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This study examined a new vegetation index, based on the universal pattern decomposition method (VIUPD). The universal pattern decomposition method (UPDM) allows for sensor-independent spectral analysis. Each pixel is expressed as the linear sum of standard spectral patterns for water, vegetation and soil, with supplementary patterns included when necessary. Pattern decomposition coefficients for each pixel contain almost all the sensor-derived information, while having the benefit of sensor independence. The VIUPD is expressed as a linear sum of the pattern decomposition coefficients; thus, the VIUPD is a sensor-independent index. 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), an enhanced vegetation index (EVI) and a conventional vegetation index based on pattern decomposition (VIPD). The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than the NDVI and EVI. [ABSTRACT FROM AUTHOR]

The universal pattern decomposition method (UPDM) is a sensor-independent method in which each satellite pixel is expressed as the linear sum of fixed, standard spectral patterns for water, vegetation and soil. The same normalized spectral patterns can be used for different solar-reflected spectral satellite sensors. Supplementary patterns are included when necessary. The UPDM has been applied successfully to simulated data for Landsat/ETM+, Terra/MODIS, ADEOS-II/GLI and 92-band CONTINUE sensors using ground-measured data. This study validates the UPDM using MODIS and ETM+ data acquired over the Three Gorges region of China. The reduced χ2 values for selected area D, that with the smallest terrain influences, are 0.000409 (MODIS) and 0.000181 (ETM+), and the average linear regression factor between MODIS and ETM+ is 1.0077, with root mean square (rms) value 0.0082. The linear regression factor for the vegetation index based on the UPDM (VIUPD) between MODIS and ETM+ data for area D is 1.0089 with rms 0.0696. Both UPDM coefficients and VIUPD are sensor independent for the above sensors. [ABSTRACT FROM AUTHOR]

This paper describes a modified pattern decomposition method with a supplementary pattern. The proposed approach can be regarded either as a type of spectral mixing analysis or as a kind of multivariate analysis; the later explanation is more suitable considering the presence of the additional supplementary patterns. The sensor‐independent method developed herein uses the same normalized spectral patterns for any sensor: fixed multi‐band (1260 bands) spectra serve as the universal standard spectral patterns. The resulting pattern decomposition coefficients showed sensor independence. That is, regardless of sensor, the three coefficients had nearly the same values for the same samples. The estimation errors for pattern decomposition coefficients depended on the sensor used. The estimation errors for Landsat/MSS and ALOS/AVNIR‐2 were larger than those of Landsat/TM (ETM+), Terra/MODIS and ADEOS‐II/GLI. The latter three sensors had negligibly small errors. [ABSTRACT FROM AUTHOR]

The 'pattern decomposition method' (PDM) is a new analysis method originally developed for Landsat Thematic Mapper (TM) satellite data. Applying the PDM to the radiospectrometer data of ground objects, 121 dimensional data in the wavelength region 350-2500 nm were successfully reduced into three-dimensional data. The nearly continuous spectral reflectance of land cover objects could be decomposed by three standard spectral patterns with an accuracy of 4.17% per freedom. We introduced a concept of supplementary spectral patterns for the study of specific ground objects. As an example, availability of a supplementary spectral pattern that can rectify standard spectral pattern of vivid vegetation for spectra of withered vegetation was studied. The new Revised Vegetation Index based on Pattern Decomposition (RVIPD) for hyper-multi-spectra is proposed as a simple function of the pattern decomposition coefficients including the supplementary vegetation pattern. It was confirmed that RVIPD is linear to the area cover ratio and also to the vegetation quantum efficiency. [ABSTRACT FROM AUTHOR]

We have developed a 'pattern decomposition method' based on linear spectral mixing of ground objects for n-dimensional satellite data. In this method, spectral response patterns for each pixel of an image are decomposed into three components using three standard spectral shape patterns determined from the image data. Applying this method to Landsat Thematic Mapper data, six-dimensional data are successfully transformed into three-dimensional data. Nearly 94% of the information in the six-dimensional data is retained in the three components. This method is very useful for classifying and monitoring changes in land cover. [ABSTRACT FROM AUTHOR]