GF-3全极化SAR数据极化分解估算人工林冠层生物量

doi:10.11707/j.1001-7488.20200919

Abstract

Abstract:

Objective: The object of this study was to explore the potential of GF-3 fully polarized SAR data in the estimation of artificial forest canopy biomass and to make an attempt to propose an accurate estimation method for forest canopy biomass. Method: In this study,based on the data from 22 sample plots of Pinus tabulaeformis and Larix principis-rupprechtii artificial forest in Wangyedian Forest Farm,Chifeng,Inner Mongolia and from GF-3 fully polarimetric SAR,Freeman three-component decomposition,Freeman two-component decomposition and Yamaguchi three-component decomposition were used to obtain polarization decomposition components. The volume-to-ground scattering ratio parameters (R₁,R₂ and R₃) corresponding to each polarization decomposition method were constructed respectively. The multiple stepwise regression method was used to establish regression models for forest canopy biomass and SAR extraction parameters,and the model was evaluated using the leave-one-out cross test. Result: There were significant negative correlations between the components obtained by different polarization decomposition methods and the canopy biomass. The correlation between the volume scattering component of the Freeman three-component decomposition and the canopy biomass (r=-0.68) was higher than that between the double-bounce scattering component and the canopy biomass (r=-0.6),and also higher than that between the surface scattering component and the canopy biomass (r=-0.424). Similarly,the correlation between the volume scattering component of the Freeman two-component decomposition and the canopy biomass (r=-0.718) was higher than that between the ground scattering component and the canopy biomass (r=-0.62). The correlation between the double-bounce scattering component of Yamaguchi three-component decomposition and the canopy biomass was the highest (r=-0.743). Compared with Freeman three-component decomposition,the decomposition components of Freeman two-component decomposition and Yamaguchi three-component decomposition had better correlations with the canopy biomass. The optimal parameters obtained by stepwise regression were the volume-to-ground scattering ratio parameters R₂ and R₃ corresponding to Freeman two-component decomposition and Yamaguchi three-component decomposition,respectively. Then the canopy biomass estimation model was established (R²=0.658,RMSE=4.943 t·hm^-2). The cross-validation results of the model showed that the prediction error of the model was relatively low (ME=-0.665 t·hm^-2,MAE=4.845 t·hm^-2,MRE=3.33%,AMRE=23.233%,P=91.5%). The model was tested by means of confidence ellipse F test,and the estimated values were consistent with the measured values. The simulation results were good,and the predicted values were generally distributed near the 1:1 line. No saturation point appears in the model. Conclusion: The decomposition components obtained by the three polarization decomposition methods of Freeman three-component decomposition,Freeman two-component decomposition,and Yamaguchi three-component decomposition were significantly correlated with the canopy biomass. The rotation transformation of polarization coherence and the optimization of volume scattering model could effectively improve polarization decomposition effects of forest area. The volume-to-ground scattering ratio parameters were more sensitive to the canopy biomass than each single polarization decomposition component. Collaborative use of multiple polarization decomposition components could estimate forest canopy biomass more accurately. Polarization decomposition was feasible for estimating forest canopy biomass,and there was no obvious saturation problem.

Key words: polarization decomposition, canopy biomass, SAR, GF-3

CLC Number:

S758

Jingyu Wei,Wenyi Fan,Ying Yu,Xuegang Mao. Polarimetric Decomposition Parameters for Artificial Forest Canopy Biomass Estimation Using GF-3 Fully Polarimetric SAR Data[J]. Scientia Silvae Sinicae, 2020, 56(9): 174-183.

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样地序号 Sample No.	株数 Number of trees/hm^-2	胸径DBH(D)/cm		树高Tree height(H)/m		冠幅Crown width/m
样地序号 Sample No.	株数 Number of trees/hm^-2	平均值Mean	标准差SD	平均值Mean	标准差SD	平均值Mean	标准差SD
1	2 816	11.00	3.24	8.40	2.10	1.27	0.53
2	1 136	16.68	4.85	13.01	3.30	1.87	0.49
3	1 072	16.23	6.00	11.69	3.66	1.79	0.99
4	1 216	17.56	7.01	11.15	3.72	2.18	0.72
5	2 032	12.47	4.13	8.80	2.33	1.49	0.44
6	1 728	11.81	3.15	7.36	1.37	1.44	0.40
7	2 000	13.32	2.95	11.24	1.77	1.34	0.42
8	2 288	11.13	3.57	8.92	1.80	1.25	0.42
9	1 840	11.38	3.72	8.05	1.87	1.39	0.40
10	1 616	14.82	4.39	10.72	2.71	1.31	0.44
11	1 680	12.44	3.57	12.06	2.49	1.30	0.36
12	2 096	12.62	2.09	11.06	1.63	1.20	0.30
13	1 712	10.90	2.53	8.41	1.14	1.37	0.32
14	816	16.89	2.58	13.49	1.44	1.75	0.29
15	288	27.91	4.38	15.67	1.05	3.24	0.52
16	5 264	6.85	1.59	6.99	0.84	0.87	0.15
17	1 616	11.16	2.38	9.88	1.64	1.13	0.23
18	1 024	18.23	5.35	15.14	3.23	1.71	0.51
19	944	19.91	4.71	18.12	4.19	1.49	0.36
20	1 344	16.87	3.81	14.36	2.14	1.24	0.36
21	592	23.66	6.51	18.40	9.75	2.23	0.80
22	1 344	17.15	3.22	16.89	1.76	1.37	0.34

树种Species	组分Components	相对生长方程Allometric equations	确定系数Coefficient of determination
油松P.tabulaeformis	W_B	lnW_B=－4.676 29+0.912 519 ln(D²H)	0.865
油松P.tabulaeformis	W_L	lnW_L=－3.856 41+0.763 557 ln(D²H)	0.830
华北落叶松 L. principis-rupprechtii	W_B	lnW_B=－3.170 2+1.850 4 lnD	0.848
华北落叶松 L. principis-rupprechtii	W_L	lnW_L=－2.349 8+1.452 2 lnD	0.723

样地序号 Sample No.	林分类型 Stand type	冠层生物量 Canopy biomass/ (t·hm^-2)
1	油松P.tabulaeformis	31.08
2	油松P.tabulaeformis	36.93
3	油松P.tabulaeformis	32.60
4	油松P.tabulaeformis	40.98
5	油松P.tabulaeformis	29.45
6	油松P.tabulaeformis	18.25
7	油松P.tabulaeformis	35.89
8	油松P.tabulaeformis	26.86
9	油松P.tabulaeformis	20.32
10	油松P.tabulaeformis	36.22
11	华北落叶松L. principis-rupprechtii	15.47
12	华北落叶松L. principis-rupprechtii	18.54
13	华北落叶松L. principis-rupprechtii	13.24
14	华北落叶松L. principis-rupprechtii	11.75
15	华北落叶松L. principis-rupprechtii	9.71
16	华北落叶松L. principis-rupprechtii	17.08
17	华北落叶松L. principis-rupprechtii	11.56
18	华北落叶松L. principis-rupprechtii	22.97
19	华北落叶松L. principis-rupprechtii	18.91
20	华北落叶松L. principis-rupprechtii	19.63
21	华北落叶松L. principis-rupprechtii	16.13
22	华北落叶松L. principis-rupprechtii	21.71

lgW	tdFreeman三分量分解 Freeman three-component decomposition			Freeman二分量分解 Freeman two-component decomposition		Yamaguchi三分量分解 Yamaguchi three-component decomposition
lgW	lgP_V	lgP_D	lgP_S	lgP_C	lgP_G	lgP_V(θ)	lgP_D(θ)	lgP_S(θ)
	-0.68^*	-0.60^*	-0.42	-0.72^*	-0.62^*	-0.69^*	-0.74^*	-0.72^*

参数 Parameter	非标准化系数 Unstandardized coefficient	T检验值 T test value	显著性系数 Sig.	共线性容差 Collinearity tolerance	剩余残差平方和 RSS	剩余标准差 S_{y. x}
常量Constant	0.912	4.525			537.587	5.185
R₃	1.809	5.533	< 0.001	0.507
R₂	-0.393	-2.144	0.045	0.507

Polarimetric Decomposition Parameters for Artificial Forest Canopy Biomass Estimation Using GF-3 Fully Polarimetric SAR Data

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