Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Area and Field Reference
2.2. Kinect Measurements
2.3. Kinect Data Processing
- Extraction of trunk skeleton from range image by thresholding along the direction of the camera’s depth axis, i.e., removal of the background to produce a trunk mask;
- Automatic detection of the markers on the trunk from RGB images (first converted to grey scale) by comparing intensities, as markers are brighter than surroundings;
- Extraction of the point cloud from range images in a buffer area of detected marker positions;
- Fitting an optimum circle to the extracted point cloud and computing the diameter of the reconstructed circle at the corresponding height.
2.4. Measurements with Tango Sensor
2.5. Tango Data Processing
2.6. Accuracy Assessment
2.7. Local Field Reference Test
- (1)
- All 292 plots of Evo data were applied as references to develop prediction models using an area-based method. Developed models were then used to make the estimation of plot attributes for 33 plots in Kalkkinen. This corresponds to a situation where the calibration data comes from a different area than the area being the subject of inventory.
- (2)
- Kalkkinen data were used for both developing models and making predictions by area-based method. In this case, two third of data were used as training and one third as testing. The procedure was repeated 100 times. This corresponds to an ideal situation where calibration data can be collected from the area being studied.
- (3)
- A hybrid approach, consisting of the currently applied solution added with local reference data. All Evo data and 10% of Kalkkinen data (randomly selected 96 trees) were used to develop models. Predictions were conducted firstly for individual trees and then individual predictions were aggregated to plot level followed by area-based estimations. This corresponds to a situation where a certain amount of local reference data can be collected from the area being studied.
3. Results
3.1. Measuring Individual Trees
3.2. Measuring Multiple Trees
3.3. Local Field Reference Test
4. Discussion
5. Conclusions
Supplementary Materials
Supplementary File 1Acknowledgments
Author Contributions
Conflicts of Interest
References
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Minimum | Maximum | Mean | Standard Deviation | |
---|---|---|---|---|
Kalkkinen | ||||
Mean tree height (m) | 7.12 | 28.14 | 21.94 | 5.43 |
Mean DBH (cm) | 6.79 | 25.83 | 18.40 | 4.89 |
Volume (m3/ha) | 21.20 | 540.39 | 292.32 | 122.75 |
Evo | ||||
Mean tree height (m) | 3.9 | 31.7 | 18.0 | 6.1 |
Mean DBH (cm) | 7.6 | 50.8 | 18.3 | 6.9 |
Volume (m3/ha) | 0.4 | 586.2 | 148.2 | 110.7 |
Kinect Data vs. Caliper | Caliper (Mean of Two Directions) vs. Tape | Kinect Data (Mean) vs. Tape | Tango (Mean) vs. Tape | |||
---|---|---|---|---|---|---|
East-West | North-South | Mean | ||||
Bias (cm) | −2.60 | 0.30 | 1.50 | 0.95 | 0.54 | 0.33 |
RMSE (cm) | 2.25 | 2.63 | 2.50 | 1.16 | 1.90 | 0.73 |
RMSE-% | 10.2 | 10.0 | 10.0 | 5.9 | 7.3 | 1.89 |
Strategy 1—Reference Plots Located at Different Forest Area | ||||
Bias | RMSE | RMSE-% | R | |
Mean DBH (cm) | 5.08 | 6.27 | 32.85 | 0.74 |
Volume (m3/ha) | 56.98 | 104.32 | 34.32 | 0.80 |
Strategy 2—Reference Plots Located at Test Site | ||||
Mean DBH (cm) | 0.22 | 2.90 | 15.18 | 0.83 |
Volume (m3/ha) | −3.41 | 96.06 | 31.61 | 0.73 |
Strategy 3—Reference Plots Located at Different Forest Area, Selective Measurements in Test Site | ||||
Mean DBH (cm) | 3.51 | 4.67 | 24.42 | 0.83 |
Volume (m3/ha) | 24.48 | 96.97 | 31.90 | 0.77 |
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Hyyppä, J.; Virtanen, J.-P.; Jaakkola, A.; Yu, X.; Hyyppä, H.; Liang, X. Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information. Forests 2018, 9, 6. https://doi.org/10.3390/f9010006
Hyyppä J, Virtanen J-P, Jaakkola A, Yu X, Hyyppä H, Liang X. Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information. Forests. 2018; 9(1):6. https://doi.org/10.3390/f9010006
Chicago/Turabian StyleHyyppä, Juha, Juho-Pekka Virtanen, Anttoni Jaakkola, Xiaowei Yu, Hannu Hyyppä, and Xinlian Liang. 2018. "Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information" Forests 9, no. 1: 6. https://doi.org/10.3390/f9010006