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Assessment of Relationships Between Precipitation and Satellite Derived Vegetation Condition Within South Australia

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Abstract:

The normalised difference vegetation index (NDVI) has evolved as a primary tool for monitoring continental-scale vegetation changes and interpreting the impact of short to long-term climatic events on the biosphere. The objective of this research was to assess the nature of relationships between precipitation and vegetation condition, as measured by the satellite-derived NDVI within South Australia. The correlation, timing and magnitude of the NDVI response to precipitation were examined for different vegetation formations within the State (forest, scrubland, shrubland, woodland and grassland). Results from this study indicate that there are strong relationships between precipitation and NDVI both spatially and temporally within South Australia. Differences in the timing of the NDVI response to precipitation were evident among the five vegetation formations. The most significant relationship between rainfall and NDVI was within the forest formation. Negative correlations between NDVI and precipitation events indicated that vegetation green-up is a result of seasonal patterns in precipitation. Spatial patterns in the average NDVI over the study period closely resembled the boundaries of the five classified vegetation formations within South Australia. Spatial variability within the NDVI data set over the study period differed greatly between and within the vegetation formations examined depending on the location within the state.

ACRONYMS

AVHRR Advanced Very High Resolution Radiometer

ENVSAEnvironments of South Australia

EOS Terra-Earth Observing System

EVIEnhanced Vegetation Index

MODIS Moderate Resolution Imaging Spectro-radiometer

MVC Maximum Value Composite

NDVINormalised Difference Vegetation Index

NIRNear Infra-Red

NOAANational Oceanic and Atmospheric Administration

SPOT Systeme Pour l’Observation de la Terre

Keywords: AVHRR NDVI; Precipitation; South Australia; rangelands management; spatial analysis; time series correlation; vegetation formations

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/1467-8470.00204

Affiliations: The University of Queensland, Australia, Email: joanne.nightingale@csiro.au

Publication date: July 1, 2003

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