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Abstract
Time series analysis of Normalized Difference Vegetation Index (NDVI) imagery is a powerful tool in studying land use and precipitation interaction in data‐scarce and inaccessible areas. The Fast Fourier Transform (FFT) was applied to the annual time series of 36 average dekadal NDVI images. The dekadal annual average pattern was calculated from 189 NDVI images from April 1998 to June 2003 acquired with the VEGETATION instruments of the SPOT‐4 and SPOT‐5 satellites in Tibet. It is shown that the first two harmonic terms of a Fourier series suffice to distinguish between land use classes. The results indicate that the highest biomass production occurs before the monsoon peak. Regression analysis with 15 meteorological stations has shown that the total amount of precipitation during the growing season shows the strongest relation with the sum of the amplitudes of the first two harmonic terms (R 2 = 0.72). Inter‐annual NDVI variation based on Fourier‐transformed time series was studied and it was shown that, early in the season, the expected NDVI behaviour of the up‐coming season could be forecast; if linked to food production this might provide a robust early warning system. The most important conclusion from this work is that harmonic time series analysis yields more reliable results than ordinary time series analysis.
Acknowledgments
We thank Dr Lu Changhe of the Institute of Geographic Sciences and Natural Resources Research of the Chinese Academy of Sciences (CAS) for providing meteorological data for Tibet. We also express our gratitude to the SPOT VEGETATION project for providing and distributing NDVI satellite imagery free of charge. This work was funded by the Dutch ecoregional fund to support methodological initiatives.