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--- nsw_lidar [2019/02/09 22:41] – bushwalking
+++ nsw_lidar [2019/02/23 08:37] – bushwalking
@@ Line 44: / Line 44: @@
 ===== Contours =====
+==== Basic Processing ====
 There are various contour extraction algorithms in QGIS, for example:
   * GDAL : Raster Extraction : Contour (same as Raster -> Extraction -> Contour...)
@@ Line 51: / Line 51: @@
 {{:2019_02_08_12_17_09_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 ====
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 {{: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 =====
-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 ====
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 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 ====
 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.