LIDAR data reduction for efficient and high quality DEM generation

Abstract

Airborne Light Detection and Ranging (LiDAR) - also referred to as Airborne Laser Scanning (ALS), provides means for high density and high accuracy topographic data acquisition. LiDAR data have become a major source of digital terrain data and have been used in a wide of areas, such as building extraction and 3D urban modelling, hydrological modelling, glacier monitoring, landform or soil classification, river bank or coastal management, and forest management. However, terrain modelling has been the primary focus of most LiDAR collection missions. The use of LiDAR for terrain data collection is becoming a standard practice in spatial science community. There has been a significant increase in the use of LiDAR data for DEM generation over the last decade as more reliable and accurate LiDAR systems were developed. LiDAR data accuracy and density are such that reliable and high accuracy, high resolution DEM generation can be confidently contemplated. However, high density data lead to a significant increase in the data volume, imposing challenges with respect to data storage, processing and manipulation. Strategies for handling the large volume of terrain data without sacrificing accuracy are required. Through informed reduction in data (i.e. ration of the information content to the volume of the dataset), a more manageable and operationally sized terrain dataset for DEM generation is possible. This study aims to generate an efficient and high quality DEM using LiDAR data in a catchment region in Australia. It explored the effects of LiDAR data density on the accuracy of DEMs and examined to what extent a set of LiDAR data can be reduced yet still maintain adequate accuracy for DEM generation. LiDAR data reduction mitigates the data redundancy and improves data processing efficiency in terms of both storage and processing time.