Improving our understanding of the uncertainty associated with a map of land-cover change is needed given the importance placed on modeling our changing landscape. My dissertation research addressed the challenges of estimating the accuracy of a map of change by improving our understanding of the spatio-temporal structure of error in multi-date classified imagery, investigating the relative strength and importance of a temporal dependence between classification errors in multi-date imagery, and exploring the interaction of classification errors within a simulated model of land-cover change. First, I quantified the spatial and temporal patterns of error in multi-date classified imagery acquired for Pittsfield Township, Michigan. Specifically, I examined the propagation of error in a post-classification change analysis. The spatial patterns of misclassification for each classified map, the temporal correlation between the errors in each classified map, and secondary variables that may have affected the pattern of error associated with the map of change were analyzed by addressing a series of research hypothesis. The results of all analyses provided a thorough description and understanding of the spatio-temporal error structure for this test township. Second, I developed a model of error propagation in land-cover change that simulated user-defined spatial and temporal patterns of error within a time-series of classified maps to assess the impact of the specified error patterns on the accuracy of the resulting map of change. Two models were developed. The first established the overall modeling framework using land-cover maps composed of two land-cover classes. The second extended the initial model by using three land-cover class maps to investigate model performance under increased landscape complexity. The results of the simulated model demonstrated that the presence of temporal interaction between the errors of individual classified maps affected the resulting accuracy of the map of change, and the magnitude of this effect was dependent on both the pattern of change and pattern of error considered. This dissertation took an important step forward in improving our understanding of the spatio-temporal structure of classification error in a change analysis. The results of this work provide the starting point for building a conceptual model of change error.Ph.D.Earth SciencesPhysical geographyRemote sensingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/127046/2/3328777.pd
