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nsw_lidar [2019/02/09 11:41]
bushwalking
nsw_lidar [2019/02/22 21:37]
bushwalking
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 ===== Contours ===== ===== Contours =====
 +==== Basic Processing ====
 There are various contour extraction algorithms in QGIS, for example: There are various contour extraction algorithms in QGIS, for example:
   * GDAL : Raster Extraction : Contour (same as Raster -> Extraction -> Contour...)   * GDAL : Raster Extraction : Contour (same as Raster -> Extraction -> Contour...)
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 {{:​2019_02_08_12_17_09_untitled_project_qgis.png?​300|}} {{:​2019_02_08_12_17_09_untitled_project_qgis.png?​300|}}
 {{:​2019_02_08_12_17_57_untitled_project_qgis.png?​300|}} {{:​2019_02_08_12_17_57_untitled_project_qgis.png?​300|}}
 +
 +Even with sink removal, small 
 +
 +==== Simplifying ====
 +
 +Vectors can be compressed by using something like:
 +  * Vector geometry : Simplify
 +A tolerance of 1(m) seems reasonable for 1:25000 mapping. Smaller tolerances may be appropriate for larger scale maps (eg 1:10000, 1:5000).
 +
 +For more options in compression,​ look at:
 +  * 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 ====
 +
 +Once simplified, it is worth removing small closed loops, such as those in the image below.
 +{{:​contour_loops.png|}}
 +
 +Here is one approach, which involves adding a length attribute to each contour, and removing those that fall below a certain length. It may cause issues if you have short sections of contour near the edge of the map that you need.
 +
 +  * Open Attribute Table (F6)
 +  * Open field calculator (Ctrl+I)
 +  * Add new attribute length, calculated as $length
 +{{::​qgis_add_field.png|}}
 +  * Select all features and filter on length < 25 (or whatever length is appropriate for your scale)
 +{{:​qgis_filter_field.png|}}
  
 ==== Contour Labelling ==== ==== Contour Labelling ====
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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