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Vegetation responses and ecosystem function are spatially variable and influenced by climate variability. The Spatial and TemporalAdaptive Reflectance Fusion Model (STARFM) was used to combine MODIS (Moderate Resolution Imaging Spectrometer) andLandsat TM/ETM+ (Thematic Mapper/ Enhanced Thematic Mapper plus) imagery for an 8 year dataset (2000–2007) at 30m spatialresolution with 8 day intervals. This dataset allows for a functional analysis of ecosystem responses, suitable for heterogeneouslandscapes. Derived vegetation index information in form of the NDVI (Normalised Difference Vegetation Index) was used toinvestigate the relationship between vegetation responses and gridded rainfall data for regional ecosystems. A hierarchicaldecomposition of the time series has been carried out in which relationships among the time-series were individually assessed fordeterministic time-series components (trend component and seasonality) as well as for the stochastic seasonal anomalies. While nocommon long-term trends in NDVI and rainfall data in the time period considered exist, there is however, a strong concurrence inthe seasonally of NDVI and rainfall data. This component accounts for the majority of variability in the time-series. On the level ofseasonal anomalies, these relationships are more subtle. The statistical analysis required, among others, the removal of temporalautocorrelation for an unbiased assessment of significance. Significant lagged correlations between rainfall and NDVI were found incomplex Queensland savannah vegetation communities. For grasslands and open woodlands, significant relationships with lag timesbetween 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 ofLandsat (30m)

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

ANALYSING AND QUANTIFYING VEGETATION RESPONSES TO RAINFALL WITH HIGH RESOLUTION SPATIO-TEMPORAL TIME SERIES DATA FOR DIFFERENT ECOSYSTEMS AND ECOTONES IN QUEENSLAND