Remote Sensing Estimation of Biomass of Pinus kesiya var.langbianensis by Geographically Weighted Regression Models

doi:10.13466/j.cnki.lyzygl.2017.01.015

Abstract

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)

CLC Number:

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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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