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Change detection with heterogeneous data using ecoregional stratification, statistical summaries and a land allocation algorithm

Publication Abstract

Bergen, K.M., Daniel Brown, J.F. Rutherford, and E.J. Gustafson. 2005. "Change detection with heterogeneous data using ecoregional stratification, statistical summaries and a land allocation algorithm." Remote Sensing of Environment, 97(4): 434-446.

A ca. 1980 national-scale land-cover classification based on aerial photo interpretation was combined with 2000 AVHRR satellite imagery to derive land cover and land-cover change information for forest, urban, and agriculture categories over a seven-state region in the U.S. To derive useful land-cover change data using a heterogeneous dataset and to validate our results, we a) stratified the classification using predefined ecoregions, b) developed statistical relationships by ecoregion between land-cover proportions derived from the 1980 national-level classification and aggregate statistical data that were available in time series for all regions in the U.S., c) classified multi-temporal AVHRR data using a process that constrained the results to the estimated proportions of land covers in ecoregions within a multi-objective land allocation (MOLA) procedure, d) interpreted land cover from a sample of aerial photographs from 2000, following the protocols used to produce the 1980 classification for use in accuracy assessment of land cover and land-cover change data, and e) compared land cover and land-cover change results for the MOLA method with an unsupervised classification alone. Overall accuracies for the 2000 MOLA and unsupervised land-cover classifications were 85% and 82%, respectively. On average, the 1980-2000 land-cover change RMSEs were one order of magnitude lower using the MOLA method compared with those based on the unsupervised data. (C) 2005 Elsevier Inc. All rights reserved

DOI:10.1016/j.rse.2005.03.016 (Full Text)

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Country of focus: United States of America.

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