Remotely sensed images as a major data source to observe the earth have been extensively used in spatial-temporal analysis in environmental research. Information on the spatial distribution and spatial-temporal dynamics of natural landscapes recorded by series of images, however, usually bears various kinds of uncertainties. This thesis proposes a random set method to deepen our insight into the uncertainties that are inherent in these observations of natural phenomena from images. The general objective of this research is to develop different techniques based on random sets to represent image objects with indeterminate boundaries, quantify their extensional uncertainties, and address uncertainty modeling in a spatial temporal change analysis. The methods are applied to classifying wetland vegetation and monitoring wetland inundation in the Poyang Lake area in China. This research shows that random sets provide a general framework for describing uncertainties of natural landscape extracted from remote sensing images. It enriches spatial and spatio-temporal modeling of phenomena which are uncertain in space and dynamic in time.
