Classification of tropical forest classes from Landsat TM data.
Authors: Foody, G.M. and Hill, R.A.
Journal: International Journal of Remote Sensing
Volume: 17
Pages: 2353-2367
ISSN: 0143-1161
DOI: 10.1080/01431169608948777
Abstract:The spectral separability of thirteen topical vegetation classes, including twelve forest types, was assessed. Although the thirteen classes could not be classified to a high accuracy the results of a set of supervised and unsupervised classifications revealed that three groups of classes were highly separable; a classification of the three groups by a discriminant analysis had an accuracy of 92.20%. These three spectrally separable groups also corresponded closely to ecological groups identified from an ordination of data on tree species contained within a detailed ground data set. On the basis of the class separability analyses the three spectrally separable groups were mapped, with an accuracy of 94.84%, from Landsat TM data by a maximum likelihood classification. It was apparent that some of the errors in this classification could be resolved through the use of contextual information and ancillary information, particularly on topography.
http://www.informaworld.com/smpp/content~content=a777938045~db=all~order=page
Source: Manual
Preferred by: Ross Hill