User Tools

Site Tools


nsw_lidar

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision
Previous revision
Next revisionBoth sides next revision
nsw_lidar [2019/02/11 08:28] bushwalkingnsw_lidar [2020/08/05 18:50] bushwalking
Line 24: Line 24:
 The NSW DEM data is supplied in 2km squares. The squares need to be merged into a single DEM for further operations. The NSW DEM data is supplied in 2km squares. The squares need to be merged into a single DEM for further operations.
  
-While this can be done in theory using a virtual raster, I have had poor performance with this. Any operation seems to result in screen redrawing, so moving around and zooming in and out is quite slow and painful.+While this can be done in theory using a Virtual Raster, I have had poor performance with this (including recent version 3.12 Bucuresti). Any operation seems to result in screen redrawing, so moving around and zooming in and out is quite slow and painful. That said, if you are just using the Virtual Raster for future steps, then the limitations from the screen redrawing may not be important.
  
-Instead, I generally use the the Raster- > Miscellaneous -> Merge... function+<insert function here> 
 + 
 +I generally use the the Raster- > Miscellaneous -> Merge... function 
 + 
 +Note that while most of the eastern ranges, where a lot of bushwalking happens, are 2m DEMs, the coast is typically 1m, and the western slopes and plains are 5m (with major rivers 1m!).  
 + 
 +QGIS uses [[https://gdal.org/programs/gdal_merge.html|gdal_merge]], which defaults to using the resolution of the first file. This is not always desired. It can be controlled by using the optional -ps (pixel size) switch. For example, if you have a combination of 1m and 2m DEMs, you can use -ps 1 1 to force them to a merged 1m DEM, or -ps 2 2 to force them to merge to a 2m DEM.
  
 ===== Fill Sinks ===== ===== Fill Sinks =====
Line 131: Line 137:
 The raw stream data is very jagged. Smooth using  The raw stream data is very jagged. Smooth using 
   * v.generalize   * v.generalize
-  * Algorithm = Hermite (there are other options which can be used) +  * Algorithm = Hermite (there are other options which can be used, but Hermite has the smoothed line passing through the points of the original
   * Maximal tolerance value = 20 (in m, obviously scale dependent)   * Maximal tolerance value = 20 (in m, obviously scale dependent)
  
Line 141: Line 147:
 ===== Clifflines ===== ===== Clifflines =====
  
-The steps below have been tested in the Blue Mountains, a region that has a significant number of relatively vertical sandstone cliffs. It may be less effective in different terrain.+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! 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!
Line 153: Line 159:
 using DEM and [1] Maximum Triangle Slope (Tarboton (1997)). I haven't tested any other algorithms.  using DEM and [1] Maximum Triangle Slope (Tarboton (1997)). I haven't tested any other algorithms. 
   
-Cliff areas can be identified using a range of 60-90 and 70-90 degrees on the Slope file. Using 60-90 degrees helps connect logical cliffs and avoid small breaks.+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.
  
 ==== Initial Cleaning ==== ==== Initial Cleaning ====
  
 Next convert data to 1 bit (1,2 not 0,1, as Sieve ignores 0s) using Raster Calculator. Next convert data to 1 bit (1,2 not 0,1, as Sieve ignores 0s) using Raster Calculator.
-Formula is: (Slope > 0) + 1+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. 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.
nsw_lidar.txt · Last modified: 2023/06/02 12:33 by allchin09

Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki