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 much more details. Using LiDAR data for DEM generation is becoming a standard practice in spatial related disciplines. 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 investigate the suitability of commonly used interpolation algorithms to the LiDAR data, including inverse distance weighted (IDW) method, Kriging method, and Spline method. All these interpolation algorithms are applied to the DEM generation from LiDAR at various resolutions. 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 the study area and analyzed the relationship between interpolation algorithm and the model resolution. Suitable interpolation algorithm for LiDAR data was recommended.