Uncertainty characterization in remotely sensed land cover information

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Jingxiong Zhang ¹ and Jiabing Sun ²
_{¹ School of Remote Sensing, Wuhan University

LIESMARS – Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing

129 LuoYu Road, Wuhan 430079, China

Tel: +086 27 63187371; Fax: + 086 27 68778086

jxzhang@whu.edu.cn

² School of Remote Sensing, Wuhan University

129 LuoYu Road, Wuhan 430079, China}

Abstract
Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are they useful for quantifying error margins in derivatives, such as areal extents of different land cover types and land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be developed on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. Experiment using simulated data sets was carried out to quantify error in land cover change derived from post-classification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus consolidating the need for stochastic simulation in error modeling. Further investigations are anticipated incorporating indicators and probabilistic data for mapping and propagating misclassification.

Keywords: geostatistics, land cover change, misclassification, stochastic simulation

In: Caetano, M. and Painho, M. (eds). Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 5 – 7 July 2006, Lisboa, Instituto Geográfico Português