Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
A. Schmidt; F. Rottensteiner; U. Soergel (2013)
Publisher: Copernicus Publications
Journal: The International Archives of the Photogrammetry
Languages: English
Types: Article
Subjects: Morphological changes, Spatial and temporal variability, Conditional Random Fields, Temperature control, TA1501-1820, Engineering (General). Civil engineering (General), Classification (of information), Geowissenschaften, Time-differences, Informatik, Informationswissenschaft, allgemeine Werke, Applied optics. Photonics, Lidar, Lidar point clouds, Optical radar, Digital terrain model, Random processes, Technology, Classification, TA1-2040, Coast, Monitoring tasks, T, Ecosystems, Airborne lidar data, Coastal zones
ddc: ddc:550, ddc:000
Coastal areas are characterized by high spatial and temporal variability. In order to detect undesired changes at early stages, enabling rapid countermeasures to mitigate or minimize potential harm or hazard, a recurrent monitoring becomes necessary. In this paper, we focus on two monitoring task: the analysis of morphological changes and the classification and mapping of habitats. Our concepts are solely based on airborne lidar data which provide substantial information in coastal areas. For the first task, we generate a digital terrain model (DTM) from the lidar point cloud and analyse the dynamic of an island by comparing the DTMs of different epochs with a time difference of six years. For the deeper understanding of the habitat composition in coastal areas, we classify the lidar point cloud by a supervised approach based on Conditional Random Fields. From the classified point cloud, water-land-boundaries as well as mussel bed objects are derived afterwards. We evaluate our approaches on two datasets of the German Wadden Sea.
  • No references.
  • No related research data.
  • No similar publications.