Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Measurements and Field Biomass Estimation
2.3. Remote Sensing Data Acquisition and Use
2.4. 3D Model Generation
2.5. ITC Process and SfM-Derived Variables
2.6. Individual Tree Biomass Estimation
2.7. Accuraccy Analysis of the ITC and Individual Tree Variables
2.8. Growth Analysis
3. Results
3.1. Accuracy Analysis of the 3D Data Cloud
3.2. Field and SfM Biomass Estimation
3.3. Accuracy Analysis of the ITC and Individual Tree Variables
3.4. Growth Analysis
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Plot | h | d | wa | |||
---|---|---|---|---|---|---|
2015 # | 2017 | 2015 | 2017 | 2015 | 2017 | |
T0 (mean) | 9.64 | 10.58 | 43.1 | 45.3 | 840 | 1021 |
T1 (mean) | 8.74 | 9.59 | 40.8 | 43.5 | 692 | 851 |
T2 (mean) | 9.08 | 9.92 | 42.4 | 45.2 | 755 | 929 |
min | 6.38 | 7.00 | 30.9 | 32.2 | 322 | 367 |
max | 11.28 | 12.72 | 53.6 | 57.3 | 1486 | 1868 |
mean | 9.16 | 10.04 | 42.1 | 44.7 | 764 | 935 |
SD | 0.96 | 1.06 | 4.78 | 5.11 | 232 | 299 |
Dataset | No. of GCPs | Density (Points m−2) | RMSEX (m) | RMSEY (m) | RMSEZ (m) |
---|---|---|---|---|---|
2015 | 5 | 65.3 | 0.0332 | 0.0568 | 0.0485 |
2017 | 10 | 64.9 | 0.0266 | 0.00693 | 0.0208 |
5 (check points) | 0.0446 | 0.00936 | 0.0439 |
Dependent Variable | Equation | Independent Variable (Parameter) | Parameter Estimate | Std. Error | p > |t| | Mef | RMSE | RMSE (%) |
---|---|---|---|---|---|---|---|---|
ww | Multiplicative component (Equation (1)) | β0 | 1.16 | 0.261 | <0.0001 | 0.94 | 117.4 | |
c (λ1) | 2.27 | 0.0734 | <0.0001 | |||||
h (λ2) | 2.15 | 0.0738 | <0.0001 | |||||
wb | 1st Additive component (Equation (1)) | β1 | 23.8 | 8.585 | 0.0072 | 0.85 | 21.36 | |
c (λ3) | 2.91 | 0.462 | <0.0001 | |||||
wl | 2nd Additive component (Equation (1)) | β2 | 39.4 | 6.50 | <0.0001 | 0.88 | 38.44 | |
c (λ4) | 3.17 | 0.209 | <0.0001 | |||||
wbr | 3rd Additive component (Equation (1)) | β3 | 215 | 13.2 | <0.0001 | 0.72 | 123.4 | |
c (λ5) | 1.52 | 0.0856 | <0.0001 | |||||
wa | ww + wb + wl + wbr (Equation (1)) | 0.96 | 163.7 | 33.53 |
Dependent Variable | Equation | Independent Variable (Parameter) | Parameter Estimate | Standard Error | p > |t| | Mef | RMSE (cm) | RMSE (%) |
d2015 | Multiplicative (Equation (1)) | β0 parameter | 7.10059 | 1.17034 | <0.0001 | 0.79 | 2.23 | 4.99 |
hSfM (λ1) | 0.35057 | 0.10825 | 0.0022 | |||||
caSfM (λ2) | 0.23006 | 0.03661 | <0.0001 | |||||
d2017 | Multiplicative (Equation (1)) | β0 parameter | 5.99931 | 1.04605 | <0.0001 | 0.79 | 2.36 | 5.60 |
hSfM (λ1) | 0.41024 | 0.09869 | <0.0001 | |||||
caSfM (λ2) | 0.23775 | 0.03370 | <0.0001 | |||||
Dependent Variable | Equation | Independent Variable | Parameter Estimate | Standard Error | p > |t| | Mef | RMSE (Kg) | RMSE (%) |
waSfM2015 | Multiplicative (Equation (2)) | β0 parameter | 2.44131 | 0.93930 | 0.0124 | 0.85 | 87.46 | 11.44 |
hSfM (λ1) | 1.63850 | 0.23857 | <0.0001 | |||||
caSfM (λ2) | 0.47852 | 0.07782 | <0.0001 | |||||
waSfM20177 | Multiplicative (Equation (2)) | β0 parameter | 1.11449 | 0.49991 | 0.0306 | 0.84 | 117.8 | 12.59 |
hSfM (λ1) | 1.97592 | 0.08079 | <0.0001 | |||||
caSfM (λ2) | 0.49129 | 0.24338 | <0.0001 |
Plot | hSfM (n = 289) | caSfM | waSfM | ||||||
---|---|---|---|---|---|---|---|---|---|
hSfM in 2015 (m) | hSfM in 2017 (m) | ΔhSfM (m) | caSfM in 2015 (m2) | caSfM in 2017 (m2) | ΔcaSfM (m2) | waSfM in 2015 (kg) | waSfM in 2017 (kg) | ΔwaSfM (kg) | |
T0 (mean) | 9.56 | 9.95 | 0.40 | 83.15 | 92.11 | 8.96 | 830.4 | 987.4 | 157.0 |
T1 (mean) | 9.14 | 9.63 | 0.47 | 79.08 | 98.05 | 18.98 | 754.7 | 957.4 | 202.7 |
T2 (mean) | 9.44 | 9.92 | 0.47 | 89.59 | 113.31 | 23.72 | 844.9 | 1081.5 | 236.6 |
min | 6.38 | 6.73 | 0.17 | 14.96 | 25.56 | −17.84 | 155.7 | 263.2 | 21.9 |
max | 11.86 | 12.36 | 0.79 | 169.76 | 190.28 | 38.75 | 1500.1 | 1922.2 | 448.9 |
mean | 9.39 | 9.83 | 0.45 | 84.05 | 101.35 | 17.29 | 810.0 | 1008.8 | 198.7 |
SD | 1.07 | 1.08 | 0.12 | 28.78 | 31.61 | 9.58 | 262.5 | 338.7 | 93.9 |
t-test p-value | <0.01 | <0.01 | <0.01 |
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Guerra-Hernández, J.; González-Ferreiro, E.; Monleón, V.J.; Faias, S.P.; Tomé, M.; Díaz-Varela, R.A. Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands. Forests 2017, 8, 300. https://doi.org/10.3390/f8080300
Guerra-Hernández J, González-Ferreiro E, Monleón VJ, Faias SP, Tomé M, Díaz-Varela RA. Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands. Forests. 2017; 8(8):300. https://doi.org/10.3390/f8080300
Chicago/Turabian StyleGuerra-Hernández, Juan, Eduardo González-Ferreiro, Vicente J. Monleón, Sonia P. Faias, Margarida Tomé, and Ramón A. Díaz-Varela. 2017. "Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands" Forests 8, no. 8: 300. https://doi.org/10.3390/f8080300