Abstract Uncertainty modeling now engages the attention of researchers in spatial temporal change analysis in remote sensing. Some studies proposed to use random sets for modeling the spatial uncertainty of image objects with uncertain boundaries, but none have considered the parameter determination problem for large datasets. In this paper we refined the random set models for monitoring monthly changes in wetland vegetation areas from series of images. Twelve cloud-free HJ-1A/1B images from April 2009 to March 2010 were used for monitoring spatial-temporal changes of Poyang Lake wetlands. We applied random sets to represent spatial uncertainty of wetland vegetation that were extracted from normalized difference vegetation index (NDVI) maps. Time series of random sets reflect the seasonal differences of location and extents of the wetlands, whereas degree of uncertainties indicated by SD and CV indices reflect the gradual change of the wetland vegetation in space. Results show that the uncertain extents of wetland vegetation change through the year, achieving the largest range and uncertainty degree in autumn. This coincides with the highly heterogeneous vegetation status in autumn, since the wetland recovers gradually after flooding and young vegetation emerges at gradually changing densities, thus providing forage in different ecological zones for different types of migratory birds. We conclude that the random set model enriches spatial-temporal modeling of phenomena which are uncertain in space and dynamic in time.