Estimation of Aboveground Biomass of Arbor Forest Based on Landsat 8 Image

doi:10.13466/j.cnki.lyzygl.2018.06.007

Abstract

Abstract:

With the Landsat 8 image and 96 field survey data in Zhejiang Province as the data source,the vegetation indices,texture features,terrain factors were extracted and optimized,and the preferred variables were selected.The factors were grouped into two sets of independent variables:vegetation indices,vegetation indices+ texture features.The least squares regression method was used to construct the aboveground biomass estimation model of arbor forest,and the estimation results were compared and analyzed.The results show that in the constructed models,the model accuracy of vegetation indices + texture features set is better than the model constructed by the vegetation indices set.The result indicates that the texture features of the multispectral bands have the effect of improving the model estimation accuracy.In addition,the variable screening method adopted in this paper takes into account the correlation between variables and the collinearity problem,which can improve the stability of the constructed model.

Key words: arbor forest, biomass model, vegetation index, texture feature, Landsat 8 image

CLC Number:

REN Yi, WANG Haibin, XU Dengping. Estimation of Aboveground Biomass of Arbor Forest Based on Landsat 8 Image[J]. FOREST RESOURCES WANAGEMENT, 2018, (6): 38-44.

Figures/Tables 9

Fig.1

Tab.1

Tab.2

Modeling variable factors

变量类型	变量名称	计算公式
植被指数	归一化植被指数NDVI	$NDVI = NIR - RED NIR + RED$
	比值植被指数RVI	$RVI = NIR / RED$
	差值植被指数DVI	$DVI = NIR - RED$
	比值植被指数1 RVI54	$RVI 54 = SWIR 1 / NIR$
	比值植被指数2 RVI64	$RVI 64 = SWIR 2 / NIR$
	土壤植被指数SAVI	$SAVI = (1 + L) (NIR - RED) (NIR + RED + L)$
	优化土壤调整指数NLI	$NLI = NI R 2 - RED NI R 2 + RED$
	大气抗阻植被指数ARVI	$ARVI = (NIR - RED + r (BLUE - RED)) (NIR + RED - r (BLUE - RED))$
	增强植被指数EVI	$EVI = G × NIR - RED NIR + C 1 × RED - C 2 × BLUE + L$
纹理特征	平均值	$Mean = ∑ i,j=0 N-1 i (P i, j)$
	方差	$Variance = ∑ i,j=0 N-1 i P i, j (i - Mean) 2$
	均一性	$Homogeneity = ∑ i,j=0 N-1 i P i, j 1 + (i - j) 2$
	对比度	$Contrast = ∑ i,j=0 N-1 1 i P i, j (i - j) 2$
	相异性	$Dissimilarity = ∑ i,j=0 N-1 i P i, j i - j$
	熵	$Entropy = ∑ i,j=0 N-1 i P i, j - ln P i, j$
	角二阶矩	$SecondMoment = ∑ i,j=0 N-1 i P i, j 2$
	相关性	$Correlation = ∑ i,j=0 N-1 i P i, j (i - Mean) (j - Mean) Varianc e i Varianc e j$
地形因子	海拔
	坡度
	坡向

Tab.2

Tab.3

Pearson correlation coefficients between independent variables and arbor forest above ground biomass

变量	相关系数	变量	相关系数	变量	相关系数
NDVI	0.644^**	Greenness	0.306^**	B6_Mean	-0.320^**
RVI	0.678^**	海拔	0.321^**	B6_Variance	-0.345^**
RVI54	-0.400^**	B1_Mean	-0.336^**	B6_Contrast	-0.295^**
RVI64	-0.398^**	B2_Mean	-0.342^**	B6_ $Diss$	-0.277^**
SAVI	0.644^**	B3_Mean	-0.407^**	B6_Entropy	-0.318^**
NLI	0.387^**	B3_Entropy	-0.321^**	B6_Second	0.285^**
ARVI	0.646^**	B3_Second	0.316^**
PCA2	-0.500^**	B3_Correlation	0.282^**

Tab.3

Tab.4

Tab.5

Tab.6

Tab.7

Fig.2

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波段序号	波长范围/um	波段名称	分辨率/m
1	0.433~0.453	气溶胶	30
2	0.450~0.515	蓝	30
3	0.525~0.600	绿	30
4	0.630~0.680	红	30
5	0.845~0.885	近红外	30
6	1.560~1.660	短波红外	30
7	2.100~2.300	短波红外	30
8	0.500~0.680	全色	15
9	1.136~1.390	卷云	30

自变量集	碳储量模型
植被指数	RVI,NLI,海拔
植被指数+纹理特征	RVI,NLI,海拔,B1_Mean,B3_ Correlation,B6_Mean,B6_Variance

自变量集	碳储量模型
植被指数	5.8452×RVI+1.6765×10⁴×NLI-0.0122×海拔-1.6763×10⁴
植被指数+纹理特征	2.9581×RVI+4.2002×10⁴×NLI+0.0030×海拔-4.5295×B1_Mean+10.0608×B3_ Correlation-7.7782×B6_Mean-9.8515×B6_Variance-4.1966×10⁴

自变量集	R²	RMSE/(t/hm²)	MAE/(t/hm²)	RE/%
植被指数	0.4069	20.53	0.17	0.43
植被指数+纹理特征	0.4212	20.30	0.10	0.27