Estimating Horizontal Displacement between DEMs by Means of Particle Image Velocimetry Techniques

To date, digital terrain model (DTM) accuracy has been studied almost exclusively by computing its height variable. However, the largely ignored horizontal component bears a great influence on the positional accuracy of certain linear features, e.g., in hydrological features. In an effort to fill this gap, we propose a means of measurement different from the geomatic approach, involving fluid mechanics (water and air flows) or aerodynamics. The particle image velocimetry (PIV) algorithm is proposed as an estimator of horizontal differences between digital elevation models (DEM) in grid format. After applying a scale factor to the displacement estimated by the PIV algorithm, the mean error predicted is around one-seventh of the cell size of the DEM with the greatest spatial resolution, and around one-nineteenth of the cell size of the DEM with the least spatial resolution. Our methodology allows all kinds of DTMs to be compared once they are transformed into DEM format, while also allowing comparison of data from diverse capture methods, i.e., LiDAR versus photogrammetric data sources.

[1]  E. Stamhuis,et al.  PIVlab - Time-Resolved Digital Particle Image Velocimetry Tool for MATLAB , 2015 .

[2]  Jay Gao,et al.  Resolution and Accuracy of Terrain Representation by Grid DEMs at a Micro-Scale , 1997, Int. J. Geogr. Inf. Sci..

[3]  Anders Östman,et al.  A comparative test of photogrammetrically sampled digital elevation models , 1986 .

[4]  B. Bates,et al.  Effects of catchment discretization on topographic index distributions , 2008 .

[5]  A. Prasad Particle image velocimetry , 2000 .

[6]  J. F. Reinoso An algorithm for automatically computing the horizontal shift between homologous contours from DTMs , 2011 .

[7]  J. F. Reinoso,et al.  Optical flow algorithm as estimator of horizontal discrepancy between features derived from DEMs: rivers and creeks as case study , 2014 .

[8]  Markus Raffel,et al.  Particle Image Velocimetry: A Practical Guide , 2002 .

[9]  D. Massonnet,et al.  Earthquake displacement fields mapped by very precise correlation. Complementarity with radar interferometry , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[10]  S. M. Wise,et al.  Effect of differing DEM creation methods on the results from a hydrological model , 2007, Comput. Geosci..

[11]  Fulvio Scarano,et al.  Planar velocity measurements of a two-dimensional compressible wake , 2003 .

[12]  Christophe Delacourt,et al.  Velocity field of the “La Clapière” landslide measured by the correlation of aerial and QuickBird satellite images , 2004 .

[13]  E. Berton,et al.  Embedded LDV measurement methods applied to unsteady flow investigation , 2001 .

[14]  Berend G. van der Wall,et al.  Detailed investigation of rotor blade tip vortex in hover condition by 2C and 3C-PIV , 2006 .

[15]  A Avraham Hirschberg,et al.  Application of vortex sound theory to vortex-pairing noise: sensitivity to errors in flow data , 2003 .

[16]  J. F. Reinoso,et al.  A priori horizontal displacement (HD) estimation of hydrological features when versioned DEMs are used , 2010 .

[17]  L. Lourenço Particle Image Velocimetry , 1989 .