Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Test Site
2.2. Field Measurements
2.3. UAV Remotely Sensed Image Acquisition
3. Method
3.1. UAV-Based Photogrammetry
3.2. DTM Generation
Algorithm 1: RBF neural network against noisy data |
Parameters: and are the width and height of the DSM, respectively; and correspond to the mean and standard deviation of the height values ; r is the radius centered on candidate ; is a multiple factor; is the residual value; and is a given threshold. |
Generate the DTM using the RBF neural network from the ground points. |
Compute the DoG map using the DTM. |
for to do |
for to do |
if is the local maxima or minima, then |
while !() or !() |
end while |
end if |
if then |
is regarded as a noisy point. |
is then derived from the fitted quadratic surface model. |
Update the height value of the ground points. |
end if |
end for |
end for |
Generate the DTM using the RBF neural network from the updated ground points again. |
3.3. CHM Generation
3.4. Tree Height Estimation
3.5. Evaluation Criteria for Tree Height Estimation Performance
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameters | Value |
---|---|
Image size | 4000 × 3000 |
2687.62 | |
2686.15 | |
1974.34 | |
1496.10 | |
−0.13097076 | |
0.10007409 | |
0.00141688 | |
−0.00020433 |
Site | RMSE X (cm) | RMSE Y (cm) | RMSE Z (cm) | Total RMSE (cm) |
---|---|---|---|---|
Plantation 1 | 4.61 | 5.12 | 9.77 | 6.79 |
Plantation 2 | 5.37 | 5.62 | 8.70 | 6.63 |
Study | Terrain Characteristic | Platform | AGL (m) | Height RMSE (cm) |
---|---|---|---|---|
Tonkin et al., 2014 [43] | Moraine–mound | Hexacopter | 117 | 51.7 |
Long et al., 2016 [44] | Coastal | Fixed-wing | 149 | 17 |
Koci et al., 2017 [45] | Gullied | Quadcopter | 86/97/99 | >30 |
Gindraux et al., 2017 [46] | Glacier | Fixed-wing | 115 | 10–25 |
Gonçalves et al., 2018 [47] | Dune | Quadcopter | 80/100 | 12 |
Our study | Mountainous | Quadcopter | 120 | 9.24 |
Plantation 1 | Plantation 2 | |||
---|---|---|---|---|
Measured Height | Estimated Height | Measured Height | Estimated Height | |
min | 11.70 | 10.84 | 11.94 | 11.69 |
p25 | 14.68 | 13.19 | 16.83 | 15.39 |
median | 16.83 | 17.16 | 19.48 | 19.62 |
p75 | 19.57 | 20.34 | 22.64 | 22.48 |
max | 26.73 | 27.08 | 26.83 | 29.62 |
mean | 17.52 | 17.31 | 19.60 | 19.46 |
std | 3.85 | 4.20 | 4.31 | 4.84 |
MAE | 1.45 | 1.73 |
① | ② | Ours | |
---|---|---|---|
Plantation 1 | 2.80 | 2.19 | 1.95 |
Plantation 2 | 2.74 | 2.31 | 2.02 |
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Share and Cite
He, H.; Yan, Y.; Chen, T.; Cheng, P. Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network. Remote Sens. 2019, 11, 1271. https://doi.org/10.3390/rs11111271
He H, Yan Y, Chen T, Cheng P. Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network. Remote Sensing. 2019; 11(11):1271. https://doi.org/10.3390/rs11111271
Chicago/Turabian StyleHe, Haiqing, Yeli Yan, Ting Chen, and Penggen Cheng. 2019. "Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network" Remote Sensing 11, no. 11: 1271. https://doi.org/10.3390/rs11111271
APA StyleHe, H., Yan, Y., Chen, T., & Cheng, P. (2019). Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network. Remote Sensing, 11(11), 1271. https://doi.org/10.3390/rs11111271