Opportunities and challenges provided by regional-scale LiDAR data sets

Abstract

Light detection and Ranging (LiDAR) systems are now widely regarded as the preferred source data for a range of terrain mapping applications owing to their high spatial accuracy, dense surface sampling, and the ability of laser scanners to map topography beneath forest and other vegetation covers. In the past, the prohibitive expense and difficulty involved in LiDAR acquisition meant that these data were most often collected in response to project-specific needs and for relatively small spatial extents. In most jurisdictions, the patchwork of LiDAR data that were publicly available were unlikely to allow for regional-scale analyses and applications. However, the decreased cost of acquisition that has occurred over the past decade, and the proliferation of LiDAR data providers, has changed this situation significantly. An increasing number of municipal, regional, provincial and federal governments have been involved in large-scale LiDAR data acquisition campaigns, the result of which has been the availability of regional-scale fine-resolution digital elevation models (DEMs) for use by researchers, practitioners, and other stakeholders. For example, a recent LiDAR acquisition project carried out in Ontario will soon make aerial LiDAR data publicly available in large portions of the province. The recent availability of extensive LiDAR data sets has been marked by a period of exploration, as practitioners work to replace older topographic data with LiDAR and as novel applications of these data emerge. The unique characteristics of these data provide many opportunities to improve existing workflows and processing methods in a wide range of terrain-related fields of study. For example, LiDAR data have been used for soils mapping, forest canopy modelling, stream mapping, sediment erosion modelling, solar potential modelling, and many other applications involving accurate topographic and canopy modelling. However, the properties of LiDAR data also present numerous and significant challenges for end-users and at present practitioners are commonly struggling to take full advantage of their LiDAR data sets. In addition to the technical issues associated with managing large data volumes, researchers and practitioners are also commonly confronted with problems associated with the extremely fine detail of surface representation. For instance, LiDAR DEMs often include microtopography and excessive surface roughness that can complicate the measurement of the surface parameters (e.g. slope, orientation, curvature, topographic position, surface flow) that are common inputs for other upstream modelling workflows. This presentation will introduce potential solutions to some of these issues, as well as describe their role in enabling large-scale applications of these unique data.