Narrowband vegetation index performance using the AVIRIS hyperspectral remotely sensed data

Abstract

The objective of this paper is the description of the development and the validation, using airborne hyper-spectral imagery data, of a non-conventional technique for the vegetation information extraction. The proposed approach namely the universal pattern decomposition method (UPDM) is tailored for hyper-spectral imagery analysis, which can be explained using two analysis methods: spectral mixing analysis and multivariate analysis. For the former, the UPDM expresses the spectrum of each pixel as the linear sum of three fixed, standard spectral patterns (i.e., the patterns of water, vegetation, and soil); each coefficient represents the ratio of spectral patterns of three components. If we think of the UPDM as multivariate analysis, standard patterns are interpreted as an oblique coordinate system, and coefficients are thought of as the coordinates of a pixel's reflectance. The later explanation is much more comprehensible than the former for the reason of additional supplementary pattern presence when necessary. The vegetation index based on the UPDM (VIUPD) is expressed as a linear sum of the pattern decomposition coefficients. Here, the VIUPD was used to examine vegetation amounts and degree of terrestrial vegetation vigor; VIUPD results were compared with results by the normalized difference vegetation index (NDVI), and an enhanced vegetation index (EVI). This paper described the calculation of VIUPD, using AVIRIS airborne remotely sensed data. The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than the NDVI and EVI.

Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

“…To reduce the impact of sensor dependence on analysis results, Zhang et al (2006a) developed the universal pattern decomposition method (UPDM) and further proposed a new vegetation index (VI) based on this method (VIUPD) (Zhang et al 2007b). VIUPD was proved to be more sensitive in reflecting vegetation activity than the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), using ground data (Zhang et al 2007b) and hyperspectral data (Airborne Visible Infrared Imaging Spectrometer (AVIRIS)) (Zhang, Yan, and Yang 2006b). In addition, UPDM and VIUPD have been applied to multispectral data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) to evaluate their dependence on the sensor type (Zhang et al 2007a(Zhang et al , 2010, with results indicating that VIUPD is more sensor independent than NDVI and EVI.…”

On-site spectral calibration of hyperspectral imager using target spectral features

“…With advantages of acquiring both spectral and spatial information, there are now growing interests in the research and applications in different fields, including geology, agriculture, forestry, coastal, environment hazards assessment and urban studies [1,2]. With the development of earth observation from qualitative to quantitative, we can only make the best of hyperspectral image data by quantification.…”