Supervised classification of types of glaciated landscapes using digital elevation data

Automated approaches for identifying different types of glaciated landscapes using digitally processed elevation data were evaluated. We tested the ability of geomorphic measures (e.g. elevation, relative relief, roughness, and slope gradient) derived from digital elevation models (DEMs) to differentiate glaciated landscapes using maximum likelihood classification and artificial neural networks (ANN). The automated methods were trained and validated using an existing Quaternary geology map and a manual interpretation of the contour data portrayed on topographic quadrangles. The need for such methods arises from efforts to classify types of landscapes (e.g. ecoregions) in Michigan. One fundamental control of the landscape structure in Michigan, including soil type and vegetation, is the underlying sedimentary and landform assemblages produced by an array of glacial processes during the waning phase of the Pleistocene. Traditional methods for identifying different landscapes (e.g. ice-contact landscapes, stagnation landscapes) have relied on printed topographic maps and have been very effective, but time consuming. The maps resulting from the four supervised classification trials had between 51% and 61% agreement with the original Quaternary geology map. The output from the maximum likelihood classification had slightly higher agreements than the output from the neural net, which is attributed to the generalization inherent in the Quaternary geology map compared with the nature of the classifier for the neural net. The neural net, however, identifies significant detail and non-linear relationships between classification inputs and output classes. Future work should incorporate a map of soils into the classification.