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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 63-70     DOI: 10.6046/gtzyyg.2020.03.09
Inversion model of forest canopy height based on image texture,spectral and topographic features
GAO Kaixuan(), JIAO Haiming, WANG Xinchuang()
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
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To tackle the problem of low inversion accuracy of regional forest crowns based on optical remote sensing data, in this study the authors used multiple stepwise regression (MSR), partial least squares regression (PLSR) and back-propagation (BP) network models to perform regional forest crown height inversion based on the texture, spectral and topographic characteristics of SPOT5 multispectral images. The inversion accuracy of the models was compared and analyzed to determine the optimal model for the study area. The results show that the correlation between the texture parameters of each forest type and the measured canopy height of the plot is better than other spectral parameters. The BP neural network model performs better than other models, and the determination coefficients R 2 of the validation results for the broad-leaved, coniferous, and mixed forest were 0.76, 0.97 and 0.92, respectively, and the root mean square error (RMSE) were 1.6 m, 1.35 m and 2.29 m, respectively. Studies have shown that texture parameters can reflect the structural characteristics of forest canopy well, and the BP neural network model combining image texture, spectrum and terrain feature parameters has good application potential in forest canopy height inversion.

Keywords forest canopy height      multi-spectral imagery      texture parameter      multiple stepwise regression model      partial least squares model      BP neural network model     
:  P237  
Corresponding Authors: WANG Xinchuang     E-mail:;
Issue Date: 09 October 2020
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Kaixuan GAO
Haiming JIAO
Xinchuang WANG
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Kaixuan GAO,Haiming JIAO,Xinchuang WANG. Inversion model of forest canopy height based on image texture,spectral and topographic features[J]. Remote Sensing for Land & Resources, 2020, 32(3): 63-70.
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Fig.1  Lushuihe forest farm and plot distribution
Fig.2  Technology roadmap
序号 植被指数 公式
1 归一化植被指数(NDVI) NDVI=(NIR-R)/(NIR+R)
2 比值植被指数(RVI) RVI=NIR/R
3 差值植被指数(DVI) DVI=NIR-R
4 土壤调节植被指数(SAVI) SAVI=1.5(NIR-R)/(NIR+R+0.5)
5 修正土壤调节植被指数(MSAVI) MSAVI=NIR+1-(2NIR+1)2-8(NIR-R)2
Tab.1  Vegetation index
序号 纹理特征 公式
1 均值(ME) ME=i,j=0N-1iPi,j
2 方差(VA) VA=i,j=0N-1iPi,j(i-ME)2
3 对比度(CO) CO=i,j=0N-1iPi,j(i-j)2
4 相关性(CC) CC=i,j=0N-1iPi,j(i-ME)(j-ME)VAiVAj
5 同质性(HO) HO=i,j=0N-1iPi,j1+(i-j)2
6 异质性(DI) DI=i,j=0N-1iPi,ji-j
7 熵(EN) EN=i,j=0N-1iPi,j(-lnPi,j)
8 角二阶距(SM) SM=i,j=0N-1iPi,j2
Tab.2  Texture parameters
阔叶林 针叶林 针阔混交林
遥感变量 相关性 遥感变量 相关性 遥感变量 相关性
ENRVI 0.790** RVI 0.800** ENB2 0.860**
ENNDVI 0.550** B3 -0.718* ENB3 0.842**
MEMSAVI -0.584** HORVI -0.765** ENNDVI 0.841**
ENASP -0.656** CCB3 -0.960** SMSLO 0.808**
SMB3 -0.673** B2 -0.692**
MEB1 -0.810**
Tab.3  Correlation analysis between measured tree height and remote sensing parameters in forests
林型 指数 模型 R2① AdjR2 RMSE 独立验证
阔叶林 MSR y=28.798+5.38ENRVI-28.32SMB3-2.458ENASP 0.75* 0.72 1.93 0.65 2.56
PLSR y=30.696+3.82ENRVI+1.869ENNDVI-0.049MEMSAVI-3.217ENASP-23.884SMB3 0.75* 0.70 1.94 0.75 2.36
BP 0.86 1.46 0.76 1.6
针叶林 MSR y=18.582-22.945CCB3 0.92** 0.91 1.99 0.81 1.97
PLSR y=58.132+0.99RVI-406.121B3-21.719HORVI-9.176CCB3 0.96** 0.93 1.46 0.79 1.78
BP 0.99 0.73 0.97 1.35
针阔混交林 MSR y=18.019+8.681ENB2+8.324SMSLO-0.397MEB1 0.90** 0.89 1.88 0.87 2.23
PLSR y=18.289+5.567ENB2+2.673ENB3+0.534ENNDVI+7.135SMSLO+61.514B2-0.44MEB1 0.92** 0.90 1.76 0.91 2.07
BP 0.98 0.64 0.92 2.29
Tab.4  High-inversion model and accuracy verification of forest crowns in different forest types
Fig.3  Simulation and verification of crown height in different forest with BP neural network models
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