Author:

Keywords:

vegetation dynamics, climate variability

Abstract:

To evaluate human impacts on forests and other carbon-storing ecosystems, temporal patterns of vegetation cover status must be explained in terms of their causal factors. More specifically, the impacts of climate variability on vegetation dynamics must be identified, quantified, and separated from the impacts of human disturbances. A generic approach is to combine remote sensing-derived information about the vegetation cover status with meteorological data for a corresponding period and study area. A common method consists of correlation analyses between time series of Normalized Difference Vegetation Index (NDVI) and precipitation depths. We propose a number of methodological improvements related to the choice of variables, and the handling of periodic fluctuations at different time scales. Previous studies have utilized standardized NDVI anomalies and anomalies of precipitation accumulated over variable time spans. However, we found that off-site and lag effects are better accounted for by using anomalies of soil moisture content as an indicator of climate variability. Since the soil pore space serves as a buffering reservoir for precipitation, we observed a more instantaneous and site specific correlation of soil moisture content with vegetation growth over a study area spanning East and Central Africa. Furthermore, the fraction of absorbed photosynthetically active radiation (fAPAR) is, as opposed to NDVI, a biophysical variable and is considered here to replace NDVI for further temporal analysis. Moreover, its sensor-independent characteristics allow the compilation of a 30-year time series. The methodology to quantify the response of vegetation to variable climate conditions must be capable of detecting the time scales at which fluctuations occur. Time series of soil moisture content and vegetation indices display a clear annual periodicity, arising from the annual cycles of precipitation and the growing seasons of dominant vegation types. As the objective is to understand climatic forcing beyond this seasonal response, all possible time scales must be accounted for in the analysis of the temporal signals of the explanatory variables. Therefore, time adaptive expansions of regular harmonic analysis are considered, and applied on univariate time series. Ultimately, bivariate analysis on a meteorological variable and a vegetation index will indicate which climatic fluctuations affect vegetation growth most. The preliminary results will be presented for the case of fAPAR (derived from NOAA-AVHRR and SPOT-VEGETATION imagery) and GLDAS soil moisture content, over East and Central Africa for a period of >30 years (1981 to present) at a 10 km spatial resolution.