Sentinel-2A多特征变量反演针叶林地上生物量能力评估

郭正齐; 张晓丽; 王月婷

doi:10.12171/j.1000-1522.20200097

Sentinel-2A多特征变量反演针叶林地上生物量能力评估

北京林业大学林学院，北京 100083

基金项目: 国家重点研发计划项目（2017YFD0600902）

详细信息

作者简介:
郭正齐。主要研究方向：资源监测与信息化管理。Email：guozhengqi94@bjfu.edu.cn　地址：100083 北京市海淀区清华东路35号北京林业大学林学院

责任作者:
张晓丽，教授，博士生导师。主要研究方向：定量遥感。Email：zhang-xl@263.net　地址：同上

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出版历程
- 收稿日期: 2020-04-02
- 修回日期: 2020-04-20
- 网络出版日期: 2020-10-13
- 发布日期: 2020-12-13

Ability evaluation of coniferous forest aboveground biomass inversion using Sentinel-2A multiple characteristic variables

School of Forestry, Beijing Forestry University, Beijing 100083, China

摘要

摘要:
目的森林生物量是衡量森林碳储量的关键因子，准确估算生物量对掌握森林现状和森林资源合理利用具有重要意义。欧空局发射Sentinel-2A数据因其丰富的光谱信息和较高的空间分辨率为生物量的反演和监测提供了新的机会。本文旨在评估基于Sentinel-2A的各类特征变量反演针叶林地上生物量的能力以及完成区域尺度的针叶林地上生物量定量估测。
方法试验以内蒙古赤峰市喀喇沁旗旺业甸林场针叶林为研究对象，以Sentinel-2A为主要数据源，提取了10个波段反射率、20个植被指数和5个生物物理参数共3种类型变量，分别建立基于光谱反射率、植被指数、生物物理参数，以及融合3类变量的多元逐步回归生物量估算模型，同时每组均加入高程因子分析地形对估算精度的影响。
结果（1）基于多种类型参数建立的模型估算效果最好，模型决定系数达到0.765，均方根误差为39.49 t/hm²；（2）在3组单类型变量模型中，基于植被指数的预测结果最好，说明相比于波段反射率和生物物理参数，植被指数对针叶林地上生物量的估算贡献更大；（3）无论基于何种类型参数建模，高程信息的加入都会提高针叶林地上生物量的估算精度。
结论基于Sentinel-2A植被指数与地形特征的针叶林地上生物量反演模型较好，可用于区域生物量估算。该研究对区域性森林资源监测的实际应用具有指导意义。
- Sentinel-2A /
- 地上生物量 /
- 多元逐步回归 /
- 生物物理参数 /
- 红边波段
Abstract:
Objective Forest biomass is the key factor to measure forest carbon reserves. Therefore, accurate estimation of forest biomass is helpful for forest management and resource utilization. The data from Sentinel-2A provide new opportunities for biomass estimation and monitoring due to rich spectral information and high spatial resolution. In this paper, we evaluated the ability of various parameters to estimate aboveground biomass of coniferous forest , and completed regional-scale forest biomass estimation based on Sentinel-2A.
Method Selecting the coniferous forest of Wangyedian Forest Farm in Chifeng City, Inner Mongolia of northern China as the research object, this research extracted spectral reflectance, vegetation index and biophysical parameters in Sentinel-2A. Then we set up multiple stepwise regression equations by data types to estimate biomass. In addition, the elevation factor was added to improve the accuracy of model.
Result The results showed that: (1) the model built with multiple types of parameters had the highest accuracy, with R² reaching 0.765 and RMSE was 39.49 t/ha; (2) among the models established by spectral reflectance, vegetation index and biophysical parameters, the accuracy based on the vegetation index was higher, indicating that the vegetation index had a greater impact on coniferous forest aboveground biomass estimation than the spectral reflectance and biophysical parameters; (3) for all the models of our research, elevation always improved accuracy.
Conclusion The retrieved aboveground biomass from Sentinel-2A spatial distribution is basically consistent with the actual situation, which shows that the coniferous forest aboveground biomass inversion is meaningful for regional forest resource monitoring.
- Sentinel-2A /
- aboveground biomass /
- multiple stepwise regression /
- biophysical parameter /
- red edge band

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图 1 研究区位置及调查样地分布图

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

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图 2 技术路线图

Figure 2. Technology flowchart

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图 3 预估生物量与实测生物量的散点图与残差图

Figure 3. Scatter plots and residual plots of predicted and measured biomass

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图 4 旺业甸林场针叶林生物量反演分布图

Figure 4. Biomass inversion distribution of coniferous forest in Wangyedian Forest Farm

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表 1 落叶松、油松的生物量计算模型

Table 1 Biomass calculating models of Larix gmelinii and Pinus tabuliformis

树种(组) Tree species (group)	生物量模型和参数 Biomass model and parameter
落叶松 Larix gmelinii	${W}_{{\rm{r}}}=0.0462\;38{\left({D}^{2}H\right)}^{0.905\;002}$
油松 Pinus tabuliformis	${W}_{{\rm{S}}}=0.0276\;36{\left({D}^{2}H\right)}^{0.990\;5};$ ${W}_{{\rm{B}}}=0.00913\;13{\left({D}^{2}H\right)}^{0.982};$ ${W}_{{\rm{L}}}=0.00457\;55{\left({D}^{2}H\right)}^{0.989\;4};$ ${W}_{{\rm{r}}}={W}_{{\rm{S}}}+{W}_{{\rm{B}}}+{W}_{{\rm{L}}}$
注： ${W}_{{\rm{S}}}$ 、 ${W}_{{\rm{B}}}$ 、 ${W}_{{\rm{L}}}$ 、 ${W}_{{\rm{r}}}$ 分别为树干生物量、树枝生物量、树叶生物量、地上部分总生物量，t/hm²；D为胸径，cm；H为树高，m。Notes: ${W}_{{\rm{S}}}$ , ${W}_{{\rm{B}}}$ , ${W}_{{\rm{L}}}$ , ${W}_{{\rm{r}}}$ indicate stem biomass, branch biomass, leaf biomass, total aboveground biomass, respectively, t/ha; D is DBH, cm; H is tree height, m.

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表 2 生态变量列表

Table 2 List of ecological variables

数据源 Data source	类别 Type	变量名称 Variable name	属性 Attribute	公式 Formula
Sentinel-2A	波段信息 Band information	B2	蓝色 Blue (波长 Wavelength (WL), WL = 490 nm)
		B3	绿色 Green (WL = 560 nm)
		B4	红色 Red (WL = 665 nm)
		B5	红边波段 Red edge band (WL = 705 nm)
		B6	红边波段 Red edge band (WL = 749 nm)
		B7	红边波段 Red edge band (WL = 783 nm)
		B8	近红外 Near infrared (WL = 842 nm)
		B8a	近红外 Near infrared (WL = 865 nm)
		B11	短波红外 Shortwave infrared (WL = 1 610 nm)
		B12	短波红外 Shortwave infrared (WL = 2 190 nm)
	植被指数 Vegetation index	RVI	比值植被指数 Ratio vegetation index	B8/B4
		DVI	差值植被指数 Difference vegetation index	B8 − B4
		WDVI	权重差值植被指数 Weighted difference vegetation index	${\rm{B}}8 - 0.5\times {\rm{B}}4$
		IPVI	红外植被指数 Infrared vegetation index	B8/(B8 + B4)
		PVI	垂直植被指数 Perpendicular vegetation index	$\sin (45^\circ)\times {\rm{B}}8 - \cos (45^\circ)\times {\rm{B}}4$
		NDVI	归一化差值植被指数 Normalized difference vegetation index	(B8 − B4)/(B8 + B4)
		NDVI45	B4和B5归一化差值植被指数 NDVI with band4 and band5	(B5 − B4)/(B5 + B4)
		GNDVI	绿波归一化差值植被指数 NDVI of green band	(B7 − B3)/(B7 + B3)
		IRECI	反红边叶绿素指数 Inverted red edge chlorophyll index	(B7 − B4)/(B5/B6)
		SAVI	土壤调节植被指数 Soil adjusted vegetation index	1.5 × (B8 − B4)/8 × (B8 + B4 + 0.5)
		TSAVI	转化土壤调节植被指数 Transformed soil adjusted vegetation index	0.5 × (B8 − 0.5 × B4 − 0.5)/(0.5 × B8 + B4 − 0.15)
		MSAVI	修正型土壤调节植被指数 Modified soil adjusted vegetation index	$\begin{array}{l}\left ( {2 - {\rm{NDVI}} \times {\rm{WDVI}}} \right) \times \left ( {{\rm{B}}8 - {\rm{B}}4} \right)/ \\8 \times \left ( {{\rm{B}}8 + {\rm{B}}4 + 1 - {\rm{NDVI}} \times {\rm{WDVI}}} \right)\end{array}$
		MSAVI2	二次修正型土壤调节植被指数 Secondly modified soil adjusted vegetation index	$0.5 \times \left[ {2 \times \left ( {{\rm{B}}8{\rm{}} + {\rm{}}1} \right) - \sqrt {{{\left ( {2 \times {\rm{B}}8 + 1} \right)}^2} - 8 \times \left ( {{\rm{B}}8 - {\rm{B}}4} \right)} } \right]$
		ARVI	大气阻抗植被指数 Atmospherically resistant vegetation index	${\rm{B}}8- (2\times {\rm{B}}4-{\rm{B}}2)/{\rm{B}}8+ (2\times {\rm{B}}4-{\rm{B}}2)$
		PSSRa	特定色素简单比值植被指数 Pigment specific simple ratio chlorophyll index	B7/B4
		MTCI	Meris陆地叶绿素指数 Meris terrestrial chlorophyll index	(B6 − B5)/(B5 − B4)
		MCARI	修正型叶绿素吸收比植指数 Modified chlorophyll absorption ratio index	$\left[\left ({\rm{B}}5-{\rm{B}}4\right)-0.2\times ({\rm{B}}5-{\rm{B}}3)\right]\times ({\rm{B}}5-{\rm{B}}4)$
		S2REP	“哨兵2号”红边位置指数 Sentinel-2 red edge position index	$705+35\times \left[\dfrac{\left ({\rm{B}}4+{\rm{B}}7\right)}{2}-{\rm{B}}5\right]\times ({\rm{B}}6-{\rm{B}}5)$
		REIP	红边感染点指数 Red edge infection point index	$700+40\times \left[\dfrac{\left ({\rm{B}}4+{\rm{B}}7\right)}{2}-{\rm{B}}5\right]/ ({\rm{B}}6-{\rm{B}}5)$
		GEMI	全球环境监测指数 Global environmental monitoring index	$\begin{array}{l}{\rm{eta}} \times \left ( {1 - 0.25 \times {\rm{eta}}} \right) - \dfrac{{{\rm{B}}4 - 0.125}}{{1 - {\rm{B}}4}}, \\{\rm{eta}}= [ 2 \times \left ( {{\rm{B}}8{\rm{A}} - {\rm{B}}4} \right) + 1.5 \times {\rm{B}}8{\rm{A}} + 0.5 \times {\rm{B}}4 ]/\\ \left ( {{\rm{B}}8{\rm{A}} + {\rm{B}}4 + 0.5} \right)\end{array}$
	生物物理参数 Biophysical parameter	LAI	叶面积指数 Leaf area index
		FVC	植被覆盖度 Vegetation coverage
		FAPAR	有效光合吸收辐射度 Effective photosynthetically absorbed radiance
		Cab	叶绿素含量 Chlorophyll content
		Cwc	冠层水分含量 Canopy water content
SRTM DEM	地形指数 Topographic index	H	高程 Elevation

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表 3 变量与地上生物量之间的相关性分析

Table 3 Correlation analysis of aboveground biomass and variables

变量 Variable	相关系数 Correlation coefficient	变量 Variable	相关系数 Correlation coefficient	变量 Variable	相关系数 Correlation coefficient	变量 Variable	相关系数 Correlation coefficient
B2	−0.590**	RVI	0.748**	TSAVI	−0.092	LAI	0.527**
B3	−0.588**	DVI	−0.352*	MSAVI	−0.418**	FVC	−0.123
B4	−0.526**	WDVI	−0.405**	MSAVI2	−0.267	FAPAR	0.528**
B5	−0.572**	IPVI	0.582**	ARVI	−0.534**	Cab	0.459**
B6	−0.489**	PVI	0.462**	PSSRa	0.757**	Cwc	0.643**
B7	−0.417**	NDVI	0.582**	MTCI	0.700**	H	0.425**
B8	−0.442**	NDVI45	0.569**	MCARI	−0.502**
B8a	−0.444**	GNDVI	0.685**	S2REP	0.105
B11	−0.582**	IRECI	0.589**	REIP	0.620**
B12	−0.585**	SAVI	−0.393**	GEMI	−0.311*
注：代表显著性水平为0.05，代表显著性水平为0.01。下同。Notes: means the significance level is 0.05 and ** is 0.01. Same as below.

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表 4 模型评价结果

Table 4 Evaluation results of models

数据源 Data source	类别 Type	预测变量 Predictive variable	决定系数 R²	显著性 Significance	均方根误差/(t·hm⁻²) RMSE/(t·ha⁻¹)
Sentinel-2A	波段信息 Band information	B4 + B12	0.465	< 0.01	49.27
	波段信息 Band information	B4 + B12 + H	0.523	< 0.01	47.33
	植被指数 Vegetation index	PSSRa + AVRI	0.601	< 0.01	44.08
	植被指数 Vegetation index	PSSRa + AVRI + H	0.682	< 0.01	41.14
	生物物理参数 Biophysical parameter	LAI + FAPAR + Cwc	0.506	< 0.01	46.31
	生物物理参数 Biophysical parameter	LAI + FAPAR + Cwc + H	0.604	< 0.01	44.62
	不分组 No grouping	PSSRa + AVRI + Cwc	0.673	< 0.01	41.84
	不分组 No grouping	PSSRa + AVRI + Cwc + H	0.765	< 0.01	39.49

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表 5 模型精度评价表

Table 5 Evaluation of model accuracy

类别 Type	模型方程 Model equation	均方根误差/(t·hm⁻²) RMSE/(t·ha⁻¹)	相对均方根误差 Relative root mean square error (rRMSE)/%	平均绝对百分误差 Mean absolute percentage error/%
波段信息 Band information	Y = −104.388 − 47 558.708 × B12 + 20 487 × B4 + 0.255 × H	47.33	33.97	33.03
植被指数 Vegetation index	Y = 214.919 + 22.950 × PSSRa − 735.420 × AVRI + 0.176 × H	41.14	29.53	25.98
生物物理参数 Biophysical parameter	Y = −1 059.178 + 8 097.090 × Cwc − 1 219.432 × LAI + 4 037.249 × FAPAR + 0.441 × H	44.62	32.03	29.73
不分组 No grouping	Y = 147.724 + 19.581 × PSSRa − 770.512 × AVRI + 1 593.239 × Cwc + 0.230 × H	39.49	28.35	27.96

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