Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials and Measurements
2.1.1. Study Sites
2.1.2. Acquisition of Point Clouds
2.1.3. Manual Measurement of Stems
2.2. Methods of Point Processing
2.2.1. Outline
2.2.2. Format of Point Cloud Data
2.2.3. Removal of Points on the Ground
2.2.4. Conversion into Wireframe Model and Removal of Small Components
2.2.5. Cross-Sectional Points on Horizontal Planes
2.2.6. Fitting Closed Curves to Section Points
Segmentation of Section Points
Ellipse Fitting
Polygonal Approximation
Spline Curve Approximation
2.2.7. Extraction of Stems as Vertically Aligned Curves
- (1)
- and are overlapping when they are projected on the horizontal plane.
- (2)
- The difference between their z coordinates is less than the threshold .
- (3)
- The difference between their radii is less than the threshold .
2.2.8. Calculation of Traits
3. Results
3.1. Experimental Methods
3.2. Evaluation for Processing Large-Scale Point Clouds
3.2.1. Evaluation of Required Memory Size for Large-Scale Point Clouds
3.2.2. Performance of Point Processing
3.2.3. Detection of Stems
3.3. Evaluation of Stem Sections
3.3.1. Estimation of Diameter and Height
3.3.2. Reproducibility of Calculated Traits
4. Discussion
4.1. Efficiency of Point Processing
4.2. Accuracy of Stem Detection and Calculation of Stem Traits
4.3. Morphological Traits Calculated from Sectional Shapes of Stems
4.4. Application for Forest Investigations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study 1 | Study 2 | Study 3 | ||
---|---|---|---|---|
Dataset | A | B | C | D |
Study site | Site 1 | Site 2 | Site 2 | Site 2 |
Measured age (years) | 18 | 22 | 23 | 23 |
Objective area (ha) | 0.517 | 0.056 | 0.019 | 0.018 |
Stem density within Objective area (ha−1) | 1410 | 1238 | 1440 | 1598 |
Number of targeted trees | 636 | 8 | 3 | 3 |
Scanner type | S120 | X330 | X330 | X330 |
Number of scanner locations | 37 | 19 | 12 | 15 |
Number of points | 3,573,945,616 | 1,237,481,441 | 867,906,402 | 1,105,273,190 |
FARO Focus3D S120 | FARO Focus3D X330 | |
---|---|---|
Range of distance | 0.6–120 m | 0.6–330 m |
Wave length | 905 nm | 1550 nm |
Ranging error | ±2 mm at 10 m and 25 m, each at 90% and 10% reflectivity | |
Angle resolution | 0.018 deg (20,480 measurements per 360 deg) | |
Scan speed | 488,000 points/sec (Quality 2X) |
Number of measurements | 7.0 billion |
Data size | 112 GB |
Number of valid points | 3.6 billion |
Data size | 43 GB |
Interval of Sections | ||
---|---|---|
10 cm | 5 cm | |
Number of section points | 39 million | 79 million |
Data size | 313 MB | 632 MB |
Ratio to original coordinate data | 0.73% | 1.47% |
Number of section curves | 62,624 | 131,458 |
Time | Ratio | |
---|---|---|
Loading and ground removal | 1001 s | 84% |
Calculation of cross-sectional points | 45 s | 3% |
Curve fitting | 121 s | 10% |
Stem detection | 20 s | 2% |
Total | 1187 s | 100% |
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Masuda, H.; Hiraoka, Y.; Saito, K.; Eto, S.; Matsushita, M.; Takahashi, M. Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands. Remote Sens. 2021, 13, 2476. https://doi.org/10.3390/rs13132476
Masuda H, Hiraoka Y, Saito K, Eto S, Matsushita M, Takahashi M. Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands. Remote Sensing. 2021; 13(13):2476. https://doi.org/10.3390/rs13132476
Chicago/Turabian StyleMasuda, Hiroshi, Yuichiro Hiraoka, Kazuto Saito, Shinsuke Eto, Michinari Matsushita, and Makoto Takahashi. 2021. "Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands" Remote Sensing 13, no. 13: 2476. https://doi.org/10.3390/rs13132476