A generic LiDAR workflow in OCAD orienteering mapping
Orienteering isn’t mentioned amongst the 17 industry uses of Light detection and ranging (LIDaR) that Wikipedia covers. Yet LiDAR is being adopted increasingly for our mapping. It offers a reduction in effort, sometimes significant, in both cartography and field work. Bendigo Orienteering Club, VIC was an early adopter of LIDaR and some of their experience will be covered in a forthcoming article.
So what is involved in our using LiDAR? The following 5 step generic workflow from OCAD AG gives you an insight.
1. Import LiDAR data
- LAS (interchange format of 3 dimensional point cloud data)
- XYX (RAW data ASCII)
- ASCII (ESRI ASCII Grid and ESRI Grid XYZ)
- SRMT (Shuttle Radar Topography Mission)
LAS is the preferred format if available as it is efficient and non-proprietary.
DTM and DSM
Separate Digital Elevation Model (DEM) data sets can be derived from LAS. These data sets are the Digital Terrain Model (DTM) and Digital Surface Model (DSM). These allow calculation of vegetation height.
Different parts of DEM data can be merged in OCAD 11.
2. Derive contour lines
Contour lines can be derived at any interval. Assign LiDAR data directly to symbols of index contour line, normal contour line or form line.
The option split in tiles, can save time in deriving contour lines.
3. Derive hill shading
Maybe not for orienteering? However, samples I have seen of relief shading at high resolution, can reveal ditches, erosion gullies and small depressions.
This functionality is likely to be very useful for trail maps.
4. Vegetation height map
This process can not only show vegetation boundaries but also vegetation stages. The latter can indicate the likelihood of runnability and even visibility. For example short, and therefore young, pines are likely to have low runnability and maybe low visibility. Is this useful in preparing for field survey? Maybe we will find out from Bendigo’s experience.
A practical use
A recent development in NSW has enabled detection of species using LiDAR mapping. This is to sufficient accuracy that it is saving considerable field hours in forest management including assessing fire risk from season to season.
5. Derive slopes and cliffs
A slope map can be derived. Cliffs can also be derived according to steepness. For example any slope greater than 60º might be designated as a cliff. The field mapper would then determine if it does rate as a cliff and if so, is it rock or earth. Nicely, the location is already fixed.