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Original Articles

A study of the El Niño-Southern Oscillation influence on vegetation indices in Brazil using time series analysis from 1995 to 1999

, , , , , & show all
Pages 423-437 | Received 09 Apr 2007, Accepted 09 May 2008, Published online: 08 Jan 2010
 

Abstract

This study aims at improving the understanding of the behaviour of vegetation in Brazil due to the regional influences of climatic events, specifically the El Niño-Southern Oscillation (ENSO). To accomplish this we used a set of filtered data from the European Fourier-Adjusted and Interpolated Normalized Difference Vegetation Index (EFAI-NDVI), generated by the Advanced Very High Resolution Radiometer (AVHRR), along with the Vegetation Condition Index (VCI), with spatial resolution of 0.1° × 0.1° and temporal resolution of 10 days, covering the period from 1995 to 1999. Through analysis of these time series based on principal components transformation, we evaluated the influence and location of the ENSO effects in both datasets. The results show teleconnection patterns between climatic conditions in the Pacific Ocean and vegetation in specific locations in Brazil. Principal component 9 of the EFAI-NDVI presented significant correlations with the Southern Oscillation Index (SOI), R = −0.48, and with the Multivariate ENSO Index (MEI), R = 0.62, at p < 0.01. For the VCI, principal component 3 showed the greatest relations with the SOI, R = 0.45, and MEI, R = −0.51, at p < 0.01. The use of VCI has not improved the response of the ENSO's teleconnection in relation to NDVI. The eigenvector field of component EFAI-NDVI indicated a greater influence of the phenomenon, mainly in the north, north-east and parts of the southern regions of the country. These findings show that data on plant cover reflectance captured by polar orbit satellites can be used as indicators of interannual climatic variability.

Acknowledgements

We would like to thank the Brazilian Institute of Geography and Statistics (IBGE) for supplying data and technical support. We also thank Dr Reto Stöckli for making available the EFAI-NDVI dataset.

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