- Managing DEMs
- Topographic maps
- Mobile apps
This is an old revision of the document!
NSW Spatial Services have undertaken a program to map all of NSW using lidar (light detecting and ranging) For details, see information on their elevation program.
Elevation data can best be accessed through the Geoscience Australia ELVIS program.
It can then processed with a GIS such as QGIS to create useful topographic maps. Instructions below are specifically for use with QGIS, though the general outline may be useful for other GISs.
The original topics here are being progressively moved to their own pages
There are several primary data items for topographic maps that can be generated using the DEM data from the NSW Lidar.
Once you have a depressionless DEM, the following items can be generated:
There are several primary data items for topographic maps that can be generated using the DEM data from the NSW Lidar. The main ones are:
The steps below are works in progress to determine effective (the best?) ways to extract the various items out of the DEM data for use in topographic maps. Any feedback/suggestions of improvements are welcome.
The steps below are being developed for use in the Blue Mountains, a region that has a significant number of relatively vertical sandstone cliffs. It may be less effective in different terrain.
This is more a set of ideas than a fully fledged process. The main aims are to get a set of steps that can largely be automated, and that create cliffline vectors that are running in the correct direction. There is still some way to go on this!
SAGA → Terrain Analysis - Morphometry → Slope, Aspect, Curvature
using DEM and  Maximum Triangle Slope (Tarboton (1997)). I haven't tested any other algorithms.
Cliff areas can be identified using a range of say 60-90 and 70-90 degrees on the Slope file. Using 60-90 degrees helps connect logical cliffs and avoid small breaks.
Next convert data to 1 bit (1,2 not 0,1, as Sieve ignores 0s) using Raster Calculator. Formula is: (Slope > 60) + 1
Then Sieve resulting data using a Threshold of 100 and 8-connectedness to get rid of small non-connected cliffs. Note above that Sieve doesn't like 0s.
Also good to rerun Sieve with smaller Threshold (1-10) and 4-connectedness to a) get rid of some small dangles. b) fill small holes.
Additional smoothing can be done using a User Defined Filter with the following matrix. This will apply some smoothing by allowing you to reclassify the pixel values, and remove single pixel indentations like this:
000 000 101 -> 111 111 111
and single pixel protrusions like this:
000 000 010 -> 000 111 111
The main problem is that the matrix has to be defined each time in QGIS. There doesn't seem to be an option to load it. Possibly this can be done outside QGIS.
0.0 0.5 0.0 0.5 0.5 0.5 0.0 0.5 0.0
If the original matrix is 0/1 then the cutoff will be 1.5
If the original matrix is 1/2 then the cutoff will be 3.5
This step could be run multiple times - some testing would need to be done to determine how many times.
Other options for cleaning the data include a plugin called LecoS, but this doesn't work on QGIS 3. Another possibility is Shrink and Expand - radius 1? But this also creates some new holes that didn't previously exist, so not ideal.
Convert back to 0/1 data using Raster Calculator
Use Translate: set Output Data Type = Byte, set NoData = 0
Run r.thin - r.thin is quite picky about the input file format. Needs to be NULL/non-NULL (not float or int). The Translate process above provides this. The previous two steps could be combined into one. Also, this file may need to be explicitly saved (not just a temporary file?!)
Run r.to.vect: set Feature Type = line
Run v.clean: Cleaning Tool = rmdangle, Threshold = 5,10
GRASS - r.geomorphon function information page. This is a different approach that could be taken for landform classification. Yet to be tested.
Training lession for QGIS 3.4 on GRASS Setup and basic use. Specific GRASS setup is required to use any GRASS functions in QGIS.