Canadian Forest Service Publications

Land cover from multiple thematic mapper scenes using a new enhancement-classification methodology. 1999. Beaubien, J.; Cihlar, J.; Simard, G.; Latifovic, R. Journal of Geophysical Research 104 (D22): 27909-27920.

Year: 1999

Available from: Laurentian Forestry Centre

Catalog ID: 16873

Language: English

CFS Availability: Order paper copy (free), PDF (request by e-mail)

Abstract

The purpose of this paper is to test a methodology for extracting land cover information from high-resolution satellite images over large areas. The use of multiple scenes brings additional complications which are related to atmospheric, phenological, spectral, and classification legend aspects of the data set. The approach is based on a supervised digital mosaicking of the input scenes and on a guided unsupervised classification of the mosaic. The goal of the development was to produce a methodology which is robust, minimally sensitive to the biases of the analyst, and capable of extracting all land cover type information contained in the satellite data. The mosaicking procedure aims to achieve radiometric consistency across the mosaic for key cover types. The classification procedure, named enhancement-classification method (ECM), operates on three (or less) input channels and produces a classification in which virtually the entire relevant spectral content has been extracted. The above procedures were employed to produce a land cover classification of the BOREAS transect from six Landsat thematic mapper images. Initial assessment indicated that the classification accuracy of the mosaics varies with cover type (44-82% for forest cover types). The variability appears related to residual radiometric effects, lack of unique spectral land cover signatures, and vegetation phenology. Thus nonremote sensing (or multitemporal) information is likely to be required for consistent accuracies. While the two-step procedure could be successfully automated under some conditions, the input of an analyst will remain essential until more experience is obtained, especially with classification across multiple scenes.

Date modified: