Spatio-temporal errors in land–cover change analysis: implications for accuracy assessment

Abstract

This research examined the spatial and temporal patterns of error in time-series classified maps as a first step to creating a model to propagate error in post-classification change analysis. Two Landsat images were acquired for Pittsfield Township, MI, USA, classified, and overlaid to produce a map of change. Error variables were created for the classified maps. Hypotheses were proposed describing the spatial and temporal structures of error in the classified maps, and evaluated using geostatistics and point pattern analysis. Results showed that the spatial error structure for all three classified maps was composed of both a small-scale, local error interactions, and a large-scale trend. Errors in the individual land-cover (LC) maps interacted as a result of temporal dependence, which increased the expected accuracy of the map of change. These findings have important implications on change accuracy assessment, particularly affecting how sampling schemes are defined and how accuracy information is reported.