Error Propagation In Environmental Modeling With Gis
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Request full-text Error Propagation in Environmental Modeling with GISChapter · January 1998 with 41 Reads Publisher: Taylor and Francis1st Gerard Heuvelink38.55 · Wageningen University & ResearchDo you want to read the rest of this chapter?Request full-text CitationsCitations276ReferencesReferences1Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index"Classification-derived area class maps, such as land use/cover and crop type maps, are routinely used as input data for various environmental modeling tasks, such as natural disaster prediction modeling, https://www.crcpress.com/Error-Propagation-in-Environmental-Modelling-with-GIS/Heuvelink/p/book/9780748407439 crop yield assessment, and spatial estimation of air pollution [1][2][3]. Since class maps are used as inputs into environmental models, any errors arising during classification may propagate to the applied model outputs, hence leading to error propagation problems [4]. Therefore, it is of critical importance to generate reliable classification results for further analysis. https://www.researchgate.net/publication/224839892_Error_Propagation_in_Environmental_Modeling_with_GIS "[Show abstract] [Hide abstract] ABSTRACT: Traditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-derived ambiguity index with classification accuracy values at reference data locations. As for the ambiguity measure, a novel discrimination capability index (DCI) is defined from per class posteriori probabilities and then calibrated via logistic regression to derive soft probabilities. Integration of indicator-coded reference data with soft probabilities is finally carried out for mapping classification accuracy. It is demonstrated via a case study involving classification of multi-temporal and multi-sensor SAR datasets, that the proposed approach can provide a map of locally-varying accuracy values, while respecting the overall accuracy derived from the confusion matrix. It can also highlight areas where the benefit of data fusi
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