联合GF-1和GF-3影像的森林地上生物量反演

史建敏; 张王菲; 曾鹏; 赵丽仙; 王梦金

doi:10.12171/j.1000-1522.20220029

联合GF-1和GF-3影像的森林地上生物量反演

西南林业大学林学院，云南昆明 650224

基金项目: 国家自然科学基金项目（31860240、32160365），兴滇英才支持计划（80201444），中国林业科学研究院中央级公益性科研院所基本科研业务费专项（CAFYBB2021SY006）

详细信息

作者简介:
史建敏。主要研究方向：资源与环境遥感。Email：shijianmin20212021@163.com　地址：650224 云南省昆明市盘龙区青云街道白龙寺300号西南林业大学

责任作者:
张王菲，教授，博士生导师。主要研究方向：林业遥感机理及应用研究。Email：mewhff@163. com　地址：同上

中图分类号: S771.8
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出版历程
- 收稿日期: 2022-01-16
- 修回日期: 2022-04-11
- 网络出版日期: 2022-10-27
- 发布日期: 2022-11-24

Inversion of forest aboveground biomass from combined images of GF-1 and GF-3

College of Forestry, Southwest Foresty University, Kunming 650224, Yunnan, China

摘要

摘要:
目的探索高分（GF）光学、合成孔径雷达（SAR）数据及其联合数据在森林地上生物量（AGB）及其组成部分反演中的可行性。
方法以云南省昆明市宜良县小哨林区的云南松为研究对象，结合实地调查数据，以GF-1光学数据和GF-3 SAR数据作为数据源，提取光学数据常用的植被指数和纹理特征，SAR数据的各极化后向散射系数、纹理特征以及极化分解等参数，利用KNN-FIFS方法分别进行森林AGB及其分量的反演；然后采用留一交叉验证法对反演结果进行精度评价，并在此基础上绘制森林AGB及其分量空间分布图。
结果联合GF-1和GF-3数据反演森林AGB及其分量的精度最高，R²均超过了0.710，RMSEr的值在22% ~ 27%之间，其中树叶的反演精度最优，模型的R²为0.714，RMSE为10.270 t/hm²，RMSEr为24.58%；除树叶生物量外，森林AGB和其他分量仅采用GF-1提取的特征进行反演时，精度均优于采用GF-3特征的反演结果。
结论联合GF-1光学数据和GF-3全极化SAR可以实现一定程度的互补，提高森林AGB及其分量的反演精度，此外KNN-FIFS方法在低生物量水平的云南松纯林的AGB及其分量的反演中具有一定的鲁棒性，且KNN-FIFS优选的重要参数多为SAR和光学的纹理特征。
- GF系列卫星 /
- 森林地上生物量 /
- 森林地上生物量分量 /
- KNN-FIFS
Abstract:
Objective The objective of this study was to explore the feasibility of GF-1, GF-3, and combination of GF-1 and GF-3 for total forest aboveground biomass (AGB) and its component inversion.
Method In this study, the vegetation indices, texture characterizations extracted from GF-1, backscattering coefficients, texture characterizations and polarimetric decomposition features extracted from GF-3 were used independently and in combination to estimate total AGB and component AGB of a pure forest of Yunnan pine (Pinus yunnanensis), located in Xiaoshao Forest Region in Yiliang County, Yunnan Province of southwestern China. A fast iterative features selection method for k-NN method (KNN-FIFS) was applied in forest total AGB and component AGB inversion and the leave one out cross validation (LOOCV) method was used to evaluate the model and the inversion results and the results were mapped and analyzed.
Result The joint GF-1 and GF-3 data had the highest accuracy for inversion of forest AGB and each component AGB, and the R²of each exceeded 0.710, and the values of RMSEr were between 22% and 27%, among which the inversion accuracy of foliage was the best, with the model’s R² of 0.714, RMSE of 10.270 t/ha, and RMSEr of 24.58%; except for foliage component AGB, forest AGB and other components of AGB had better accuracy than the inversion results with GF-3 features when only the features extracted from GF-1 were used for the inversion.
Conclusion Combining GF-1 optical data and GF-3 fully polarized SAR data can achieve a certain degree of complementarity to improve the inversion accuracy of forest AGB and fractional AGB. In addition, the KNN-FIFS method is robust in the inversion of AGB and fractional AGB of pure Yunnan pine forests at low biomass levels, and the important parameters preferred by KNN-FIFS mostly are texture features extracted form SAR and optics data.
- GF series satellite /
- forest AGB /
- forest AGB component /
- KNN-FIFS

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图 1 研究区地理位置及样地分布

Figure 1. Location of the study area and distribution of sample plots

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图 2 样地森林地上生物量及其组成

Figure 2. Forest AGB and AGB components in sample plot sites

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图 3 利用GF-1数据反演森林AGB与实测值对比散点图

实线为1∶1验证线。The solid line is 1∶1 verification line.

Figure 3. Scatter plots between forest AGB inversion results using GF-1 data and field collected sample values

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图 4 利用GF-3数据反演森林AGB与实测值对比散点图

实线为1∶1验证线。The solid line is 1∶1 verification line.

Figure 4. Scatter plots between forest AGB inversion results using GF-3 data and field collected sample values

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图 5 利用GF-1、GF-3数据联合反演森林AGB与实测值对比散点图

实线为1∶1验证线。The solid line is 1∶1 verification line.

Figure 5. Scatter plots between forest AGB inversion results using combination of GF-1, GF-3 data and field collected sample values

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图 6 精度评价对比

Figure 6. Accuracy evaluation and comparison

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图 7 研究区各分量生物量分布图

Figure 7. Biomass distribution of each component in the study area

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图 8 研究区森林AGB分布

Figure 8. Forest AGB distribution map in the study area

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表 1 GF-1卫星传感器参数

Table 1 GF-1 satellite sensor parameters

波段号 Band No.	波段 Band	频谱范围 Spectral range/μm	分辨率 Spatial resolution/m
Pan1	Pan	0.45 ~ 0.90	2
Band1	Blue	0.45 ~ 0.52	8
Band2	Green	0.52 ~ 0.59	8
Band3	Red	0.63 ~ 0.69	8
Band4	NIR	0.77 ~ 0.89	8

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表 2 GF-3 PolSAR数据详细参数

Table 2 Detailed information of the acquired GF-3 PolSAR data

成像时间 Acquisition time	极化方式 Polarization way	入射角 Incidence angle/(°)	波长 Wave length/m	距离向分辨率 Range resolution/m	方位向分辨率 Azimuth resolution/m	经纬度 Latitude & Longitude
2018−05−18	HH、HV、VH、VV	39.104	0.055 5	2.248	5.12	103°01′48″E、 24°40′12″N
注：HH.发射和接收的都为水平极化的电磁波；HV.发射的电磁波为水平极化，接收的电磁波为垂直极化；VH.发射的电磁波为垂直极化，接收的电磁波为水平极化；VV.发射和接收的都为垂直极化的电磁波。Notes: HH, both the transmitted and received electromagnetic waves are horizontally polarized; HV, the transmitted electromagnetic wave is horizontally polarized and the received electromagnetic wave is vertically polarized. VH, the transmitted electromagnetic wave is vertically polarized and the received electromagnetic wave is horizontally polarized. VV, transmitted and received electromagnetic waves that are vertically polarized.

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表 3 植被指数

Table 3 Vegetation indexes

植被指数 Vegetation index	计算公式 Calculation formula
归一化植被指数 Normalized difference vegetation index (NDVI)	${\rm{NDVI}} = \dfrac{{{\rho _{\rm{NIR}}} - {\rho _{\rm{red}}}}}{{{\rho _{\rm{NIR}}} + {\rho _{\rm{red}}}}}$
简单植被指数 Simple ratio index (SR)	${\rm{SR} } = {\rho _{\rm{red} } }/{\rho _{{\text{NIR} } } }$
可见光大气阻抗植被指数 Visible atmospherically resistant index (VARI)	${\rm{VARI}} = \dfrac{{{\rho _{\rm{{green}}}} - {\rho _{\rm{red}}}}}{{{\rho _{{\text{green}}}} + {\rho _{\rm{red}}} - {\rho _{{\rm{blue}}}}}}$
差值植被指数 Difference vegetation index (DVI)	${\rm{DVI} } = {\rho _{{\rm{NIR}}} } - {\rho _{\rm{red} } }$
垂直植被指数 Perpendicular vegetation index (PVI)	${\rm{PVI} } = 0.939 \times {\rho _{{\text{NIR} } } } - 0.344 \times {\rho _{\rm{red} } } + 0.09$
土壤调节植被指数 Soil-adjusted vegetation index (SAVI)	${\rm{SAVI} } = \dfrac{ {1.5 \times \left( { {\rho _{\rm{NIR} } } - {\rho _{{\rm{red}} } }} \right)} }{ { {\rho _{\rm{NIR} } } + {\rho _{\rm{red} } } + 0.5} }$
增强植被指数 Enhanced vegetation index (EVI)	${\rm{EVI} } = \dfrac{ {2.5 \times \left( { {\rho _{ {\text{NIR} } } } - {\rho _{ {\text{red} } } }} \right)} }{ {1 + {\rho _{\rm{NIR} } } + 6 \times {\rho _{\rm{red} } } - 7.5 \times {\rho _{{\rm{blue}}} } } }$
修正土壤调节植被指数 Modified soil-adjusted vegetation index (MSAVI)	${\rm{MSAVI}} = \dfrac{{2{\rho _{\rm{NIR}}} + 1 - \sqrt {{{\left( {2{\rho _{{\text{NIR}}}} + 1} \right)}^2} - 8\left( {{\rho _{{\text{NIR}}}} - {\rho _{\rm{red}}}} \right)} }}{2}$
注：表中的 $\;{\rho }_{\rm{NIR}}、{\rho }_{\rm{red}}、{\rho }_{\text{green}}、{\rho }_{{\rm{blue}}}$ 分别为GF-3影像的近红外、红光、绿光和蓝光波段。Notes: $\;{\rho }_{\rm{NIR}},\;{\rho }_{\rm{red}},\;{\rho }_{\text{green}},\;{\rho }_{{\rm{blue}}}\;$ are the near-infrared, red, green and blue bands of GF-3 images.

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表 4 GF-1的重要参数及反演精度

Table 4 Important parameters of GF-1 and inversion accuracy

生物量 Biomass	最近邻样本数量取值 Number of nearest neighbor samples (K)	窗口 Window	决定系数 Coefficient of determination (R²)	均方根误差/（t·hm⁻²） Root mean square error (RMSE)/(t·ha⁻¹)	相对均方根误差 Relative root mean square error (RMSEr)/%	重要参数 Major parameter
森林地上生物量 Forest AGB	1	5	0.634	11.310	30.61	Me_b2、Con_pan、Var_b3、 Con_b1、Con_3
干材生物量 Stem biomass	1	5	0.609	6.971	32.76	Me_b2、Con_pan、Var_b3、 Con_b1、Con_3
树皮生物量 Bark biomass	4	1	0.595	1.119	30.37	Me_b1、Var_b1、Cor_b3
树枝生物量 Branch biomass	1	5	0.646	2.663	30.01	Me_b2、Var_pan、Var_b1、 Con_b2
树叶生物量 Leaf biomass	4	1	0.598	0.976	30.01	Me_b1、Var_b1、Cor_b3
注：Con_pan、Var_pan为GF-1全色波段纹理的对比度和方差；Me_b1、Con_b1、Var_b1为GF-1蓝光波段的均值、对比度、方差；Me_b2为GF-1绿光波段的均值；Var_b3、Con_3、Cor_b3为红光波段的方差、对比度、相关性。Notes: Con_pan, Var_pan are the contrast and variance of GF-1 panchromatic band texture; Me_b1, Con_b1, Var_b1 are the mean, contrast, and variance of GF-1 blue band; Me_b2 is the mean of GF-1 green band; Var_b3, Con_3, Cor_b3 are the variance, contrast, and correlation of red band, respectively.

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表 5 GF-3的重要参数及反演精度

Table 5 Important parameters and inversion accuracies based on GF-3

生物量 Biomass	K	窗口 Window	R²	RMSE/(t·hm⁻²) RMSE/(t·ha⁻¹)	RMSEr/%	重要参数 Major parameter
森林地上生物量 Forest AGB	1	7	0.500	14.110	37.17	T23_im、VHDB、T13_re
干材生物量 Stem biomass	2	7	0.464	8.522	38.53	T23_im、T13_re、Fre_Vol
树皮生物量 Bark biomass	7	7	0.372	1.495	37.99	T23_im、Sp_con135
树枝生物量 Branch biomass	1	7	0.515	3.243	35.89	T23_im、VHDB、T13_re
树叶生物量 Leaf biomass	2	11	0.630	0.993	29.13	Sp_ent135、T23_re、Sp_ent0、T12_re、HHUTM、Sp_con0
注：Sp_con135和Sp_con0、Sp_ent135和Sp_ent0分别为GF-3的SPAN特征纹理的135°和0°的对比度、135°和0°的熵；VHDB为GF-3交叉极化的后向散射系数分贝化结果；HHUTM为GF-3同极化的后向散射系数；T23_im、T13_re、T23_re为相干矩阵（T）中的元素；Fre_Vol为Freeman2分解的体散射分量。Notes: Sp_con135, Sp_con0, Sp_ent135, Sp_ent0 are the 135° and 0° contrast, 135° and 0° entropy of the SPAN feature texture of GF-3; VHDB is the decibelized result of backscattering coefficient of GF-3 cross-polarization. HHUTM is the backscattering coefficient of GF-3 co-polarization; T23_im, T13_re, T23_re are the elements in the coherence matrix (T); Fre_Vol is the body scattering component of the Freeman2 decomposition.

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表 6 联合GF-1 + GF-3的重要参数及反演精度

Table 6 Important parameters and inversion accuracies based on the combination of GF-1 + GF-3

生物量 Biomass	K	窗口 Window	R²	RMSE/(t·hm⁻²) RMSE/(t·ha⁻¹)	RMSEr/%	重要参数 Major parameter
森林地上生物量 Forest AGB	2	3	0.714	10.270	26.61	B2、Sp_dis90、HVUTM、VHUTM、Sp_hom45、Me_b2、 Sp_con90
干材生物量 Stem biomass	2	1	0.713	6.285	26.53	Dis_b3、Sp_con90、Me_b2、Sp_con45、Hom_b3、 Sp_ent135、Var_b1、VH
树皮生物量 Bark biomass	2	3	0.757	0.931	22.54	B3、Sp_dis90、HVUTM、VHUTM、Sp_hom45、Var_b1、 Sp_hom90、Me_b3
树枝生物量 Branch biomass	2	3	0.711	2.419	26.58	B3、Sp_dis90、HVUTM、VHUTM、Sp_hom45、Me_b2、 Sp_con90
树叶生物量 Leaf biomass	3	11	0.770	0.820	24.58	Dis_b3、Sec_b2、Me_b3、T12_re、Var_b2、Var_b1
注：在GF-1光学数据中，B2、B3为绿光和红光的光谱信息；Me_b2、Sec_b2为绿光波段的均值和二阶矩；Dis_b3、Hom_b3、Me_b3为红光波段的相异性、均匀性、均值。在GF-3PolSAR数据中，HVUTM、VHUTM为交叉极化的后向散射系数；Sp_dis90、Sp_hom45和Sp_hom90、Sp_con90和Sp_con45分别为SPAN特征的90°的相异性、45°和90°的均匀性、90°和45°的对比度。Notes: in GF-1 optical data, B2 and B3 are spectral information of green and red light; Me_ b2, Sec_ B2 are the mean and second-order moment of the green band; Dis_ b3, Hom_ b3, Me_ B3 are the difference, uniformity and mean value of red light band. In gf-3polsar data, HVUTM and VHUTM are backscattering coefficients of cross polarization; Sp_ dis90, Sp_ hom45, Sp_ hom90, Sp_ con90, Sp_ Con45 are the 90° non similarity, 45° and 90° uniformity, 90° and 45° contrast of span characteristics.

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