Research on vegetation cover information extraction technologies under different terrain conditions

doi:10.3724/SP.J.1258.2013.00002

Abstract

Abstract:

Aims We compared a variety of vegetation cover extraction methods by hyperspectral remote sensing that are currently popular. Our aims were to systematically study vegetation information extraction technology and provide a reference for further study of vegetation cover.
Methods The methods of vegetation index, dimidiate pixel model, principal component regression (PCR), partial least squares regression first order differential (PLSR) and linear spectral mixture decomposition model were used to extract vegetation information.
Important findings The vegetation cover estimation ability of dimidiate pixel models established by different normalized differential vegetation index (NDVI) was higher than that of the regression models established by NDVI directly. The optimization model for shady slopes was PLSR model based on the first order differential (FD) with modeling R² = 0.810, root mean square error (RMSE) = 6.29 and validation R² = 0.773, RMSE = 8.85. The optimization model for sunny slopes was PLSR model based on the second order differential (SD), with modeling R² = 0.823, RMSE = 6.04 and validation R² = 0.801, RMSE = 7.35. The optimization model for plains was full confine linear spectral mixture model (FCLS), with validation R² = 0.852, RMSE = 5.86.

Key words: arid and semi-arid, high spectrum, model, vegetation coverage

WU Jian, LIU Min-Shi, LI Wei-Tao. Research on vegetation cover information extraction technologies under different terrain conditions[J]. Chin J Plant Ecol, 2013, 37(1): 18-25.

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URL: https://www.plant-ecology.com/EN/10.3724/SP.J.1258.2013.00002

https://www.plant-ecology.com/EN/Y2013/V37/I1/18

Figures/Tables 13

References 13

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	NIR (nm)	Red (nm)	建模 Modeling		验证 Verification
	NIR (nm)	Red (nm)	R²	RMSE	R²	RMSE
NDVI₁	823.65	701.55	0.656	10.73	0.638	11.74
NDVI₂	803.3	671.02	0.508	14.65	0.497	14.40
NDVI₃	TM4	TM3	0.483	15.01	0.476	15.29

	NIR (nm)	Red (nm)	建模 Modeling		验证 Verification
	NIR (nm)	Red (nm)	R²	RMSE	R²	RMSE
NDVI₁	823.65	701.55	0.656	10.73	0.638	11.74
NDVI₂	803.3	671.02	0.508	14.65	0.497	14.40
NDVI₃	TM4	TM3	0.483	15.01	0.476	15.29

	NIR (nm)	Red (nm)	建模 Modeling		验证 Verification
	NIR (nm)	Red (nm)	R²	RMSE	R²	RMSE
NDVI₁	823.65	701.55	0.731	8.85	0.711	9.94
NDVI₂	803.3	671.02	0.712	9.87	0.682	10.73
NDVI₃	TM4	TM3	0.713	9.96	0.708	10.69

	NIR (nm)	Red (nm)	建模 Modeling		验证 Verification
	NIR (nm)	Red (nm)	R²	RMSE	R²	RMSE
NDVI₁	823.65	701.55	0.731	8.85	0.711	9.94
NDVI₂	803.3	671.02	0.712	9.87	0.682	10.73
NDVI₃	TM4	TM3	0.713	9.96	0.708	10.69

	NIR (nm)	Red (nm)	建模 Modeling		验证 Verification
	NIR (nm)	Red (nm)	R²	RMSE	R²	RMSE
NDVI₁	823.65	701.55	0.746	7.09	0.732	8.95
NDVI₂	803.3	671.02	0.723	7.84	0.706	10.47
NDVI₃	TM4	TM3	0.705	9.12	0.691	10.70