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Abstract-A method for detecting land cover change using NDVI time series data derived from 500m MODIS satellite data is proposed. The algorithm acts as a per pixel change alarm and takes as input the NDVI time series of a 3x3 grid of MODIS pixels. The NDVI time series for each of these pixels was modeled as a triply (mean, phase and amplitude) modulated cosine function, and an Extended Kalman Filter was used to estimate the parameters of the modulated cosine function through time. A spatial comparison between the center pixel of the the 3x3 grid and each of its neighboring pixel's mean and amplitude parameter sequence was done to calculate a change metric which yields a change or no-change decision after thresholding. Although the development of new settlements is the most prevalent form of land cover change in South Africa, it is rarely mapped and known examples amounts to a limited number of changed MODIS pixels. Therefore simulated change data was generated and used for preliminary optimization of the change detection method. After optimization the method was evaluated on examples of known land cover change in the study area and experimental results indicate a 89% change detection accuracy, while a traditional annual NDVI differencing method could only achieve a 63% change detection accuracy.

Abstract-It is proposed that the normalized difference vegetation index time series derived from Moderate Resolution Imaging Spectroradiometer satellite data can be modeled as a triply (mean, phase, and amplitude) modulated cosine function. Second, a nonlinear extended Kalman filter is developed to estimate the parameters of the modulated cosine function as a function of time. It is shown that the maximum separability of the parameters for natural vegetation and settlement land cover types is better than that of methods based on the fast Fourier transform using data from two study areas in South Africa.