Methods and uncertainty modeling for land cover change detection from multi-resolution remotely sensed data

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Time series remotely sensed environmental data has been widely used in monitoring changes in earth surfaces and biophysical variables. With rapid development and widespread use of remote sensing technologies, a great deal of airborne and satellite data with a wide range of spatial, spectral and temporal scales has been accumulated. These developments demand significant changes in the approach of analyzing and integrating these data. Many traditional change detection methods in time series remotely sensed data have been developed in the past. Change detection is ideally conducted by utilizing images, separated by time, from the same sensor. However, many conditions will inevitably bring an end to the ideal temporal change detection situation. Many traditional approaches do not work well in change detection involving images with multiple spectral and spatial resolutions. Compared with rapidly growing data availability, there is a clear lag in the development of efficient change detection models and multi-scale analysis framework for handling these multi-temporal remotely sensed data of increasing complexity and quantity.