Multi-Scale and Object-Oriented Image Analysis of High-Res LiDAR Data for Geomorphological Mapping in Alpine Mountains

N. S. Anders, A. C. Seijmonsbergen, W. Bouten
Institute for Biodiversity and Ecosystem Dynamics, Computational Geo-Ecology, Universiteit van Amsterdam,
Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands
Telephone: +31 (0) 20 525 7436
Fax: +31 (0) 20 525 7431
Email: n.s.anders@uva.nl

Geomorphological maps are useful to a wide variety of applications, such as hazard risk analysis (Seijmonsbergen 1992), forest ecological research (Van Noord 1996) and geoconservation evaluation studies (Seijmonsbergen et al. in press). Traditional fieldbased geomorphological mapping strategies are often time consuming and the accuracy of these methods is questionable in steep and difficult-to-access terrain.

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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