VEGETATION INDEXES-BIOMASS MODELS FOR TYPICAL SEMI-ARID STEPPE—A CASE STUDY FOR XILINHOT IN NORTHERN CHINA

doi:10.17521/cjpe.2007.0004

Abstract

Abstract:

Aims There is a crucial need in grassland study for a vegetation index (VI)-biomass model simulating steppe biomass based on remote sensing.

Methods Thematic mapper (TM) images (spatial resolution of 30 m× 30 m) for the research area in 2005 and 1991 were rectified so that geometric errors were less than one pixel, then extracted the image of the research region in the soft of ERDAS. We used five vegetation indexes:RVI (ratio vegetation index), NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MASVI (modified soil-adjusted vegetation index) and RSR (reduced simple ratio index). They were correlated to plant biomass sampled on the ground at the same time as the TM images. We developed four kinds of regression models: linear, logarithm, second-degree polynomial and cubic polynomial.

Important findings The correlations between sampled biomass and the five VIs were highly significant, with four (NDVI, SAVI, MSAVI, RSR) being positive and one (RVI) negative. Multiple correlation coefficients (R²) of the 15 regression models were >0.6, indicating that a VI-biomass regression model was a simple method to monitor the biomass of steppe grassland. The R² of the NDVI-biomass model was the highest, indicating that it was better suited to simulate the biomass of typical steppe than the other VIs. For TM image, all four kinds of models were significant at the 0.01 level, with the cubic polynomial model as the best to simulate the biomass, followed by the second-degree polynomial, linear and logarithm models. Therefore, the cubic polynomial regression model based on NDVI-biomass was the best model, and was used to simulate the biomass of the research region. Simulated biomass was higher in the east than in the west of the research region and higher in the southeast than in the northwest. Simulated biomass was consistent with sampled biomass in 2005.

Key words: vegetation index, typical steppe, biomass, regression model

LI Su-Ying, LI Xiao-Bing, YING Ge, FU Na. VEGETATION INDEXES-BIOMASS MODELS FOR TYPICAL SEMI-ARID STEPPE—A CASE STUDY FOR XILINHOT IN NORTHERN CHINA[J]. Chin J Plant Ecol, 2007, 31(1): 23-31.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2007.0004

https://www.plant-ecology.com/EN/Y2007/V31/I1/23

Figures/Tables 8

Fig.1 The map of research plot in typical steppe of Inner Mongolia

Table 1 The vegetation index

中文名称 Chinese name	英文缩写 English abbreviation	计算公式 Formula	作者(年代) Author(year)
比值植被指数 Ratio vegetation index	RVI	R/NIR	Pearson et al., 1972
归一化差异植被指数 Normalized difference vegetation index	NDVI	(NIR-R)/(NIR+R)	Rouse et al., 1974
土壤调节植被指数 Soil-adjusted vegetation index	SAVI	$(NIR - R) (NIR + R + L)$ (1+L)	Huete, 1988
修改型土壤调整植被指数 Modified soil-adjusted vegetation index	MSAVI	$2 NIR + 1 - (2 NIR + 1) 2 - 8 (NIR - R)) 2$	Qi et al., 1994
简化比率指数 Reduced simple ratio	RSR	$NIR R$ (1- $MIR - MI R min MI R max - MI R min)$	Brown et al., 2000

Table 1 The vegetation index

中文名称 Chinese name	英文缩写 English abbreviation	计算公式 Formula	作者(年代) Author(year)
比值植被指数 Ratio vegetation index	RVI	R/NIR	Pearson et al., 1972
归一化差异植被指数 Normalized difference vegetation index	NDVI	(NIR-R)/(NIR+R)	Rouse et al., 1974
土壤调节植被指数 Soil-adjusted vegetation index	SAVI	$(NIR - R) (NIR + R + L)$ (1+L)	Huete, 1988
修改型土壤调整植被指数 Modified soil-adjusted vegetation index	MSAVI	$2 NIR + 1 - (2 NIR + 1) 2 - 8 (NIR - R)) 2$	Qi et al., 1994
简化比率指数 Reduced simple ratio	RSR	$NIR R$ (1- $MIR - MI R min MI R max - MI R min)$	Brown et al., 2000

Fig.2 The scatter map between the vegetation biomass and the vegetation index of TM

Table 2 Correlations between biomass and vegetation index

	生物量 Biomass	比值植被指数 RVI	归一化差异植被指数 NDVI	土壤调节植被指数 SAVI	修改型土壤调整植被指数 MSAVI	简化比率植被指数 RSR
生物量Biomass	1	-0.786^**	0.797^**	0.788^**	0.787^**	0.782^**
比值植被指数RVI		1	-0.997^**	-0.994^**	-1.000^**	-0.938^**
归一化差异植被指数NDVI			1	0.996^**	0.998^**	0.951^**
土壤调节植被指数SAVI				1	0.994^**	0.945^**
修改型土壤调整植被指数MSAVI					1	0.939^**
简化比率植被指数RSR						1

Table 3 Regression model from the vegetation biomass and the vegetation index (VI) of TM image

年 Year	植被指数 VI	模型类型 Model types	模型 Model	复相关系数 Multiple correlation coefficient
2005 (n=22)	RVI	线性 Linear model	y=-7.565x+1 829.100	R²=0.617
		对数 Logarithm model	y=-1 565.600ln(x)+8 610.100	R²=0.624
		二次多项式Second-degree polynomial model	y=0.090x²-44.561x+5 639.400	R²=0.636
		三次多项式Cubic polynomial model	y=0.007x³-4.120x²+820.270x-53 430.000	R²=0.649
	NDVI	线性 Linear model	y=10.022x-1 308.000	R²=0.636
		对数 Logarithm model	y=1 575.100ln(x)-7 696.800	R²=0.631
		二次多项式Second-degree polynomial model	y=0.081x²-15.622x+716.520	R²=0.641
		三次多项式Cubic polynomial model	y=-0.016x³+7.450x²-1 182.000x+62 109.000	R²=0.654
	SAVI	线性 Linear model	y=6.708x-885.270	R²=0.621
		对数 Logarithm model	y=1 147.500ln(x)-5 635.900	R²=0.612
		二次多项式Second-degree polynomial model	y=0.043x²-8.245x+404.440	R²=0.629
		三次多项式Cubic polynomial model	y=-0.005x³ +2.681x²-464.400x+26 579.000	R²=0.644
	MSAVI	线性 Linear model	y=7.584x-1 055.2	R²=0.619
		对数 Logarithm model	y=1 305.800ln(x)-6 470.100	R²=0.609
		二次多项式Second-degree polynomial model	y =0.089x²-23.343x+1 632.400	R²=0.637
		三次多项式Cubic polynomial model	y=-0.007x³+3.691x²-651.320x+37 995.000	R²=0.650
	RSR	线性 Linear model	y=3.039x-733.520	R²=0.611
		对数 Logarithm model	y=1 027.500ln(x)-5 685.700	R²=0.615
		二次多项式Second-degree polynomial model	y=-0.010x²+9.835x-1 876.900	R²=0.617
		三次多项式Cubic polynomial model	y=0.001x³-0.512x²+179.380x-20 874.000	R²=0.623
1991 (n=18)	RVI	线性 Linear model	y=-5.593x+631.780	R²=0.763
		对数 Logarithm model	y=-447.920ln(x)+2 143.500	R²=0.757
		二次多项式Second-degree polynomial model	y=-0.010x²-4.033 1x+568.820	R²=0.764
		三次多项式Cubic polynomial model	y=-0.009x³+2.156x²-181.050x+5 315.200	R²=0.814
	NDVI	线性 Linear model	y=4.033x-515.400	R²=0.771
		对数 Logarithm model	y=687.180ln(x)-3 356.500	R²=0.773
		二次多项式Second-degree polynomial model	y=-0.007x²+6.362x-714.010	R²=0.772
		三次多项式Cubic polynomial model	y=0.003x³-1.748x²+301.240x-17 238.000	R²=0.832
	SAVI	线性 Linear model	y=4.115x-520.190	R²=0.724
		对数 Logarithm model	y=684.560ln(x)-3 334.000	R²=0.722
		二次多项式Second-degree polynomial model	y=0.002x²+3.582x-475.530	R²=0.724
		三次多项式Cubic polynomial model	y=0.003x³-1.607x²+269.170x-14 959.000	R²=0.768
	MSAVI	线性 Linear model	y=5.563x-745.670	R²=0.721
		对数 Logarithm model	y=919.220ln(x)-4 519.500	R²=0.724
		二次多项式Second-degree polynomial model	y=-0.023x²+13.199x-1 375.500	R²=0.723
		三次多项式Cubic polynomial model	y=0.012x³-5.791x²+960.330x-53 029.000	R²=0.799
	RSR	线性 Linear model	y=2.508x-190.730	R²=0.687
		对数 Logarithm model	y=376.490ln(x)-1 697.000	R²=0.699
		二次多项式Second-degree polynomial model	y=-0.009x²+5.271x-397.490	R²=0.695
		三次多项式Cubic polynomial model	y=0.001x³-0.550x²+83.298x-4 067.300	R²=0.757

Table 3 Regression model from the vegetation biomass and the vegetation index (VI) of TM image

年 Year	植被指数 VI	模型类型 Model types	模型 Model	复相关系数 Multiple correlation coefficient
2005 (n=22)	RVI	线性 Linear model	y=-7.565x+1 829.100	R²=0.617
		对数 Logarithm model	y=-1 565.600ln(x)+8 610.100	R²=0.624
		二次多项式Second-degree polynomial model	y=0.090x²-44.561x+5 639.400	R²=0.636
		三次多项式Cubic polynomial model	y=0.007x³-4.120x²+820.270x-53 430.000	R²=0.649
	NDVI	线性 Linear model	y=10.022x-1 308.000	R²=0.636
		对数 Logarithm model	y=1 575.100ln(x)-7 696.800	R²=0.631
		二次多项式Second-degree polynomial model	y=0.081x²-15.622x+716.520	R²=0.641
		三次多项式Cubic polynomial model	y=-0.016x³+7.450x²-1 182.000x+62 109.000	R²=0.654
	SAVI	线性 Linear model	y=6.708x-885.270	R²=0.621
		对数 Logarithm model	y=1 147.500ln(x)-5 635.900	R²=0.612
		二次多项式Second-degree polynomial model	y=0.043x²-8.245x+404.440	R²=0.629
		三次多项式Cubic polynomial model	y=-0.005x³ +2.681x²-464.400x+26 579.000	R²=0.644
	MSAVI	线性 Linear model	y=7.584x-1 055.2	R²=0.619
		对数 Logarithm model	y=1 305.800ln(x)-6 470.100	R²=0.609
		二次多项式Second-degree polynomial model	y =0.089x²-23.343x+1 632.400	R²=0.637
		三次多项式Cubic polynomial model	y=-0.007x³+3.691x²-651.320x+37 995.000	R²=0.650
	RSR	线性 Linear model	y=3.039x-733.520	R²=0.611
		对数 Logarithm model	y=1 027.500ln(x)-5 685.700	R²=0.615
		二次多项式Second-degree polynomial model	y=-0.010x²+9.835x-1 876.900	R²=0.617
		三次多项式Cubic polynomial model	y=0.001x³-0.512x²+179.380x-20 874.000	R²=0.623
1991 (n=18)	RVI	线性 Linear model	y=-5.593x+631.780	R²=0.763
		对数 Logarithm model	y=-447.920ln(x)+2 143.500	R²=0.757
		二次多项式Second-degree polynomial model	y=-0.010x²-4.033 1x+568.820	R²=0.764
		三次多项式Cubic polynomial model	y=-0.009x³+2.156x²-181.050x+5 315.200	R²=0.814
	NDVI	线性 Linear model	y=4.033x-515.400	R²=0.771
		对数 Logarithm model	y=687.180ln(x)-3 356.500	R²=0.773
		二次多项式Second-degree polynomial model	y=-0.007x²+6.362x-714.010	R²=0.772
		三次多项式Cubic polynomial model	y=0.003x³-1.748x²+301.240x-17 238.000	R²=0.832
	SAVI	线性 Linear model	y=4.115x-520.190	R²=0.724
		对数 Logarithm model	y=684.560ln(x)-3 334.000	R²=0.722
		二次多项式Second-degree polynomial model	y=0.002x²+3.582x-475.530	R²=0.724
		三次多项式Cubic polynomial model	y=0.003x³-1.607x²+269.170x-14 959.000	R²=0.768
	MSAVI	线性 Linear model	y=5.563x-745.670	R²=0.721
		对数 Logarithm model	y=919.220ln(x)-4 519.500	R²=0.724
		二次多项式Second-degree polynomial model	y=-0.023x²+13.199x-1 375.500	R²=0.723
		三次多项式Cubic polynomial model	y=0.012x³-5.791x²+960.330x-53 029.000	R²=0.799
	RSR	线性 Linear model	y=2.508x-190.730	R²=0.687
		对数 Logarithm model	y=376.490ln(x)-1 697.000	R²=0.699
		二次多项式Second-degree polynomial model	y=-0.009x²+5.271x-397.490	R²=0.695
		三次多项式Cubic polynomial model	y=0.001x³-0.550x²+83.298x-4 067.300	R²=0.757

Fig.3 Contrast of correlation parameters of regression model on different vegetation index (VI)

Table 4 Error contrast of the observation value and the simulation value based on the experiential model

	实测值 Observation value	模拟值^* Simulation value	差值(实测值-模拟值) Subtraction (observation value-simulation value)	百分比(%)(差值/实测值) Percentage (subtraction/observation value)
1	231.73	198.10	33.63	15.14
2	154.43	156.30	-1.87	-1.21
3	178.80	177.30	1.50	0.84
4	179.60	214.20	-34.59	-19.26
5	163.65	255.00	-91.35	-55.82
6	260.00	255.00	5.00	1.92
7	283.00	244.30	38.80	13.71
8	144.27	153.40	-9.13	-6.33
9	189.07	164.10	24.97	13.21
10	191.23	174.30	16.93	8.85
11	201.47	226.20	-24.73	-12.28
12	164.67	174.60	-9.93	-6.03
13	269.90	250.10	19.80	7.34
14	61.33	54.60	6.73	10.98

Fig.4 The map of biomass based on NDVI model in Xilinhot

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