Semi-automated identification and extraction of geomorphological features using digital elevation data

Abstract Geomorphological maps that are automatically extracted from digital elevation data are gradually replacing classical geomorphological maps. Commonly, digital mapping projects are based upon statistical techniques, object-based protocols or both. In addition to digital elevation data, expert knowledge can still be used to calibrate feature extraction algorithms. Such hybrid expert/statistical-based methods translate land surface parameters into areal extents of geomorphological features in an automated, reproducible manner, which increases spatial detail of final products and speeds up map production. The development of efficient statistical methods for the extraction of geomorphological features is today promoted by high-resolution digital elevation data from light detection and ranging (LiDAR) technology. In this chapter, case studies from the Netherlands (very low relief) and Austrian Alps (high relief) are presented to illustrate how statistical-based and object-based supervised classification can be used for the semi-automated identification and extraction of geomorphological features using high-resolution LiDAR digital elevation models (DEMs). In the first case study, multinomial logistic regression is used to increase the detail of a classic geomorphological map. Medial axes of the manually delineated polygons are used to locate the training pixels and to build the statistical model, which is then implemented over the whole area of interest. In the second case study, object-based segmentation of slope and topographic openness extracted from LiDAR data are used for rule-based mapping of geomorphological features. Both studies confirm that LiDAR DEMs can be used to increase detail of existing geomorphological maps. In addition, semi-automated techniques provide a more objective framework for geomorphological mapping.

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