Abstract

Landsat Thematic Mapper data were the basis in production of a statewide land cover dataset for Wisconsin, undertaken in partnership with USGS’s Gap Analysis Program. The dataset contained seven classes comparable to Anderson Level I and 24 classes comparable to Anderson Levels II/III. Twelve scenes of dual-date TM data were processed with methods that included principal components analysis; stratification into spectrally consistent units; separate classification of upland, wetland, and urban areas; and a hybrid supervised/unsupervised classification called ”guided clustering”. The final data had overall accuracies of 94% for Anderson Level I upland classes, 77% for Level II/III upland classes, and 84% for Level II/III wetland classes. Classification accuracies for deciduous and coniferous forest were 95% and 93%, respectively, and forest species’ overall accuracies ranged from 70 to 84%. Limited availability of acceptable imagery necessitated use of an early May date in a majority of scene pairs, perhaps contributing to lower accuracy for upland deciduous forest species. The mixed deciduous/coniferous forest class had the lowest accuracy, most likely due to distinctly classifying a purely mixed class. Mixed forest signatures containing oak were often confused with pure oak. Guided clustering was seen as an efficient classification method, especially at the tree species level, although its success relied in part on image dates, accurate ground truth, and some analyst intervention.

Keywords

Land cover; multi-seasonal; Landsat

Published in

Remote Sensing of Environment
2002, number: 82
Publisher: Elsevier Science

Permanent link to this page (URI)

https://res.slu.se/id/publ/123945