 | 5,789,210 | 251 |
9 | 3.5 | 561 | 5,248,952 | 203 |
10 | 3.9 | 496 | 4,329,894 | 237 |
Bias | Bias% | RMSE | RMSE% | |
---|---|---|---|---|
Cylinder fitting extracted DBH (cm) | −0.62 | −3.14 | 1.08 | 5.52 |
Circular fitting extracted DBH (cm) | −0.01 | −0.04 | 0.79 | 4.03 |
Sample No. | Circle Fitting vs. Survey | TLS vs. Survey | Circle Fitting vs. TLS | Number of Trees | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Bias | Bias% | RMSE | RMSE% | Bias | Bias% | RMSE | RMSE% | Bias | Bias% | RMSE | RMSE% | ||
1 | −0.62 | −3.14 | 1.08 | 5.52 | −0.05 | −0.27 | 0.94 | 4.80 | −0.61 | −2.88 | 1.26 | 5.96 | 13 |
2 | −0.17 | −1.02 | 1.121 | 6.81 | −0.38 | −2.34 | 0.84 | 5.16 | 0.22 | 1.35 | 1.58 | 9.87 | 12 |
3 | −0.64 | −3.19 | 1.24 | 6.20 | −0.3 | −1.50 | 1.1 | 5.47 | −0.34 | −1.72 | 1.48 | 7.47 | 15 |
4 | −0.42 | −2.20 | 1.07 | 5.68 | −0.15 | −0.79 | 0.85 | 4.49 | −0.27 | −1.42 | 1.58 | 8.41 | 12 |
5 | −0.59 | −3.08 | 1.45 | 7.53 | 0.26 | 1.37 | 1.04 | 5.42 | −0.86 | −4.40 | 1.51 | 7.74 | 14 |
6 | −0.51 | −2.39 | 1.38 | 6.47 | −0.25 | −1.19 | 0.98 | 4.59 | −0.25 | −1.21 | 1.19 | 5.63 | 11 |
7 | −0.32 | −1.43 | 1.74 | 7.70 | 0.21 | 0.95 | 1.19 | 5.26 | −0.54 | −2.35 | 2.07 | 9.12 | 14 |
8 | 0.42 | 2.87 | 1.07 | 7.36 | −0.12 | −1.46 | 0.91 | 6.23 | 0.24 | 1.78 | 1.02 | 7.55 | 16 |
9 | −0.48 | −3.19 | 0.88 | 5.91 | 0.23 | 1.68 | 0.74 | 4.94 | −0.73 | −4.78 | 1.33 | 8.80 | 12 |
10 | −0.12 | −0.65 | 1.43 | 7.76 | −0.55 | −2.96 | 1.05 | 5.70 | 0.43 | 2.38 | 1.64 | 9.15 | 15 |
Strengths | Weaknesses | Opportunities | Threats | |
---|---|---|---|---|
Videogrammetry | 1.Lightweight equipment; 2. Fast data collection; 3. Low labor costs. | 1. Indoors data processing takes more time; 2. Need a scale. | Provide new ideas for forest resource survey methods. | Data processing algorithms need to be further optimized. |
TLS | High precision. | 1.Heavy equipment; 2. Well-designed collection plan; 3. Time-consuming. | Has been recognized by the industry. | - |
Field survey | High precision. | 1.High labor cost; 2. Long time in the field. | - | - |
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Lian, Y.; Feng, Z.; Huai, Y.; Lu, H.; Chen, S.; Li, N. Terrestrial Videogrammetry for Deriving Key Forest Inventory Data: A Case Study in Plantation. Remote Sens. 2021, 13, 3138. https://doi.org/10.3390/rs13163138
Lian Y, Feng Z, Huai Y, Lu H, Chen S, Li N. Terrestrial Videogrammetry for Deriving Key Forest Inventory Data: A Case Study in Plantation. Remote Sensing. 2021; 13(16):3138. https://doi.org/10.3390/rs13163138
Chicago/Turabian StyleLian, Yining, Zhongke Feng, Yongjian Huai, Hao Lu, Shilin Chen, and Niwen Li. 2021. "Terrestrial Videogrammetry for Deriving Key Forest Inventory Data: A Case Study in Plantation" Remote Sensing 13, no. 16: 3138. https://doi.org/10.3390/rs13163138