Estimation accuracy of fractional vegetation cover based on normalized difference vegetation index and UAV hyperspectral images

doi:10.6046/zrzyyg.2020406

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Abstract

The researches on the effects of band parameters on the biophysical parameters of vegetation estimation using the normalized difference vegetation index (NDVI) have great significance for the improvement in the application accuracy of NDVI in vegetation dynamic monitoring. Based on the hyperspectral images of artificial grassland obtained from a Resonon, Inc. Pika XC2 Hyperspectral Imaging Camera loaded by an unmanned aerial vehicle (UAV), this study analyzes the effects of the positions and width of red and near-infrared bands on NDVI and assesses the sensitivity of NDVI to fractional vegetation cover and the estimation accuracy. The results are as follows. When band positions were fixed, the width expansion of red and near-infrared bands had little effects on NDVI and its sensitivity, and the accuracy of fractional vegetation cover estimated using narrowband NDVI is higher than the accuracy based on broadband NDVI. When the red and near-infrared bands moved towards long waves, the NDVI and its sensitivity were affected to different extents. With an increase in the sensitivity, the anti-disturbance performance of NDVI decreased, and the estimation accuracy of fractional vegetation cover decreased. The sensitivity coefficient of narrowband NDVI and the R² determined by the linear fitting of the sensitivity coefficient and the fractional vegetation cover greatly fluctuated, and the estimated fractional vegetation cover at various locations was unstable. High estimation accuracy of fractional vegetation was obtained at different locations using the 10 nm NDVI, with the maximum R² value of 0.83. The broadband NDVI calculated using four popular satellite images can be well applied in the inversion of the fractional vegetation cover in areas with high vegetation cover. However, its inversion accuracy of fractional vegetation cover still suffered some attenuation compared with narrowband NDVI (10 nm). These results will serve as scientific references and bases for accurate inversion of vegetation parameters using NDVI.

Keywords band position and width NDVI fractional vegetation cover UAV hyperspectral image

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Issue Date: 24 September 2021

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	Yongmei LIU
	Hongjian FAN
	Xinghua GE
	Jianhong LIU
	Lei WANG

Cite this article:

Yongmei LIU,Hongjian FAN,Xinghua GE, et al. Estimation accuracy of fractional vegetation cover based on normalized difference vegetation index and UAV hyperspectral images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 11-17.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020406 OR https://www.gtzyyg.com/EN/Y2021/V33/I3/11

Fig.1 Overview of the test area

Fig.2 Spectral reflectance curve of grassland in the test area

Fig.3 The influence of band position and width variation on NDVI

Fig.4 Sensitivity coefficient of NDVI about vegetation coverage at different band positions and widths

Fig.5 R² between NDVI and vegetation coverage at different band positions and bandwidths

Fig.6 Broadband NDVI values compared to Resonon 10 nm NDVI_670/770 values

Tab.1 R² between broadband NDVI and vegetation coverage

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