Article thumbnail

Evaluation of the performance of DEM interpolation algorithms for LiDAR data

By Xiaoye Liu, Zhenyu Zhang and Jim Peterson


Airborne light detection and ranging (LiDAR) is one of the most effective means for high quality terrain data acquisition. The high-accuracy and high-density LiDAR data makes it possible to model terrain surface in more detail. Using LiDAR data for DEM generation is becoming a standard practice in the spatial science community. Of the three commonly used digital elevation models (e.g., triangular irregular network (TIN), gridded DEM and contour line model), the gridded DEM is the simplest and the most efficient approach in terms of storage and manipulation. However, this approach is liable to introduce errors because of its discontinuous representation of the terrain surface based on the interpolation process of sampled terrain points. Given the characteristics of LiDAR data, much attention must be paid to the selection of an appropriate interpolation algorithm, otherwise the accuracy of produced DEM from LiDAR data will be compromised. This study aims to evaluate the performance of commonly used interpolation algorithms to the LiDAR data, including inverse distance weighted (IDW) method, Kriging method, and local polynomial method. All these interpolation algorithms are applied to DEMs generated from LiDAR at various data density levels. The performance of these interpolation methods is evaluated by using both cross-validation and validation test methods. The results showed the performance of each interpolation algorithm for two study sites with different terrain types and analysed the relationship between interpolation algorithms and LiDAR data density. Considering accuracy and computing time for large volume of LiDAR data, IDW is recommended for LiDAR DEM generation from this study

Topics: 290000 Engineering and Technology
Publisher: Surveying and Spatial Sciences Institute
Year: 2009
OAI identifier:

Suggested articles


  1. (2006). A new approach to relief representation. Surveying and Land Information Science,
  2. (2004). Accuracy of airborne lidar-derived elevation: empirical assessment and error budget. Photogrammetric Engineering and Remote Sensing, doi
  3. (2008). Airborne LiDAR for DEM generation: some critical issues. doi
  4. (2008). An adaptive inverse-distance weighting spatial interpolation technique. doi
  5. (2004). ASPRS Guidelines, Vertical Accuracy Reporting for LiDAR Data. Arerican Society for Photogrammetry and Remote Sensing (ASPRS)
  6. (2006). Causes and consequences of error in digital elevation models. doi
  7. (2003). Corangamite CMA airborne laser survey data documentation. AAMHatch Pty Ltd,
  8. (2007). DEM User Requirements. In Maune,
  9. (2002). Deriving DSMs from LiDAR data with kriging. doi
  10. (2006). Deriving ground surface digital elevation models from LiDAR data with geostatistics. doi
  11. (1998). Digital elevation models by laser scanning. Photogrammetric Record, doi
  12. (2005). Digital terrain modeling: acquisition, manipulation, and application,
  13. (2005). Digital Terrain Modeling: Principles and Methodology, doi
  14. (2006). Factors Influencing ALS Accuracy.
  15. (2004). Guidelines for digital elevation data,
  16. (2004). Interpolation surfaces in ArcGIS spatial analyst.
  17. (2007). Remote Sensing of the Environment: an Earth Resource Perspective, Upper Saddle River,
  18. (1994). Spatial interpolation: an overview. doi
  19. (2007). SRTM DEM accuracy assessment over vegetated areas in Norway. doi
  20. (2008). Using ArcGIS Geostatistical Analyst. Environmental Systems Research Institute,

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.