Discrete classification approach to land cover and land use change identification based on Landsat image time sequences

Dense multi-temporal stacks of Landsat imagery have most commonly been exploited to identify land cover and land use changes (LCLUC) based on detection of abrupt changes in continuous value spectral indices. In this study, a discrete classification approach to LCLUC identification based on stable training sites is tested on a nine-date, 4-year Landsat-7 ETM + time sequence for a study area in Ghana that is prone to cloud cover. Change to Built cover, as an indication of urban expansion, was identified for over 70% of testing units when a spatial-temporal majority filter that ignored No Data values from clouds, cloud shadows and sensor effects was applied. More important, relatively stable LCLU maps were generated and No Data effects should not limit the potential of the approach for longer-term retrospective analyses or monitoring of LCLUC in cloud-prone regions.