NUMERIC CLASSIFICATION AS AN AID TO SPECTRAL MAPPING OF VEGETATION COMMUNITIES

This study demonstrates a vegetation mapping methodology that relates the reflectance information contained in multispectral imagery to trad itionally accepted ecological classifications. Key elements of the app roach used are (a) the use of cover rather than density or presence/ab sence to quantify the vegetation, (b) the inclusion of physical compon ents as well as vegetation cover to describe and classify field sites, (c) development of an objective land cover classification from this q uantitative data, (d) use of the held sample sites as training areas f or the spectral classification, and (e) the use of a discriminant func tion to effectively tie the two classifications together. Land cover o ver 39 000 ha of Australian chenopod shrubland was classified into nin e groups using agglomerative hierarchical clustering, a discriminant f unction developed to relate cover and spectral classes, and the vegeta tion mapped using a maximum likelihood classification of multi-date La ndsat TM imagery. The accuracy of the mapping was assessed with an ind ependent set of held samples and by comparison with a map of land syst ems previously interpreted from aerial photography. Overall agreement between the digital classification and the land system map was good. T he units that have been mapped are those derived from numeric vegetati on classification, demonstrating that accepted ecological methods and sound image analysis can be successfully combined.