Analysis of remote sensing data using Hilbert-Huang Transform

The information content derived from the empirical mode decomposition (EMD) is used to study seasonal and interannual variation in satellite-sensed vegetation index data. In a first application, daily normalized difference vegetation index (NDVI) images from the advanced very high resolution radiometers (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) polar satellites were corrected using EMD noise removal techniques. In this case, oscillations proper to intermittent events related to noisy-cloud contamination, to view geometry, and ground " true" signals are discriminated. Three intrinsic mode functions (IMF)s are used to associate confidence intervals to the corrected image at the pixel level providing a better quality product. As a second application, EMD is used to investigate linkages between dominant spatio-temporal dynamics of vegetation signals and modes of inter-annual variation in climate such as El Ni (n) over tildeo Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). A direct linear relationship between ENSO and NAO IMF cycles with correspondent IMFs of vegetation provides a teleconnection index of the relative importance of the climate oscillations in the interannual variation of global land surface vegetation.