2014•Unpublished venueRequires access

Calculating vegetation index based on the universal pattern decomposition method (VIUPD) using Landsat 8

Xiaojun She, Lifu Zhang, Muhammad Hasan Ali Baig, Yao Li

Open publisher page 3 citations

Abstract

This study introduced the vegetation index based on the universal pattern decomposition method (VIUPD) and then applied on a new sensor - Landsat 8 Operational Land Imager (OLI). VIUPD is a valuable sensor-independent spectral analysis method. Each pixel is described as the linear mixture of standard spectral patterns for water, vegetation, soil and supplementary patterns included when necessary. In the present paper, processing procedure about the data acquisition, radiometric calibration and atmospheric correction have been elaborated. The normalized reflectance (P) of four standard samples resampled to OLI has been listed. For validation of the results, Normalized Difference Vegetation Index (NDVI) and VIUPD have been calculated for comparison. The results showed that VIUPD is more sensitive to the vegetation amount change even in the high vegetation coverage, while the NDVI is more rapidly saturated in high vegetation cover area. In addition, VIUPD is more sensitive to the soil background than NDVI.

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What this paper is about

This study introduced the vegetation index based on the universal pattern decomposition method (VIUPD) and then applied on a new sensor - Landsat 8 Operational Land Imager (OLI). VIUPD is a valuable sensor-independent spectral analysis method. Each pixel is described as the linear mixture of standard spectral patterns for water, vegetation, soil and supplementary patterns included when necessary. In the present paper, processing procedure about the data acquisition, radiometric calibration and atmospheric correction have been elaborated. The normalized reflectance (P) of four standard samples resampled to OLI has been listed. For validation of the results, Normalized Difference Vegetation Index (NDVI) and VIUPD have been calculated for comparison. The results showed that VIUPD is more sensitive to the vegetation amount change even in the high vegetation coverage, while the NDVI is more rapidly saturated in high vegetation cover area. In addition, VIUPD is more sensitive to the soil background than NDVI.

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Available abstract

This study introduced the vegetation index based on the universal pattern decomposition method (VIUPD) and then applied on a new sensor - Landsat 8 Operational Land Imager (OLI). VIUPD is a valuable sensor-independent spectral analysis method. Each pixel is described as the linear mixture of standard spectral patterns for water, vegetation, soil and supplementary patterns included when necessary. In the present paper, processing procedure about the data acquisition, radiometric calibration and atmospheric correction have been elaborated. The normalized reflectance (P) of four standard samples resampled to OLI has been listed. For validation of the results, Normalized Difference Vegetation Index (NDVI) and VIUPD have been calculated for comparison. The results showed that VIUPD is more sensitive to the vegetation amount change even in the high vegetation coverage, while the NDVI is more rapidly saturated in high vegetation cover area. In addition, VIUPD is more sensitive to the soil background than NDVI.

Key concepts: Normalized Difference Vegetation Index, Vegetation (pathology), Enhanced vegetation index, Vegetation Index, Remote sensing, Vegetation cover, Environmental science, Calibration

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