The effect of LiDAR data density on DEM accuracy

Abstract

Digital Elevation Models (DEMs) play an important role in terrain related applications, and their accuracy is crucial for DEM applications. There are many factors that affect the accuracy of DEMs, with the main factors including the accuracy, density and distribution of the source data, the interpolation algorithm, and the DEM resolution. Generally speaking, the more accurate and the denser the sampled terrain data are, the more accurate the produced DEM will be. Traditional methods such as field surveying and photogrammetry can yield high accuracy terrain data, but are very time consuming and labour intensive. Moreover, in some situations such as in densely forested areas, it is impossible to use these methods for collecting elevation data. Light Detection and Ranging (LiDAR) offers high density data capture. The high accuracy three dimensional terrain points prerequisite to very detailed high resolution DEMs generation offers exciting prospects to DEM builders. However, because there is no sampling density selection for different area during a LiDAR data collection mission, some terrains may be oversampled thereby imposing increases in data storage requirements and processing time. Improved efficiency in these terms can accrue if redundant data can be identified and eliminated from the input data set. With a reduction in data, a more manageable and operationally sized terrain dataset for DEM generation is possible (Anderson et al., 2005a). The primary objective of data reduction is to achieve an optimum balance between density of sampling and volume of data, hence optimizing cost of data collection (Robinson, 1994). Some studies on terrain data reduction have been conducted based on the analysis of the effects of data reduction on the accuracy of DEMs and derived terrain attributes. For example, Anderson et al. (2005b) evaluated the effects of LiDAR data density on the production of DEM at different resolution. They produced a series of DEMs at different horizontal resolutions along a LiDAR point-density gradient, and then compared each of these DEMs to a reference DEM produced from the original LiDAR data, this having been acquired at the highest available density. Their results showed that higher resolution DEM generation is more sensitive to data density than is lower resolution DEM generation. It was also demonstrated that LiDAR datasets could withstand substantial data reductions yet still maintain adequate accuracy for elevation predictions (Anderson et al., 2005a) This study explored the effects of LiDAR point density on DEM accuracy and examined to scope for data volume reduction compatible with maintaining efficiency in data storage and processing. Something of the relationship between data density, data file size, and processing time also emerges from this study