Analyzing Vegetation Phenology in the Context of Climate Variability in Burkina Faso: Preliminary Results

Vegetation cover in semi-arid Burkina Faso is characterized by high annual and interannual variability, which can be attributed to rainfall patterns and is also impacted by human activities such as extraction of woody vegetation, cultivation and grazing. The year-to-year timing, intensity, and duration of the growing season have important implications for the economy, which is dominated by the agricultural sector. The choice of the study site was inspired by results from an earlier study of vegetation dynamics and climate variability in the Sahel. It includes the Central Plateau of Burkina Faso, which stands out due to a satellite- observed greening trend and evidence of increased vegetation density on the ground, as well as its surroundings, which do not show such a trend. The aim of this study is to better characterize vegetation types and their seasonal variability that might have contributed to the observed trends in vegetation greenness. Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the years 2000 to 2006 were analyzed using TIMESAT, an algorithm that can extract phenological metrics from time series of remotely sensed vegetation index and other data types. The resulting metrics were used to (1) classify the study region into different vegetation types and to (2) assess short term trends and variation in different aspects of the phenological cycle. For the years 2003 - 2006, the observed spatial patterns of vegetation phenology were analyzed in conjunction with Climate Prediction Center Morphing Technique (CMORPH) satellite precipitation estimates, and seasonal metrics extracted from them. Preliminary results show that the inclusion of phenological metrics provides a sharper distinction of different vegetation types in land cover classifications. Differential responses of various phenological metrics to various precipitation metrics are expected. The findings could improve our understanding of rainfall-vegetation relationships in this semi-arid environment and help disentangle rainfall from other driving forces of vegetation dynamics.