It is proposed that the NDVI time series derived from MODIS multitemporal remote sensing data can be modelled as a triply (mean, phase and amplitude) modulated cosine function. A non-linear Extended Kalman Filter was developed to estimate the parameters of the modulated cosine function as a function of time. It was shown that the maximum separability of the parameters for different vegetation land cover was better than that of a spectral method based on the Fast Fourier Transform (FFT). Thus it is theorized that the cosine function parameters estimated using the EKF is superior for both classifying land cover and detecting change over time when compared to methods based on the FFT. Results from two study areas in Southern Africa are provided to show the improved separability using MODIS data.
Reference:
Kleynhans W, Olivier JC, Salmon, BP, Wessels, KJ and Van den Berg, F. 2009. Improving NDVI time series class separation using an extended Kalman filter. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009, pp 1-4
Kleynhans, W., Olivier, J., Salmon, B., Wessels, K. J., & Van den Bergh, F. (2009). Improving NDVI time series class separation using an extended Kalman filter. IEEE. http://hdl.handle.net/10204/3980
Kleynhans, W, JC Olivier, BP Salmon, Konrad J Wessels, and F Van den Bergh. "Improving NDVI time series class separation using an extended Kalman filter." (2009): http://hdl.handle.net/10204/3980
Kleynhans W, Olivier J, Salmon B, Wessels KJ, Van den Bergh F, Improving NDVI time series class separation using an extended Kalman filter; IEEE; 2009. http://hdl.handle.net/10204/3980 .
