A method for estimation of a land-cover change matrix from error-prone unit-level observations

摘要

Coregistration and classification errors can seriously compromise direct unit-level (pixel) estimation of land-cover change from remotely sensed data. A more robust alternative to a pixel-based estimation of change is warranted. In a proposed method, spatially adjacent pixels are grouped into 3 x 3 clusters, and the change matrix is obtained from cluster-specific and land cover specific pixel counts at two points in time. The diagonal of a change matrix is estimated by combining an estimate of the temporal correlation of cover type specific, cluster-level counts with an estimate of the odds ratio of no change. Off-diagonal elements are least-squares solutions to a set of linear constraints or obtained by iterative proportional fitting under a model of quasi-independence. In a study with data from five sites, the proposed method produced less biased estimates on three sites if the mean coregistration error was in excess of 0.3-0.7 pixels and on four sites if classification accuracy dropped below 0.9.