Estimation of aboveground biomass in a Chinese fir (Cunninghamia lanceolata)forest combining data of Sentinel⁃1 and Sentinel⁃2

doi:10.3969/j.issn.1000-2006.201811012

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2020, Vol. 44 ›› Issue (3): 149-156.doi: 10.3969/j.issn.1000-2006.201811012

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Estimation of aboveground biomass in a Chinese fir (Cunninghamia lanceolata)forest combining data of Sentinel⁃1 and Sentinel⁃2

PAN Lei¹(), SUN Yujun¹(), WANG Yifu¹, CHEN Liping², CAO Yuanshuai³

^1.State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, College of Forest, Beijing Forestry University, Beijing 100083, China
^2.Huizhou University, Huizhou 516007, China
^3.East China Inventory and Planning Institute, State Forestry Administration, Hangzhou 310019, China

Received:2018-11-05 Revised:2019-01-24 Online:2020-05-30 Published:2020-06-11
Contact: SUN Yujun E-mail:1072344791@qq.com;panlb@bjfu.edu.cn

Abstract

Abstract: Objective

Forest above-ground biomass (AGB) is the main storage site of carbon, which plays an important role in the regional and global carbon cycle. In studies on AGB estimation based on remote sensing, saturation of optical and radar data is a well-known phenomenon, however, it is unclear so far as to how this problem may be reduced in order to improve AGB estimations. Optical and radar data offer different advantages for recording forest stand structures, thus optimized use of their features may help improve AGB estimation. This study was conducted to assess whether forest AGB estimations can be produced in a subtropical area based on data produced using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral instruments.

Method

A stand of Chinese fir (Cunninghamia lanceolata) in a state-owned forest farm in Jiangle, Fujian Province, China, and Sentinel-1 SAR data and Sentinel-2 multispectral optical data were used. Backscatter coefficients of Sentinel-1 images in different polarization modes (VH, VV, VH/VV) and second-order texture measures of a gray level co-occurrence matrix in different window sizes (3×3, 5×5, 7×7 and 9×9) were recorded, and band values, vegetation indices, and second-order texture measures in 3 × 3 windows of Sentinel-2 image were extracted as remote sensing factors. Models were fitted using multiple linear stepwise regression. AGB estimation models based on Sentinel-2 remote sensing factors only and on cooperative Sentinel-2 and Sentinel-1 remote sensing were established. Determination coefficient (R2), adjusted determination coefficient (R $a d j 2$ ), root mean square error (RMSE), and variance inflation factor were selected for assessing the models. The ability of Sentinel-2 optical data and Sentinel-2 optical data combined with Sentinel-1 SAR data to estimate forest AGB was compared and analyzed.

Result

The results showed that when Sentinel-2 band values, vegetation indices and texture measures were used as dependent variables to predict AGB, the estimation effect was inadequate, and the R $a d j 2$ values of the models were 0.319, 0.303 and 0.455, respectively; an AGB estimation model based on all remote sensing factors of Sentinel-2 optical data (Model-1) produced an R $a d j 2$ value of 0.501, while RMSE was 64.04 Mg/hm². When the Sentinel-1 remote sensing factor was added, the AGB estimation model based on Sentinel-2 optical data and Sentinel-1 SAR data was improved, with a model adjustment coefficient (R $a d j 2$ ) and RMSE were 0.575 and 59.13 Mg/hm². Compared with Model-1, the accuracy of Model-2 was considerably improved, thus the second-order texture measures of Sentinel-1 VH polarimetric imaging plays an important role.

Conclusion

Our study demonstrates that multispectral optical data of the Sentinel-2 satellite can be used as reliable data source for estimating forest AGB in subtropical areas, and the combination of Sentinel-1 SAR data and Sentinel-2 multispectral optical data can substantially improve accuracy of estimating forest AGB. Estimations of forest ABG by combining multispectral optical remote sensing images and space-borne polarimetric SAR data offers considerable advantages. The results of the present study may help improve accuracy of forest AGB estimation, and it provides a reference basis for future studies on forest AGB using different remote sensing data sources.

Key words: Chinese fir forest, aboveground biomass, Sentinel-1, Sentinel-2, texture, regression model

CLC Number:

S758.5

PAN Lei, SUN Yujun, WANG Yifu, CHEN Liping, CAO Yuanshuai. Estimation of aboveground biomass in a Chinese fir (Cunninghamia lanceolata)forest combining data of Sentinel⁃1 and Sentinel⁃2[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(3): 149-156.

Figures/Tables 5

Table 1

Table 2

Table 3

Table 4

Table 5

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地上生物量水平/(Mg·hm^-2) AGB class	样地数number of plots	地上生物量/(Mg·hm^-2) aboveground biomass weight	占比/% ratio
<100	8	23.73、 29.00、 33.00、 38.90、 52.91、 75.79、 86.35、 93.62	16
≥100~200	20	100.69、 105.00、 105.14 、 113.01、 122.99 、 124.63、 127.90 、 128.64、 135.18、 144.82、 146.47、 154.32、 159.18、 161.26、 177.21、 182.20、 182.44 、 185.66、 189.07、 199.66	40
≥200~300	18	200.63、 211.47、 219.30、 219.33、 219.74、 219.04、 226.75、 245.71、 248.61、 256.27、 256.43、 262.37、 274.59、 279.18、 284.16、 286.27、 295.10、 299.83	36
≥300	4	300.75、 310.67、 394.53、 401.22	8

植被指数 vegetable index	计算公式 calculation formula
土壤调整植被指数 soil adjusted vegetation index（I_SAV）	I_SAV=1.5（ρ_nir–ρ_red）/（ρ_nir+ρ_red+0.5）
归一化植被指数 normalized difference vegetation index（I_NDV）	I_NDV=（ρ_nir–ρ_red）/（ρ_nir+ρ_red）
第二修正土壤调整植被指数 second modified soil adjusted vegetation index（I_MSAV2）	I_MSAV2=0.5[2(ρ_nir+1)-sqrt[(2ρ_nir+1)(2ρ_nir+1)-8(ρ_nir-ρ_red)]]
差值植被指数 difference vegetation index（I_DV）	I_DV=ρ_nir–ρ_red
比值植被指数 ratio vegetation index（I_RV）	I_RV=ρ_nir/ρ_red
垂直植被指数 perpendicular vegetation index（I_PV）	I_PV=sin (a)ρ_nir-cos (a)ρ_red（a=45°）
红外比率植被指数 infrared percentage vegetation index（I_IPV）	I_IPV=ρ_nir/(ρ_nir+ρ_red)
加权差异植被指数 weighted difference vegetation index（I_WDV）	I_WDV=ρ_nir–0.5 ρ_red
转换归一化植被指数 transformed normalized difference vegetation index (I_TNDV)	I_TNDV=sqrt[(ρ_nir-ρ_red)/(ρ_nir+ρ_red)+0.5]
绿度归一化植被指数 green normalized difference vegetation index (I_GNDV)	I_GNDV=(ρ_nir–ρ_green)/(ρ_nir+ρ_green)
颜色指数 colour index (I_C)	I_C=(ρ_red-ρ_green)/(ρ_red+ρ_green)
大气修正植被指数atmospherically resistant vegetation index（I_ARV）	I_ARV=(ρ_nir-r_b)/(ρ_nir+r_b) r_b=(ρ_red)-γ(ρ_blue-ρ_red), with γ=1

遥感因子类型 remote sensing factors	模型拟合参数 model fitting parameters				截距和变量的拟合参数 fitting parameters for intercept and variables
遥感因子类型 remote sensing factors	R2	R2_adj	均方根误差/ (Mg·hm^-2) RMSE	P	截距和变量 intercept & variables	系数 coefficient	系数标准误 SE. of coefficient	P	方差膨胀因子 VIF
单波段 band	0.347	0.319	74.81	4.5×10^-5	截距	258.40	43.70	3.6×10^-7***	―
					B₂	-1.69	0.36	2.3×10^-5***	2.10
					B₈	0.11	0.02	3.6×10^-5***	2.10
植被指数 VI	0.346	0.303	75.69	0.000 2	截距	812.83	331.57	0.018 1^*	―
					I_MSAV2	-332.92	158.55	0.041 2^*	2.46
					I_RV	73.46	18.51	0.000 3^***	8.76
					I_ARV	1 348.28	558.82	0.019 8^*	5.85
纹理 texture	0.477	0.455	66.94	2.4×10^-7	截距	159.13	32.92	1.5×10^-5***	—
					C_B3	128.33	27.53	2.6×10^-5***	1.00
					E_TNDVI	-67.12	18.69	0.000 8^***	1.00
所有因子 all factors	0.542	0.501	64.04	3.1×10^-7	截距	238.47	52.40	4.0×10^-5***	—
					B₂	-0.87	0.356	0.02^*	2.84
					B₁₂	0.19	0.08	0.02^*	2.99
					C_B3	124.76	26.58	2.5×10^-5***	1.10
					E_TNDVI	-75.84	18.69	0.000 2^***	1.14

模型拟合参数 model fitting parameters				截距和变量的拟合参数 fitting parameters for intercept and variables
R2	R2_adj	均方根误差/ (Mg·hm^-2) RMSE	P	截距和变量 intercept & variables	系数 coefficient	系数标准误 SE. of coefficient	P	方差膨胀因子 VIF
0.629	0.575	59.13	6.9×10^-8	截距	232.32	48.53	2.0×10^-5***	—
				B₂	-0.77	0.35	0.032 8*	3.17
				B₁₂	0.18	0.08	0.002 2*	3.08
				C_B3	108.81	25.11	8.7×10^-5***	1.11
				E_TNOVI	-83.01	17.43	2.2×10^-5***	1.16
				C_VH5	-95.60	42.84	0.030 9*	1.32
				D_VH9	17.46	6.23	0.007 3**	1.17

Estimation of aboveground biomass in a Chinese fir (Cunninghamia lanceolata)forest combining data of Sentinel⁃1 and Sentinel⁃2

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波段号 band No.	中心波长/nm wave length	波段宽度/nm band width	空间分辨率/m spatial resolution
1	443	20	60
2	490	65	10
3	560	35	10
4	665	30	10
5	705	15	20
6	740	15	20
7	783	20	20
8	842	115	10
8b	865	20	20
9	945	20	60
10	1 380	30	60
11	1 610	90	20
12	2 190	180	20