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

Tab.3

Tab.4

Tab.5

Tab.6

Tab.7

Fig.2

References 23

[1]	Cao Lin, CoopsNicholas C, Innes John L, et al. Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data[J]. Remote Sensing of Environment, 2016,178:151-171.
[2]	范文义, 张海玉, 于颖, 等. 三种森林生物量估测模型的比较分析[J]. 植物生态学报, 2016,35(4):402-410.
[3]	王海宾, 侯瑞萍, 郑冬梅, 等. 基于地理加权回归模型的亚热带地区乔木林生物量估算[J].农业机械学报, 2018(6):184-190.
[4]	刘茜, 杨乐, 柳钦火, 等. 森林地上生物量遥感反演方法综述[J]. 遥感学报, 2015,19(1):62-74.
[5]	Lu D, Chen Q, Wang G, et al. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems[J]. International Journal of Digital Earth, 2014,9(1):63-105. doi: 10.1080/17538947.2014.990526
[6]	戚玉娇. 大兴安岭森林地上碳储量遥感估算与分析[D]. 哈尔滨:东北林业大学, 2014.
[7]	Lu, D., Chen Q., Wang, G., et al. Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates[J]. International Journal of Forestry Research, 2012,2012(2):1-16.
[8]	杜华强. 竹林生物量碳储量遥感定量估算[M]. 北京: 科学出版社, 2012.
[9]	韩爱惠. 森林生物量及碳储量遥感监测方法研究[D]. 北京:北京林业大学, 2009.
[10]	沈楚楚. 浙江省主要树种(组)生物量转换系数研究[D]. 杭州:浙江农林大学, 2013.
[11]	陶立超. 白马林场森林碳储量遥感估测[D]. 北京:北京林业大学, 2014.
[12]	许等平, 李晖, 智长贵, 等. 基于CEBERS-WFI遥感数据的森林生物量估测方法研究[J].林业资源管理, 2010(3):104-109.
[13]	Sarker L R, Nichol J E. Improved forest biomass estimates using ALOS AVNIR-2 texture indices[J]. Remote Sensing of Environment, 2011,115(4):968-977. doi: 10.1016/j.rse.2010.11.010
[14]	Nichol J E, Sarker M L R. Improved biomass estimation using the texture parameters of two high-resolution optical sensors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011,49:930-948. doi: 10.1109/TGRS.2010.2068574
[15]	王月婷, 张晓丽, 杨慧乔, 等. 基于Landsat 8卫星光谱与纹理信息的森林蓄积量估算[J]. 浙江农林大学学报, 2015,32(3):384-391. doi: 10.11833/j.issn.2095-0756.2015.03.008
[16]	Eckert S. Improved forest biomass and carbon estimations using texture measures from worldView-2 satellite data[J]. Remote Sensing, 2012,4(4):810-829. doi: 10.3390/rs4040810
[17]	Meng J H, Li S M, Wang W, et al. Estimation of forest structural diversity using the spectral and textural information derived from SPOT-5 satellite images[J]. Remote Sensing, 2016,8(2):125. doi: 10.3390/rs8020125
[18]	Sarker L R, Nichol J E. Improved forest biomass estimates using ALOS AVNIR-2 texture indices[J]. Remote Sensing of Environment, 2011,115(4):968-977. doi: 10.1016/j.rse.2010.11.010
[19]	王惠文, 吴载斌, 孟洁. 偏最小二成回归的线性与非线性方法[M]. 北京: 国防工业出版社, 2006.
[20]	张超, 彭道黎, 涂云燕, 等. 利用TM影像和偏最小二乘回归方法估测三峡库区森林蓄积量[J]. 北京林业大学学报, 2013,35(3):11-17.
[21]	Li X C, Zhang Y J, Bao Y S, et al. Exploring the best hyperspectral Features for LAI estimation using partial least squares regression[J]. Remote Sensing, 2014,6(7):6221-6241. doi: 10.3390/rs6076221
[22]	Kelsey K C, Neff J C. Estimates of aboveground biomass from texture analysis of Landsat imagery[J]. Remote Sensing, 2014,6(7):6407-6422. doi: 10.3390/rs6076407
[23]	徐小军, 周国模, 杜华强, 等. 基于Landsat TM数据估算雷竹林地上生物量[J]. 林业科学, 2011,47(9):1-6. doi: 10.11707/j.1001-7488.20110901

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

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

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^**

变量	相关系数	变量	相关系数	变量	相关系数
RVI	0.678^**	B1_Mean	-0.336^**	B6_Variance	-0.345^**
NLI	0.387^**	B3_Correlation	0.282^**
海拔	0.321^**	B6_Mean	-0.320^**

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