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nsw_lidar [2019/02/10 00:29]
bushwalking
nsw_lidar [2020/08/05 08:50]
bushwalking
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 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 =====
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 For more options in compression,​ look at: For more options in compression,​ look at:
   * GRASS : [[https://​grasswiki.osgeo.org/​wiki/​V.generalize_tutorial|v.generalize]]   * GRASS : [[https://​grasswiki.osgeo.org/​wiki/​V.generalize_tutorial|v.generalize]]
 +V.generalize can also be used to smooth contours - possibly best done prior to simplificiation
  
 ==== Cleaning ==== ==== Cleaning ====
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 {{:​2019-02-08_12_41_50-channel_network.png?​600|}} {{:​2019-02-08_12_41_50-channel_network.png?​600|}}
  
-The raster channel network can then be classified ​and converted ​to vector.+==== Classification ==== 
 + 
 +For 1:25000 maps, I've had reasonable results from using the following formula in the Raster Calculator to classify the streams into categories. Different scales may need different bounds, ​and this doesn'​t account for significantly larger rivers. 
 + 
 +''​( log10 ( "​Catchment Area@1"​ ) >= x) * ( log10 ( "​Catchment Area@1"​ ) < y) * ("​Channel Network@1"​ != 0)''​ 
 + 
 +  * Intermittent:​ 4-6.15 (x-y) 
 +  * Minor: 6.15-7.4  
 +  * Major: 7.4+ 
 + 
 +==== Convert to Vector and Simplify ==== 
 + 
 +Convert ​to vector ​using r.to.vect 
 + 
 +{{:​qgis_raw_stream.png?​600|}} 
 + 
 +The raw stream data is very jagged. Smooth using  
 +  * v.generalize 
 +  * 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) 
 + 
 +Simplify using using: 
 +  * Vector geometry : Simplify 
 +Tolerance:?​ 
  
 ===== 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!
  
 ==== Initial analysis of slope, aspect ==== ==== Initial analysis of slope, aspect ====
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 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: 2020/09/09 10:36 by allchin09