Abstract
The array of Normalized Difference Vegetation Index (NDVI) products now being derived from satellite imagery open up new opportunities for the study of short and long-term variability in climate. Using a time series analysis procedure based on the Principal Components transform, and a sequence of monthly Advanced Very High Resolution Radiometer (AVHRR)-derived NDVI imagery from 1986 through 1990, we examine trends in variability of vegetation greenness for Africa for evidence of climatic trends. In addition to the anticipated seasonal trends, we identify signals of interannual variability. The most readily identified is one that periodically affects Southern Africa. It is shown that the temporal loadings for this component exhibit a very strong relationship with the El Nino/Southern Oscillation (ENSO) Index derived from atmospheric pressure patterns in the Pacific, Pacific sea surface temperature (SST) anomalies, and with anomalous Outgoing Longwave Radiation (OLR). However, we have also detected a second interannual variation, affecting most particularly East Africa and the Sahel, that does not exhibit a consistent ENSO relationship. The results show the teleconnection patterns between climatic conditions in the Pacific Ocean basin and vegetation conditions at specific regional locations over Africa. The comprehensive spatial character and high temporal resolution of these data offer exciting prospects for deriving a land surface index of ENSO and mapping the impacts of ENSO activity at continental scale. This study illustrates that vegetation reflectance data derived from polar orbiting satellites can serve as good proxy for the study of interannual climate variability.
| Original language | English |
|---|---|
| Pages (from-to) | 2533-2548 |
| Number of pages | 16 |
| Journal | International Journal of Remote Sensing |
| Volume | 17 |
| Issue number | 13 |
| DOIs | |
| State | Published - Sep 1 1996 |
| Externally published | Yes |
Funding
Acknowledgments We wish to thank the USAID/FEWS Project for providing the NOVI time series data set used in this study and Dr Vern Kousky at the Climate Analysis Center for access to the SOl, SST, and OLR data. A. A. was supported by a NASA Graduate Student Fellowship Program in Global Change Research NGT # 30197 through the Clark Labs/George Perkins Marsh Institute at Clark University. The authors gratefully acknowledge anonymous reviewers for their comments and suggestions. This paper is part of Clark Labs research in Change and Time Series Analysis.
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