A NEW METHOD FOR MAPPING LANDFORMS USING OBJECT ORIENTED CLASSIFICATION OF DIGITAL ELEVATION MODELS: A CASE STUDY OF DRUMLINS AND HUMMOCKS

The mapping of landforms from Digital Elevation Models (DEMs) has mainly been accomplished via visualization and manual digitizing of features. In each case there is an element of subjectivity and therefore the added possibility of human error. Also, the chance for error increases if the mapping is completed by more than one individual. This is unfortunate as DEMs provide detailed information about the surface of the landscape on which we now live. In their simplest form, they consist of a matrix of numbers that contain an x and y coordinate and a z-elevation value.

This research attempts to eliminate viewer error by objectifying, quantifying, and automating the identification process through the use of the software package eCognition. This package, designed for remote sensing, classifies spatial datasets through analysis of the values of individual pixels (not unlike regular remote sensing software), but also follows the concept that information necessary to interpret an image is not represented in single pixels but in image objects and their mutual relations. These objects can have form, texture, neighbor relations and context. They are identified through a segmentation process that can be performed at a number of scales depending on the size or complexity of the features to be identified. Objects are also identified based on the border smoothness and shape.

Classification of a DEM is significantly easier than classification of a multi-layered satellite dataset. A single layer is used and it is the inter-relationships of z-values that drive the classification process. On essence, the software package treats the z-values as brightness values. Two regions have been examined thus far using this method: portions of the Glaciated Allegheny Plateau, between Cleveland, Ohio and the border of New York and Pennsylvania; and parts of southern Alberta, Canada. For each site, three hierarchies of classification were used which easily delineated large plateau tops different sizes of drumlins, different sizes and shapes of hummocks, valley bottoms (often meltwater channels), and large ripple-like forms. For each region, the methodology developed was applied over several test sites with equally successful results at each site, meaning that classification hierarchies can be successfully applied to different sites.