Canadian Forest Service Publications
Using spatial co-occurrence texture to increase forest structure and species composition classification accuracy. 2001. Franklin, S.E.; Maudie, A.J.; Lavigne, M.B. Photogrammetric Engineering and Remote Sensing 67: 849-855.
Available from: Atlantic Forestry Centre
Catalog ID: 18349
The analysis of forest structure and species composition with high spatial resolution (£ 1 m) multispectral digital imagery is described in an experiment using spatial co-occurrence texture analysis and maximum-likelihood classification. The objective was to determine if higher forest species composition classification accuracies would result in comparison to the use of spectral response patterns alone. Increased accuracy was obtained when using texture at all levels of a classification hierarchy. At the stand level, accuracies were on the order of 75 percent in agreement with field surveys, an improvement of 21 percent over the accuracy obtained using spectral data alone; in stands grouped according to species dominance/co-dominance, the accuracy improved still further to 80 percent. The overall classification accuracy in a highly generalized lifeform classification was 100 percent. This represented a 33 percent increase in accuracy over that which could be obtained, in a classic spectral “signature” classification approach, using spectral response patterns alone.