基于空间回归模型的思茅松林生物量遥感估测及光饱和点确定

doi:10.11707/j.1001-7488.20200305

Abstract

Abstract:

Objective: This paper aims to determinate the light saturation point of biomass estimation using remote sensing data of Simao pine(Pinus kesiya var. langbianensis)forest, and construct the spatial global model and the local remote sensing information model, then inverse the biomass of Simao pine forest. The results will provide a reference for estimating biomass of Simao pine using remote sensing data. Method: This study calculated the light saturation point of Simao pine forest biomass using the quadratic term function and power function model based on Landsat8 OLI images, and the forest resource inventory data in 2016 of Pu'er city, Yunnan Province, were used to construct the remote sensing information model with the ordinary least square(OLS), spatial lag model(SLM), spatial error model(SEM)and geographically weighed regression(GWR), and then the aboveground biomass of Simao pine forest was estimated. Result: 1) The light saturation point of aboveground biomass estimated by Landsat 8 OLI remote sensing in Simao pine forest of Pu'er city was 106.3 t·hm^-2. 2) The highest coefficient of determination(R²=0.373)and the lowest Akaike information index(AIC=4 577.8)of GWR model indicated that it has the better fitting performance than OLS, SLM and SEM model. 3) The results of the independent sample test showed that the estimation accuracy of GWR model was higher than those of OLS, SLM and SEM model, especially at the high value stage (above 100 t·hm^-2) and the low value stage (below 50 t·hm^-2) based on the Jackknife method test. 4) The aboveground biomass by inversion using GWR model was 66.496 t·hm^-2, and its estimation error with the measured data was 23.511%, which was less than the estimation values of OLS, SLM and SEM model. Conclusion: GWR model is superior to OLS model and other spatial global regression models in estimating biomass of Simao pine forest in Pu'er city by remote sensing. Moreover, GWR model can solve the problems of high-value under estimation and low-value over estimation to a certain extent, and reduce the influences of the light saturation point on the accuracy of remote sensing estimation.

Key words: aboveground biomass, light saturation point, spatial regression model, forest resource inventory data, Pinus kesiya var. langbianensis forest

CLC Number:

S757

Lü Zhou,Guanglong Ou,Junfeng Wang,Hui Xu. Light Saturation Point Determination and Biomass Remote Sensing Estimation of Pinus kesiya var. langbianensis Forest Based on Spatial Regression Models[J]. Scientia Silvae Sinicae, 2020, 56(3): 38-47.

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References 0

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变量Variables	公式或说明A formula or description
植被指数Vegetation index
NDVI	(B5-B4)/(B5+B4)
SRI	B5/B4
SAVI	1.5×(B5-B4)/(B5+B4+0.5)
PVI	0.939B5-0.344B4+0.9
BVI	0.272 8B2+0.249 3B3+0.480 6B4+0.556 8B5+0.443 8B6+0.170 6B7
GVI	-0.272 8B2-0.217 4B3-0.550 8B4+0.722 1B5+0.073 3B6-0.164 8B7
WVI	0.144 6B2+0.176 1B3+0.332 2B4+0.339 6B5-0.621 0B6-0.418 6B7
IIVI	(B5-B6)/(B5+B6)
DVI	B5-B4
ARVI	(B4-2B3+B1)/(B4+2B3-B1)
TVI	$\sqrt {{\rm{NDVI + 0}}{\rm{.5}}} $
MVI5	(B5+B4-B2)/(B5+B4+B2)
MVI7	(B5-B7)/(B5+B7)
Albedo	0.356B1+0.130B3+0.373B4+0.85B5+0.072B7-0.0018
信息增强因子Information enhancement factor
KT1	0.304B2+0.279B3+0.474B4+0.559B5+0.508B6+0.186B7
KT2	0.285B2-0.244B3-0.212B4+0.787B5-0.421B6-0.372B7
KT3	0.151B2+0.197B3+0.328B4+0.341B5-0.711B6-0.457B7
PCA1	0.135B1-0.043B2+0.356B3-0.014B4+0.561B5+0.287B6+0.677B7
PCA2	0.163B1-0.062B2+0.392B3-0.019B4+0.470B5+0.237B6-0.733B7
PCA3	0.217B1-0.048B2+0.448B3-0.015B4+0.048B5-0.863B6+0.045B7
PCA4	0.290B1-0.149B2+0.563B3-0.024B4-0.677B5+0.339B6+0.055B7
PCA5	0.032B1+0.934B2+0.025B3+0.143B4-0.032B5+0.038B6-0.004B7
PCA6	0.667B1-0.122B2-0.361B3-0.639B4+0.035B5-0.001B6+0.006B7
PCA7	0.523B1-0.288B2-0.268B3+0.755B4+0.037B5-0.003B6+0.002B7

序号 Number	变量 Variables	相关性 Correlation
1	B1	-0.470^**
2	B2	-0.470^**
3	B3	-0.461^**
4	B4	-0.424^**
5	B5	-0.223^**
6	B6	-0.417^**
7	B7	-0.395^**
8	SRI	0.347^**
9	GVI	-0.350^**
10	BVI	-0.321^**
11	WVI	0.278^**
12	PCA1	-0.432^**
13	PCA2	-0.145^**
14	PCA3	0.327^**
15	PCA4	-0.173^**
16	PCA5	-0.466^**
17	PCA6	0.327^**
18	PCA7	-0.459^**
19	KT1	-0.447^**
20	KT2	0.416^**
21	KT3	0.247^**
22	IIVI	0.307^**
23	MVI5	0.307^**
24	SAVI	0.311^**
25	TVI	0.351^**
26	NDVI	0.351^**
27	MVI7	-0.198^**
28	DVI	0.027
29	PVI	-0.113^**
30	Albedo	-0.456^**
31	ARVI	-0.386^**

变量Variables	函数Function	R²
B1	二次项函数Quadratic function	0.284
B1	幂函数Power function	0.265
SAVI	二次项函数Quadratic function	0.191
SAVI	幂函数Power function	0.155
PCA2	二次项函数Quadratic function	0.045
PCA2	幂函数Power function	0.049

参数 Parameter	OLS	SLM	SEM	GWR
P-LM	—	0.000 0	0.000 0	—
LM	—	50.342 0	63.400 9	—
P-RLM	—	0.073 1	0.000 0	—
RLM	—	3.212 5	16.271 4	—
AIC	4 628.7	4 599.7	4 596.3	4 577.8
Moran’s I	0.094	0.007 2	-0.015 9	0.020
P	0.000	0.206	0.107	0.037
R²	0.236	0.288	0.294	0.373

对比项Contrast	平方和残差Sum Sq.	DF	均方残差Mean Sq.	F
OLS模型残差OLS model residuals	301 981.823	496.000	—	—
GWR模型改善GWR model improvement	54 459.958	32.236	1 689.435	—
GWR模型残差GWR model residuals	247 521.865	463.764	533.723	3.165

Light Saturation Point Determination and Biomass Remote Sensing Estimation of Pinus kesiya var. langbianensis Forest Based on Spatial Regression Models

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变量 Variables	参数 Parameters	标准误 SE	T(Est/SE)
截距Intercept	114.773	8.361	13.727
SAVI	0.000 003	0.000 001	3.076
PCA2	0.015 6	0.005 93	2.623
B1	-0.061 4	0.011 3	-5.444

变量 Variables	最小值 Min.	上四分位数 Upper quartile	中位数 Median	下四分位数 Lower quartile	最大值 Max.
截距Intercept	-45.919	110.198	129.785	140.332	163.636
SAVI	-0.000 007	0.000 002	0.000 003	0.000 004	0.000 008
PCA2	-0.007 04	0.005 55	0.012 9	0.022 0	0.052 5
B1	-0.123	-0.085 3	-0.067 8	-0.056 6	0.037 5

模型Models	RS	EE	RMA
OLS	25.6	24.7	74.4
SLM	3.2	2.5	33.0
SEM	3.1	2.2	32.8
GWR	3.2	3.0	30.6

模型Models	< 50 t·hm^-2		50~100 t·hm^-2		>100 t·hm^-2		All
模型Models	ME	MRE	ME	MRE	ME	MRE	ME	MRE
OLS	-21.515	-0.394	22.734	0.314	65.653	1.467	14.002	0.247
SLM	-24.388	-0.402	7.247	0.124	40.787	0.578	2.116	0.025
SEM	-24.920	-0.412	7.098	0.117	42.840	0.617	2.070	0.022
GWR	-19.755	-0.335	5.275	0.105	39.862	0.556	2.172	0.031

模型 Models	总生物量 Total biomass/t	单位面积生物量 Biomass per unit area/(t·hm^-2)	与实测值偏差 Deviation from measured values(%)
OLS	8 054 406.752	68.521	27.272
SLM	8 136 806.883	69.222	28.575
SEM	7 847 717.995	66.763	24.007
GWR	7 816 332.771	66.496	23.511

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