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.