A new vegetation index based on the universal pattern decomposition method

Abstract

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.

Present address: Institute of Remote Sensing and Geographical Information System, Peking University, Beijing, 100871, China.

Present address: Laboratory of Nature Information Science, Department of Information and Computer Sciences, Nara Industrial University, Nara, Japan.

Keywords:

• Vegetation index
• Hyperspectral data
• Sensor‐independent
• Sensitivity analysis

Acknowledgements

This work was supported under the ADEOS‐II/GLI Project of the Japan Aerospace Exploration Agency (JAXA), the Academic Frontier Promotion Project of the Ministry of Education, Science, Sports, and Culture of Japan, and the 973 Project of the People's Republic of China (Project No. 2003CB415205). This paper was partially funded by China Postdoctoral Science Foundation (Project No. 2005038002).

Notes

Present address: Institute of Remote Sensing and Geographical Information System, Peking University, Beijing, 100871, China.

Present address: Laboratory of Nature Information Science, Department of Information and Computer Sciences, Nara Industrial University, Nara, Japan.