Detecting land cover change using an extended Kalman filter on MODIS NDVI time series data

A method for detecting land cover change using NDVI time-series data derived from 500-m MODIS satellite data is proposed. The algorithm acts as a per-pixel change alarm and takes the NDVI time series of a 3 x 3 grid of MODIS pixels as the input. 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 3 x 3 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 amount to a limited number of changed MODIS pixels. Therefore, simulated change data were generated and used for the 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 an 89% change detection accuracy while a traditional annual NDVI differencing method could only achieve a 63% change detection accuracy.