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Open Access Modeling the Probability of Misclassification in a Map of Land Cover Change

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An empirical model was developed to produce a spatially-explicit classification error probability surface for a map of land-cover change resulting from post-classification change analysis. The role of contextual information in predicting change classification error was assessed by testing the significance of variables identifying the absence or presence of classification error in the time-series maps and variables describing landscape composition and structure. A generalized additive model relating errors in classifying change to the series of predictor variables successfully explained over 90 percent of model residual deviance. Predictors capturing the location of classification error in the time-series were the primary determinants of change classification error. However, several predictors describing landscape characteristics were included in the final model. Their inclusion resulted in a sharper delineation between low- and high-error probability regions and a better understanding of the nature of change classification error.
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Document Type: Research Article

Publication date: January 1, 2011

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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