A new vegetation index based on the universal pattern decomposition method

Zhang, Lifu; Furumi, Shinobu; Muramatsu, Keiichi; Fujiwara, N.; Daigo, M.; Zhang, Liangpei

doi:10.1080/01431160600857402

Cited by 44 publications

(41 citation statements)

References 25 publications

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“…Due to the scaling problem, NDVI is easily saturated in areas with high vegetation cover [7]. It was assumed that VIUPD was equal to 1 when the target was covered by green vegetation which explains the high values [38]. The distributions of VIUPD values among the three non-forest types were different from the three forest types.…”

Section: Discussionmentioning

confidence: 99%

“…44 ( ) where R(i) is the reflectance of band i for each pixel and CW, CV, and CS are the decomposition coefficients (or abundance of elements). Pk(i) (k = w, v, s, 4) indicates for the universal spectra of patterns [38]. The transformation coefficients for VIUPD, Pk(i), and TCG were slightly different.…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…In this study, we used three band-geometric-computing VIs (NDVI, SAVI, and EVI) and two band-transformation VIs (VIUPD and TCG), as shown in Table 2 [9,[36][37][38][39].…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…Specifically, the objectives of this study were as follows: (1) to evaluate the variation of different VI generation methods on the consistency between Landsat OLI and ETM+ data; five VIs were selected, including three geometry-computing VIs (Normalized Difference Vegetation Index (NDVI) [9], Soil-Adjusted Vegetation Index (SAVI) [36], Enhanced Vegetation Index (EVI) [37]) and two transformation-operation vegetation indices (VIUPD [38] and Tasseled Cap Transformation Greenness (TCG) [39]); (2) to analyze the influence of subtle differences in spectral responses between ETM+ and OLI on the VI results of two sensors; ground experiment data covering four vegetation classes were simulated using at-sensor reflectance according to the OLI and ETM+ Spectral Response Function (SRF), respectively, and were used to verify the continuity between OLI and ETM+; (3) to explore the response of VIs among vegetation types; and (4) to analyze the continuity of VIs using multi-temporal data during the four seasons.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

She

Zhang

et al. 2015

Remote Sensing

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Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than OPEN ACCESSRemote Sens. 2015, 7 13486 band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.

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Section: Discussionmentioning

confidence: 99%

Section: Vegetation Indicesmentioning

confidence: 99%

“…In this study, we used three band-geometric-computing VIs (NDVI, SAVI, and EVI) and two band-transformation VIs (VIUPD and TCG), as shown in Table 2 [9,[36][37][38][39].…”

Section: Vegetation Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

She

Zhang

et al. 2015

Remote Sensing

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“…For vegetation index, most of the studies focused on the VCI derived from NDVI, whereas few studies have considered drought monitoring using other easily calculated vegetation indices. The vegetation index based on the universal pattern decomposition method (VIUPD), which was established by Zhang et al [22], has several advantages over other vegetation indices. Like NDVI, VIUPD is also based on satellite-based data and can provide near real-time data over the globe at a relatively high spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

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Monitoring and Performance Analysis of Workflow Applications in Large Scale Distributed Systems

Sbîrlea

Simion

Pop

et al. 2010

Intelligent Networking, Collaborative Systems and Applications

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A new vegetation index based on the universal pattern decomposition method

Cited by 44 publications

References 25 publications

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Monitoring and Performance Analysis of Workflow Applications in Large Scale Distributed Systems

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