Geostatistical and texture analysis of airborne-acquired images used in forest classification

Abstract

Airborne sensor image texture derived following a geostatistical analysis can increase the accuracy of forest classification because the resulting texture is insensitive to random variations in spectral response but related to the structural features of interest at the scale of a forest inventory (e.g. tree species). The combination of spectral and textural data derived from a kriging surface provided 86% classification accuracy in 36 pure and mixed-wood stands in seven forest classes in Alberta. This is an increase over the classification accuracy obtained when texture was derived from the original image data, and when the spectral response patterns were used alone.

Acknowledgments

Funding was provided by the Canadian Forest Service and the Natural Sciences and Engineering Research Council of Canada. L. Monika Moskal and Graham Gerylo are thanked for their assistance in the collection of the field data. Two anonymous reviewers provided many helpful comments to improve the manuscript.