Accuracy of automatically extracted geomorphological breaklines from airborne lidar curvature images

Rutzinger, M., Höfle, B. and Kringer, K., 2012. Accuracy of automatically extracted geomorphological breaklines from airborne LiDAR curvature images. Geografiska Annaler: Series A, Physical Geography, 94, 33–42. doi:10.1111/j.1468‐0459.2012.00453.x ABSTRACT Geomorphological breaklines are 3D polylines extracted from high‐resolution digital terrain models (DTMs), which have manifold applications in geomorphology and related fields. Breaklines are used to maintain and refine the characteristic information on discontinuities in the process of DTM filtering and interpolation. Knowledge about the quality of the derived breaklines is important in order to provide adequate input data for DTM generation, geomorphological feature mapping, and change detection analysis. The spatial accuracy and the classification accuracy of derived breaklines depend on data acquisition settings and processing steps. The critical criteria are the precision and density of the LiDAR point cloud, the resolution and quality of the filtered and interpolated DTM and the target scale breaklines are derived for. In this paper a quantitative method for assessing the quality of derived breaklines, which are extracted from regions with high curvature, is presented. Selected components of the breakline detection workflow are analysed. In addition, the reliability of manually digitized reference lines is investigated. Selected geometric properties such as line length, slope and sinuosity are computed and compared to reference data. The properties are discussed regarding their explanatory power with respect to accuracy and precision. Automatic extraction results strongly differ from manually digitized breaklines. Breaklines from manual digitalization are found to vary in coverage and spatial accuracy and thus reflect always the operator's ability, perception and intention. The advantage of automated extracted breaklines is their reproducibility and adaptability to scale.

[1]  Fredrik Lantz,et al.  Determination of Terrain Features in a Terrain Model from Laser Radar Data , 2003 .

[2]  Uwe Soergel,et al.  Aspects of generating precise digital terrain models in the Wadden Sea from lidar–water classification and structure line extraction , 2008 .

[3]  N. Pfeifer,et al.  Line based reconstruction from terrestrial laser scanning data , 2008 .

[4]  Detection of lineaments using airborne laser scanning technology: Laxemar-Simpevarp, Sweden , 2007 .

[5]  K. Kraus,et al.  Determination of terrain models in wooded areas with airborne laser scanner data , 1998 .

[6]  Regine Brügelmann,et al.  AUTOMATIC BREAKLINE DETECTION FROM AIRBORNE LASER RANGE DATA , 2000 .

[7]  P. L. Borgne,et al.  Development of algorithms for , 1999 .

[8]  Daniel D. Frey,et al.  A role for "one-factor-at-a-time" experimentation in parameter design , 2003 .

[9]  M. Rutzinger,et al.  Development of Algorithms for the Extraction of Linear Patterns (Lineaments) from Airborne Laser Scanning Data , 2007 .

[10]  Johannes Otepka,et al.  DTM Modelling and Visualization - The SCOP Approach , 2005 .

[11]  Rutzinger Martin,et al.  Digital terrain models from airborne laser scanning for the automatic extraction of natural and anthropogenic linear structures , 2011 .

[12]  George Vosselman,et al.  Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.

[13]  Svenska Sällskapet för Antropologi och Geografi Geografiska annaler. Series A, Physical geography , 1965 .

[14]  B. Höfle,et al.  Topographic airborne LiDAR in geomorphology: A technological perspective , 2011 .

[15]  Philippe Lagacherie,et al.  Agrarian landscapes linear features detection from LiDAR: application to artificial drainage networks , 2008 .

[16]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[17]  T. Schenk,et al.  Airborne laser swath mapping of the summit of Erebus volcano, Antarctica: Applications to geological mapping of a volcano , 2008 .