基于Landsat 8影像的乔木林地上生物量估算

doi:10.13466/j.cnki.lyzygl.2018.06.007

摘要/Abstract

摘要：

以浙江省内的一景Landsat 8影像和96块野外调查数据为数据源,提取出植被指数、纹理特征、地形因子并进行优选,对优选后的变量因子进行分组,生成植被指数、植被指数+纹理特征相结合的2种自变量集,采用偏最小二乘回归法构建乔木林地上生物量估算模型,并对估算结果进行对比分析。结果显示:在构建的模型中,植被指数+纹理特征集构建的模型精度均要优于植被指数集构建的模型,说明多光谱波段的纹理特征具有改善模型估算效果的作用。采用的变量筛选方法较好地考虑了变量间的相关性及共线性问题,可以提高所构建模型的稳定性。

关键词: 乔木林, 生物量模型, 植被指数, 纹理特征, Landsat 8影像

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

中图分类号:

任怡, 王海宾, 许等平. 基于Landsat 8影像的乔木林地上生物量估算[J]. 林业资源管理, 2018,(6): 38-44.

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.

图/表 9

图1

表1

表2

建模变量因子

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

表2

表3

自变量因子与乔木林地上生物量的皮尔森相关系数

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^**

表3

表4

表5

表6

图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	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