ANALYSING AND QUANTIFYING VEGETATION RESPONSES TO RAINFALL WITH HIGH RESOLUTION SPATIO-TEMPORAL TIME SERIES DATA FOR DIFFERENT ECOSYSTEMS AND ECOTONES IN QUEENSLAND
Keywords: Landsat, MODIS, NDVI, distributed lag models, time series
Abstract. Vegetation responses and ecosystem function are spatially variable and influenced by climate variability. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to combine MODIS (Moderate Resolution Imaging Spectrometer) and Landsat TM/ETM+ (Thematic Mapper/ Enhanced Thematic Mapper plus) imagery for an 8 year dataset (2000–2007) at 30m spatial resolution with 8 day intervals. This dataset allows for a functional analysis of ecosystem responses, suitable for heterogeneous landscapes. Derived vegetation index information in form of the NDVI (Normalised Difference Vegetation Index) was used to investigate the relationship between vegetation responses and gridded rainfall data for regional ecosystems. A hierarchical decomposition of the time series has been carried out in which relationships among the time-series were individually assessed for deterministic time-series components (trend component and seasonality) as well as for the stochastic seasonal anomalies. While no common long-term trends in NDVI and rainfall data in the time period considered exist, there is however, a strong concurrence in the seasonally of NDVI and rainfall data. This component accounts for the majority of variability in the time-series. On the level of seasonal anomalies, these relationships are more subtle. The statistical analysis required, among others, the removal of temporal autocorrelation for an unbiased assessment of significance. Significant lagged correlations between rainfall and NDVI were found in complex Queensland savannah vegetation communities. For grasslands and open woodlands, significant relationships with lag times between 8 and 16 days were found. For denser, evergreen vegetation communities greater lag times of up to 2.5 months were found. The derived distributed lag models may be used for short-term NDVI and biomass predictions on the spatial resolution scale of Landsat (30m).