An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Plot Data
2.2.2. ALS Data Acquisition and Processing
2.2.3. ZY3 Stereo Images and Processing
2.3. Methods
2.3.1. Overview
2.3.2. GHMB
Regression Model
Uncertainties Estimation
2.3.3. RKGHMB
Regression Model
Uncertainties Estimation
2.3.4. Accuracy Assessment
3. Results
3.1. Forest Canopy Height Estimation Result of GHMB
3.2. Forest Canopy Height Estimation Result of RKGHMB
3.3. Forest Canopy Height Estimation Accuracy and Uncertainty Assessment
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameters  Value  Parameters  Value 

Platform  Tecnam P2006T  Flying height (m)  1000 
Laser beam divergence (m·rad)  0.5  Speed (Km·h^{−1})  180 
Laser wavelength (nm)  1550  Vertical accuracy (cm)  15 
Scan angle (°)  ±30  Average point density (points·m^{−2})  8.51 
Laser pulse repetition rate (kHz)  400  Pulse length (ns)  3 
Model Name  Model Forms  R^{2}  RMSE  

F 
$${Y}_{F}=2.1315+0.7706\times Hp95$$
 (17)  0.75  1.81 
G 
$${Y}_{G}=5.7147+0.6007\times CH{M}_{ZY3}$$
 (18)  0.64  2.38 
$$V\left({\xi}_{i}\right)$$

$$V\left({\xi}_{i}\right)=24.92452.2393\times {Y}_{{G}_{i}}+0.0577\times {Y}_{{G}_{i}}^{2}$$
 (19)  0.81  1.01 
Residuals Source  Model  Nugget (C_{0})  Partial Sill (C_{1})  Range (m)  Ratio (%) 

Model G  exponential  1.73  3.48  147.27  33.2 
PlotBased Reference  LiDARBased Reference  

Models  $r$  $MAE$  $r$  $MAE$ 
GHMB  0.92  1.52  0.75  1.85 
RKGHMB  0.92  1.50  0.78  1.75 
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Zhao, J.; Zhao, L.; Chen, E.; Li, Z.; Xu, K.; Ding, X. An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height. Remote Sens. 2022, 14, 568. https://doi.org/10.3390/rs14030568
Zhao J, Zhao L, Chen E, Li Z, Xu K, Ding X. An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height. Remote Sensing. 2022; 14(3):568. https://doi.org/10.3390/rs14030568
Chicago/Turabian StyleZhao, Junpeng, Lei Zhao, Erxue Chen, Zengyuan Li, Kunpeng Xu, and Xiangyuan Ding. 2022. "An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height" Remote Sensing 14, no. 3: 568. https://doi.org/10.3390/rs14030568