Vegetation dynamics of Zimbabwe investigated using NOAA-AVHRR NDVI from 1982 to 2006: a principal component analysis

Abstract

The dominant modes of vegetation variability over Zimbabwe are investigated using principal component analysis (PCA) on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) normalized difference vegetation index (NDVI) monthly imagery from 1982 to 2006. Spectral analysis is also used to determine the periodicities of the component loadings. NDVI PCA-1 corresponds to the major vegetation types of Zimbabwe, and we demonstrated that grasslands and dry savannah have the strongest relationship with mean annual precipitation. Furthermore, the March–April loadings showed the highest correlation (r = 0.73) with mean annual precipitation. NDVI PCA-1 sheds some light on the land reform challenge in Zimbabwe. NDVI PCA-2 is highly correlated (r = 0.87) with the mean annual relative variability of the rainfall map indicating a southeast/north mode of anomalies associated with the convectional rainfall-bearing systems over Zimbabwe. NDVI PCA-2 is also highly correlated (r = 0.86) with precipitation PCA-2. NDVI PCA-3 shows a southeast/west mode and is highly correlated (r = 0.87) with precipitation PCA-3. A high correlation (r = 0.66) is also noted between NDVI PCA-4 and the elevation map. Spectral analysis of the PCA loadings revealed several periodicities corresponding to those found in tropical sea surface temperatures (SSTs).

Acknowledgements

The authors gratefully acknowledge the financial support from the Environmental Opinion Leaders (ENVOL) programme and the Ryoichi Sasakawa Young Leaders Fellowship (SYLFF) Fund. We would like to thank Ms Juliet Gwenzi, a meteorologist and lecturer at the University of Zimbabwe, who kindly helped in preparing the rainfall data for Zimbabwe. Mberego, a PhD student who participated in the 2012 United Nations University Building Resilience to Climate Change (UNU-BRCC) programme, would like to acknowledge the comments received from Professor Janette Lindesay. We are also grateful for the valuable comments received from two anonymous reviewers in the production of this article.