Skip to main content

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

Buy Article:

$55.00 plus tax (Refund Policy)

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 Nino-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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: 1: Brazilian Institute of Geography and Statistics (IBGE), Rio de Janeiro, RJ, Brazil 2: Meteorology Department, Geosciences Institute (IGEO), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil 3: Technology Institute (IT), Federal Rural University of Rio de Janeiro (UFRRJ), Seropedica, RJ, Brazil 4: EMBRAPA, Corn and Sorghum Research Unit, Sete Lagoas, MG, Brazil

Publication date: 01 March 2010

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more