Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods

This study aims to quantify the effects of topographic variability (measured by coefficient variation of elevation, CV) and lidar (Light Detection and Ranging) sampling density on the DEM (Digital Elevation Model) accuracy derived from several interpolation methods at different spatial resolutions. Interpolation methods include natural neighbor (NN), inverse distance weighted (IDW), triangulated irregular network (TIN), spline, ordinary kriging (OK), and universal kriging (UK). This study is unique in that a comprehensive evaluation of the combined effects of three influencing factors (CV, sampling density, and spatial resolution) on lidar-derived DEM accuracy is carried out using different interpolation methods. Results indicate that simple interpolation methods, such as IDW, NN, and TIN, are more efficient at generating DEMs from lidar data, but kriging-based methods, such as OK and UK, are more reliable if accuracy is the most important consideration. Moreover, spatial resolution also plays an important role when generating DEMs from lidar data. Our results could be used to guide the choice of appropriate lidar interpolation methods for DEM generation given the resolution, sampling density, and topographic variability.

[1]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[2]  R. Sibson,et al.  A brief description of natural neighbor interpolation , 1981 .

[3]  J. Neter,et al.  Applied Linear Regression Models , 1983 .

[4]  S. Weisberg,et al.  Residuals and Influence in Regression , 1982 .

[5]  Margaret Armstrong,et al.  Problems with universal kriging , 1984 .

[6]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .

[7]  A. MacEachren,et al.  Sampling and Isometric Mapping of Continuous Geographic Surfaces , 1987 .

[8]  N. Cressie.  Spatial prediction and ordinary kriging , 1988 .

[9]  N. Cressie.  The origins of kriging , 1990 .

[10]  Michael Edward Hohn,et al.  An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US) , 1991 .

[11]  John C. Davis,et al.  Contouring: A Guide to the Analysis and Display of Spatial Data , 1992 .

[12]  Michael F. Polis,et al.  Iterative TIN generation from digital evaluation models , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  K. Thapa,et al.  Accuracy of spatial data used in geographic information systems , 1992 .

[14]  Jaroslav Hofierka,et al.  Interpolation by regularized spline with tension: II. Application to terrain modeling and surface geometry analysis , 1993 .

[15]  H. Mitásová,et al.  Interpolation by regularized spline with tension: I. Theory and implementation , 1993 .

[16]  M. Sambridge,et al.  Geophysical parametrization and interpolation of irregular data using natural neighbours , 1995 .

[17]  X. F. Zhang,et al.  On the weighted least-squares method for fitting a semivariogram model , 1995 .

[18]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[19]  C. Keller,et al.  Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW) , 1996 .

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

[21]  Philippe J. J. Desmet,et al.  Effects of Interpolation Errors on the Analysis of DEMs , 1997 .

[22]  M. Goodchild,et al.  Scale in Remote Sensing and GIS , 2023 .

[23]  F. Quarta,et al.  Interpolation methods comparison , 1998 .

[24]  U. Lohr,et al.  Digital Elevation Models By Laser Scanning , 1998 .

[25]  Igor V. Florinsky,et al.  Combined analysis of digital terrain models and remotely sensed data in landscape investigations , 1998 .

[26]  Garry R. Willgoose,et al.  On the effect of digital elevation model accuracy on hydrology and geomorphology , 1999 .

[27]  Aloysius Wehr,et al.  Airborne laser scanning—an introduction and overview , 1999 .

[28]  Amy J. Ruggles,et al.  An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting , 1999 .

[29]  Raymond J. Dezzani,et al.  Terrain complexity and reduction of topographic data , 1999, J. Geogr. Syst..

[30]  Qing Zhu,et al.  Effects of Various Factors on the Accuracy of DEMs: An Intensive Experimental Investigation , 2000 .

[31]  A. Behan.  ON THE MATCHING ACCURACY OF RASTERISED SCANNING LASER ALTIMETER DATA , 2000 .

[32]  Jay C. Bell,et al.  Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling , 2001 .

[33]  Thierry Toutin,et al.  Impact of terrain slope and aspect on radargrammetric DEM accuracy , 2002 .

[34]  P. Atkinson,et al.  Deriving DSMs from LiDAR data with kriging , 2002 .

[35]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[36]  L. Montefusco,et al.  Radial basis functions for the multivariate interpolation of large scattered data sets , 2002 .

[37]  Igor V. Florinsky,et al.  Errors of signal processing in digital terrain modelling , 2002, Int. J. Geogr. Inf. Sci..

[38]  Error propagation computing vegetation indices based on Landsat imagery , 2003 .

[39]  M. Hodgson,et al.  An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs , 2003 .

[40]  M. Palmer,et al.  Fractal geometry: a tool for describing spatial patterns of plant communities , 1988, Vegetatio.

[41]  Paul A. Longley,et al.  The Importance of Understanding Error in Lidar Digital Elevation Models , 2004 .

[42]  M. Hodgson,et al.  Accuracy of Airborne Lidar-Derived Elevation: Empirical Assessment and Error Budget , 2004 .

[43]  Stefan Kienzle,et al.  The Effect of DEM Raster Resolution on First Order, Second Order and Compound Terrain Derivatives , 2004, Trans. GIS.

[44]  Qinghua Guo,et al.  The point-radius method for georeferencing locality descriptions and calculating associated uncertainty , 2004, Int. J. Geogr. Inf. Sci..

[45]  Daniel A. Griffith,et al.  Spatial error propagation when computing linear combinations of spectral bands: The case of vegetation indices , 2003, Environmental and Ecological Statistics.

[46]  Tapani Sarjakoski,et al.  Error propagation of DEM-based surface derivatives , 2005, Comput. Geosci..

[47]  J. A. Tullis,et al.  An Evaluation of Lidar-derived Elevation and Terrain Slope in Leaf-off Conditions , 2005 .

[48]  E. Anderson,et al.  LIDAR density and linear interpolator effects on elevation estimates , 2005 .

[49]  Manuel A. Aguilar,et al.  Effects of Terrain Morphology, Sampling Density, and Interpolation Methods on Grid DEM Accuracy , 2005 .

[50]  D. A. Crouse,et al.  Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM , 2006 .

[51]  Norbert Silvera,et al.  Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density , 2006 .

[52]  Manuel A. Aguilar,et al.  The accuracy of grid digital elevation models linearly constructed from scattered sample data , 2006, Int. J. Geogr. Inf. Sci..

[53]  Zhilin Li.  VARIATION OF THE ACCURACY OF DIGITAL TERRAIN MODELS WITH SAMPLING INTERVAL , 2006 .

[54]  Zhenyu Zhang,et al.  The effect of LiDAR data density on DEM accuracy , 2007 .

[55]  Zhenyu Zhang,et al.  LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification , 2007, GeoInformatica.

[56]  Michael E. Hodgson,et al.  Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation , 2007 .

[57]  J. A. Tullis,et al.  Scale Management and Remote Sensor Synergy in Forest Monitoring , 2009 .

[58]  Xiaohang Liu,et al.  Accuracy Assessment of Digital Elevation Models based on Approximation Theory , 2009 .