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1

Option 1 (if you have many maps, maybe too much effort): use r.pack/r.unpack or v.pack/v.unpack to export and import the data in a lossless way (they remain in the respective GRASS GIS format). Option 2: Simply use g.mapsets (or g.mapsets -s for a GUI variant)) to add the other mapsets to the search path of the actually mapset and voilĂ  you see all the ...


0

I asked for you in the grass-dev list and obtained this answer - hope it helps: https://lists.osgeo.org/pipermail/grass-dev/2015-May/075188.html r.contour creates a DCELL array for the current region then reads the raster data into it. 12500 rows by 10000 cols by 8 bytes per cell is 1 GB of memory, on top of anything else the module uses. It's ...


0

QGis now has a plugin called Vector Bender, which can spatially adjust Vector Data. You can see it in action in this Video: Presentation of Vector Bender from red on Vimeo.


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He uses a Mac. For the procedure, look at Configuring external applications. Check the path in Processing/Toolbox/Options/Providers/GRASS... If you use the KyngChaos version, there is a GRASS GIS 6.x version integrated in the QGIS application bundle and the path is /Applications/QGIS.app/Contents/MacOS/grass if you want to use another version of GRASS ...


0

Tom Patterson, the lead cartographer at the U.S. National Parks Service has some excellent tutorials on working with DEM data to make beautiful shaded reliefs. Park of his workflow involves using Natural Scene Designer and Adobe Photoshop. For my own workflow I like to use GDAL to resample the size of the DEM before rendering a shaded relief. This often ...


0

@Micha & @gene your answers were helpful for me to get to my desired results. GRASS 6.4.4 (my_project) :~ sudo vim $GISBASE/etc/colors bcyr_custom.txt (enter my password). Now I slightly change the "bcyr" color palette. -0.000001 255:255:255 0.000000 255:255:255 0.060000 255:255:255 0.060001 blue 0.333333 cyan 0.666667 yellow 1.000000 red ...


1

The 'direction' you are referring to is known as the aspect of a slope. To calculate this for a raster DEM in QGIS you need to use the Raster > Analysis > DEM (Terrain models) tool which has a mode for aspect. There's a tutorial for working with terrain analysis in the QGIS Training Manual - lesson 8.3. Once you've generated an aspect raster from your DEM, ...


1

NetworkX (http://cheeseshop.python.org/pypi/networkx/) is a Python package with many functions for graph and network analysis. With this package installed you can solve the problem of generating a shortest distance matrix using the Python console in QGIS. All you need is a network layer (the edges) with a valid topology. The edges need at least 3 ...


1

A way to do this is with a "reclass" map, set up to classify the continuous data into fixed categories. The format of the reclass file might be: GRASS 7.0.0 (WGS84):~ >cat concensus_reclass.txt 0.000000 thru 0.059999 = 1 0.060000 thru 0.333332 = 2 0.333333 thru 0.666666 = 3 0.666667 thru 1.0 = 4 Your color rules file will be changed to: 1 238:238:238 ...


0

As you say, r.cpt2grass is an add-on. Simply download the script and make it executable. It should be executed in the Grass environment (GRASS shell for example) GRASS 6.4.4 (geol):~ > r.cpt2grass --help # or GRASS 7 Description: Convert or apply a GMT color table to a GRASS raster map Usage: r.cpt2grass [-s] input=string [map=string] [output=string] ...


2

Ok, update after another couple of hours of trying. I think I solved the problem, more or less by accident. Since I digitized/simplified the grid structure by hand, one of the problems supposedly was, that a couple of lines would have intersections between their beginning and end points. It seems like a good idea to let QGIS handle this and use 'Split ...


0

In general, you may simply enable the GDAL-internal geotiff: ./configure \ --with-geotiff=internal --with-libtiff=internal ... Rationale: Citation from the manual page: "When built with internal libtiff or with libtiff >= 4.0, GDAL also supports reading and writing BigTIFF files (evolution of the TIFF format to support files larger than 4 GB)."


1

Most textbooks suggest using atan2(Sigma(sin(x)), Sigma(cos(x))), however this is not always the right thing to do. For example, the average of 0, 0 and 90 degrees is atan( (sin(0)+sin(0)+sin(90)) / (cos(0)+cos(0)+cos(90)) ) = atan(1/2)= 26.56 deg, and not 30 deg as one may expect. Take a look at my article on CodeProject "Circular Values Math and ...


5

In R, the package CircStats is old and of rather limited scope and has been replaced by the more complete Circular package. There are tutorials and a book, Circular Statistics with R (2013, A. Pewsey, M. Neuhäuser, and G. D. Ruxton, Oxford University Press, 208 pp.) which explains how to use it (The R scripts can be downloaded from the resources site of the ...


2

you can convert your aspects into the sine and cosine, compute the mean of the sine's and the mean of the cosine's, then turn it back to aspect using atan2(sine,cosine). For more details, see Wikipedia


-2

You can use QGIS, with the GeoSud TOA plugin: The plugin is dedicated to Geosud data. However, it should be used for satellite images acquired in other ways. It converts Digital Numbers (DN) to Top of Atmosphere (ToA) Reflectance for three types of instrument : RapidEye, Spot 5 and Landsat 8.


0

Are you sure that the original "initialDEM.adf" is indeed in the EPSG:31370 coordinate system? Instead of importing with the "-o" flag, try to first create a new region that matches the import DEM by running: r.in.gdal -c input="initialDEM.adf" location="belgium_lambert" Then rerun r.in.gdal without the "-o" flag. BTW, what are you trying to do in the ...


1

I'm not sure what the reason is for this duplications, but did you run v.clean with tool=rmdupl to remove duplicates? See also the v.clean documentation: http://grass.osgeo.org/grass70/manuals/v.clean.html


0

This are the settings of my vector layer: g.region -p vector=CT_ISOBATAS_5m_ETRS89@froga projection: 1 (UTM) zone: 30 datum: etrs89 ellipsoid: grs80 north: 4818925.16988824 south: 4791485.92527958 west: 486512.5000001 east: 601274.08203047 nsres: 1.00011826 ewres: 1.00010965 rows: ...


0

As @xunilk suggested, I found GDAL to be the tool I was looking for. I also used Counter to create a form of histogram that helped me complete my analysis. for i in xrange(0,iRange,resampleInterval): for j in xrange(0,jRange,resampleInterval): scanline = band.ReadRaster(i, j, windowSize, windowSize, windowSize, windowSize, band.DataType) ...


1

What are your region settings? I tried this set of commands, and succeeded to create a DEM with negative values, with no problems: # Set region to low resoution GRASS 7.0.0 (ITM):~ > g.region -p res=5 # The contour vector GRASS 7.0.0 (ITM):~ > v.db.select test_ctours cat|elev 1|-100 2|-80 3|-60 4|-40 5|-20 # Create rasterized contours GRASS 7.0.0 ...


1

Very good points by @ChrisW. An alternative which may do what you seek is to first polygonize your line shapefile and then create your quadrants (I've included a simple example): I then used the Lines to polygons tool: Now you can use the Split features tool (from toolbar, Edit > Split features) which allows you to split your feature (line, polygon ...


2

You need to run r.sun, not r.sunmask for this task. There are also convenient GRASS GIS addons to run it in hourly or daily mode. You first need to extrude your buildings with v.extrude, then run the solar computation. See for extrusion "Extrude 2D polygons to 3D" and for a shadow example here. (image courtesy: Markus Neteler) (image courtesy: Vaclav ...


4

In addition to Micha's list, here is how you can nibble with GRASS 1) mask your image with r.mapcalc 2) with the resulting image, interpolate to the nearest neighbour using r.surf.nnbathy For combine, I would use r.cross but you can also do it using r.mapcalc with this algorithm For mosaic, I would use gdalbuildvrt: it is often not necessary to create a ...


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Here are some options Lookup: (not sure) Zonal stats: The GRASS module r.statistics Focal stats: GRASS r.neighbors Nibble: (don't know) Iterate through VAT: I think that the concept of a VAT is specific to Arc*, but r.describe might get close. Combine: Just use GRASS r.mapcalc Mosaic: GRASS - r.patch or gdal_merge



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