Nowadays, large quantity of data at faster repeatability is generated from various remote sensors and prompts for spatio-temporally integrated strategies for data handling and information extraction. Change detection is one of the essential techniques for near real-time analysis in remote sensing of the environment. Assuming overall phonological conditions being comparable, change detection is performed either on two-point timescale (bi-temporal) or on a continuous timescale (temporal trajectory analysis), with the latter having the advantage of minimizing the influence of phenology. Univariate image differencing is the most widely applied change detection algorithm, which involves subtracting one date of imagery from a second date that has been co-registered to the first. With "perfect" data, positive and negative values would represent areas of change in the resultant difference imagery, and zero values representing no change.
To quantify the uncertainty in remotely sensed change detection, a geostatistical framework is proposed so that the mean and standard error in pixel or parcel-based difference between the means of the bi-temporal image/map subsets are computed with spatial and temporal dependence accounted for properly, paving the way for probabilistic mapping of changes. To make the proposed approach adaptable to both regular and irregular sampling schemes, block co-kriging is formulated to evaluate means and standard errors in the differences between spatially aggregated means. The geostatistical framework for uncertainty mapping in bi-temporal image/map-based change detection is tested using simulated data sets, whose spatial and temporal correlation can be prescribed. It is anticipated that the geostatistical approach advocated in this paper will make valuable addition to the literature on spatial uncertainty in remote sensing and change detection.