基于地理加权回归模型的思茅松生物量遥感估测

doi:10.13466/j.cnki.lyzygl.2017.01.015

摘要/Abstract

摘要：

通过调查云南省景谷县思茅松林120株单木数据,构建思茅松单木生物量模型。结合2005年景谷县TM影像数据及2006年森林资源二类调查小班数据,采用普通最小二乘模型(OLS)和地理加权回归模型(GWR)的方法构建思茅松生物量遥感估测模型。结果表明:地理加权回归模型比普通最小二乘模型具有更好的拟合效果,其决定系数(R ²)显著高于OLS模型,Akaike信息指数(AIC)相比降低7.832;两种模型通过独立样本检验可以看出,模型预估精度从OLS模型的72.70%提高至GWR模型的75.06%;通过GWR模型反演计算,研究区内思茅松林单位面积生物量为49.02t/hm ²,比实测数据低1.229%,与实测数据基本吻合,且估算误差优于OLS模型;基于GWR模型估算的景谷县思茅松林总生物量为2.101×10 ⁷ t。可见基于地理加权回归方法估测森林地上生物量的方法是有效的,能提高森林生物量遥感估测模型的拟合和预估精度,可以用于思茅松林的生物量的遥感估算。

关键词: 思茅松, 生物量, 遥感, 普通最小二乘, 地理加权回归

Abstract:

The Biomass model of Simao pine(Pinus kesiya var.langbianensis)was built based on the data collected from 120 Simao pine sampling trees,Landsat TM images in 2005 and the data of forest resource inventory in 2006 in Jinggu County,Yunnan Province.Then the remote sensing biomass estimation Model of Simao Pine were built by the ordinary least square(OLS)and geographically weighed regression(GWR).The results showed that:GWR model had a better fitting effect than OLS,in which coefficient of determination(R ²)was significantly bigger than the OLS model,Akaike information index(AIC)reduced by 7.832;It was obviously depicted from the sample test of independence that model prediction accuracy was improved from 72.70%(OLS)to 75.06%(GWR).The unit-area biomass was 49.02t / hm ² by inversion,and basically consistent with the measured data;it was lower than the measured data 1.229%,and less than the estimation value of OLS.The total biomass of Simao pine in Jinggu County was 2.101×10 ⁷ t based on GWR model.The study indicated that forest aboveground biomass estimation based on geographically weighed regression(GWR)model could improve effectively the fitting accuracy of forest biomass estimation model,and could be used to estimate the biomass of Simao pine forest by remote sensing.

Key words: Pinus kesiya var.langbianensis, biomass, remote sensing, ordinary least square(OLS), Geographically Weighted Regression(GWR)

中图分类号:

闾妍宇, 李超, 欧光龙, 熊河先, 魏安超, 张博, 胥辉. 基于地理加权回归模型的思茅松生物量遥感估测[J]. 林业资源管理, 2017, 0(1): 82-90.

LÜ Yanyu, LI Chao, OU Guanglong, XIONG Hexian, WEI Anchao, ZHANG Bo, XU Hui. Remote Sensing Estimation of Biomass of Pinus kesiya var.langbianensis by Geographically Weighted Regression Models[J]. FOREST RESOURCES WANAGEMENT, 2017, 0(1): 82-90.

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参考文献 19

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序号	模型	样本容量/个	模型参数			R²
序号	模型	样本容量/个	a	b	c	R²
1	W=a×D^b	120	0.059	2.541	0.467	0.869
2	W=a×D^b×H^c	120	0.058	2.120		0.900
3	W=a×(D²×H)^b	120	0.097	0.817		0.882

遥感因子	生物量	遥感因子	生物量	遥感因子	生物量	遥感因子	生物量
TM1	-0.228^*	PCA_2	-0.215^*	Tm2	-0.271^**	ME1_9	-0.208^*
TM7	-0.270^*	PCA_1	0.248^*	VIS123	-0.270^*	ME2_9	-0.271^**
TM5_45	-0.255^*	Third	0.258^*	SM4_5	0.210^*	ME3_9	-0.309^**
TM5	-0.271^**	Brightness	-0.262^*	ME1_5	-0.242^*	ME5_9	-0.325^**
ND57	0.253^*	ME1_7	-0.218^*	ME2_5	-0.276^**	ME7_9	-0.337^**
ND452	0.228^*	ME2_7	-0.281^**	ME3_5	-0.309^**	ME2_15	-0.232^*
ND32	-0.284^**	ME3_7	-0.323^**	ME5_5	-0.296^**	ME3_15	-0.272^**
TM3	-0.282^**	ME5_7	-0.332^**	EN5_5	-0.215^*	ME5_15	-0.278^**
Albedo	-0.263^*	ME7_7	-0.349^**	ME7_5	-0.306^**	ME7_15	-0.285^**

模型	样本容量	R²	RMSE	AIC
OLS	71	0.152	0.528	74.781
GWR	71	0.380	0.344	66.949

对比项	Sum Sq.	df		Mean Sq.	F
OLS模型残差	10.952	69.000		—	—
GWR模型改善	2.945	10.228		0.288	—
GWR模型残差	8.008	58.772	0.136	2.113

变量	参数β	标准误	(β-1)×SD	(β+1)×SD
截距	1.845	0.047	1.798	1.892
ME7_7	-0.164	0.047	-0.211	-0.117