Topographic analysis of remotely sensed digital elevation data is a potential tool to speed up and increase accuracy of the mapping procedure. Recent studies argue that image segmentation and object-oriented classification strategies are intuitive to (semi-) automatically produce a classified hillslope or geomorphological map (Drăguţ and Blaschke 2006; Van Asselen and Seijmonsbergen 2006) based on Digital Elevation Models (DEMs) and their derivatives. However, an accurate identification and classification of individual landforms and their genesis remains a challenge, partly due to the multi-scale nature of geomorphological processes.
This research-in-progress is part of a PhD project for developing a method to classify image objects on their geomorphological nature in a multi-scale framework, based on geomorphometric parameters derived from high-resolution LiDAR (Light Detection And Ranging) data. In future research, we will integrate this detailed LiDAR-derived geomorphological information in a dynamic simulation model to facilitate landscape evolution research in complex and difficult-to-access terrain at greater detail than before.
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