Combining Decision Trees with Hierarchical Object-oriented Image Analysis for Mapping Arid Rangelands

Abstract

Decision tree analysis is a statistical approach for developing a rule base used for image classification. We developed a unique approach using object-based rather than pixelbased image information as input for a classification tree for mapping arid land vegetation. A QuickBird satellite image was segmented at four different scales, resulting in a hierarchical network of image objects representing the image information in different spatial resolutions. This allowed for differentiation of individual shrubs at a fine scale and delineation of broader vegetation classes at coarser scales. Input variables included spectral, textural and contextual image information, and the variables chosen by the decision tree included many features not available or as easily determined with pixel based image analysis. Spectral information was selected near the top of the classification trees, while contextual and textural variables were more common closer to the terminal nodes of the classification tree. The combination of multi-resolution image segmentation and decision tree analysis facilitated the selection of input variables and helped in determining the appropriate image analysis scale.