GRASS GIS can export your raster layer in most common (and quite a few less common) data formats using the r.out.gdal function (menu: file – export raster map – common raster formats). Exporting is so simple that you may forget that depending on the output format there are different options to optimise your output raster layer. Continue reading
In a by now fairly old post I described how to sample raster values at point location in QGIS. The method I described used the ‘Point Sampling Tool’ addon. However, the function creates a new point layer, which only contains the values extracted from the raster layer. None of the fields in the original point layer is copied to the new one. It is possible to join the attribute table of the new vector point layer with the original attribute table afterwards using a spatial join as explained in that post. However, this will not work if your point data includes points with exactly the same coordinates.
Since I wrote that post, QGIS has come a long way. Continue reading
Just a few days after the release of GRASS GIS 6.4.4, now the latest and greatest QGIS Chugiak. And this new version comes with a whole bunch of improvements and new features. Check out this nice visual visual changelog of the major changes or go straight to the download page to try it out yourself.
You may be aware that there is also a GRASS GIS version 7. So which version should you go for? Well, it depends. Is long-term support, backward-compatibility with the GRASS 6 line and stability important to you. Or do you use the QGIS GRASS GIS toolbox a lot (which is not yet compatible with GRASS 7)? Go for the new GRASS 6.4 series.
Are you always looking for the latest of the latests, or is speed or the ability to handle very large data sets important to you? Have a look at list of new and improved features in GRASS 7. It is still in beta, so in theory less stable. But I should add that I am using this version for some time now (on Linux) and in my experience it is very stable.
Of course, you can also install both, they should run happily next to each other.
Some notes to self about steps I had to take to make GDAL work from within QGIS. Both where compiled from source and run without problems. However, there are still a few issues with running gdal from the QGIS processing toolbox. This may have to do with the fact that I installed both in a non-default location (in the /usr/local/ folder), but in any case, the steps below solved the problem for me. Continue reading
Since Ubuntu 13.10 Google earth cannot be installed out of the box on Ubuntu 64-bit systems because it requires the deprecated ia32-libs package. The previous solution I wrote about, for Ubuntu 13.10, did not work this time. I got Google Earth to run, but it crashed all the time.
I then found the googleearth-package which downloads the latest stable Google Earth installer from Google and creates a package for you. You can then install and remove the created package at will.
But the easiest solution, Continue reading
I needed to create a raster map layer with a weighted random sample of all raster cells, using the percentage of crop land as weight. I couldn’t find a function to create such a weighted sample, so I decided to create a script to do this for me. Continue reading
Recently I needed to create a series of temporally interpolated rasters in between two input raster maps, viz., a suitability distribution map under climate conditions in 2000 and under projected climate conditions in 2020. I though this would be a good time to try out the new temporal database in GRASS GIS and use the t.rast.gapfill function, which replaces gaps in a space time raster data-set with interpolated raster maps using linear interpolation. Continue reading
Just discovered this option in QGIS to save styles for Spatialite layers to the Spatialite database. I don’t know when this option was introduced (I am running the development version at the moment), but I am happy I found it. Continue reading
A common objective of correlative species distribution model is to be able to project the potential distribution of the target species into a new environmental space. This can be a new geographic space (e.g., invasive species) or projected future conditions.
One should be very careful in interpreting results if extrapolating to areas with conditions that fall outside the range of reference environmental variation. There are several methods to visualize this uncertainty. On this blog I have for example mentioned the multi-environmental similarity tool in Maxent (also implemented in R in amongst others the dismo package and as an addon for GRASS GIS), which allows you to create maps that provides the similarity of each point to a set of reference points (Elith et al. 2010) and thus provide a quick overview of areas with ‘novel’ conditions.
A disadvantage of this and other methods is that they only consider the ranges of the individual predictors, and ignore the correlation structure of the covariates used to build the model. In reality, it is not unlikely that at a given locations values of each univariate factor fall within the original range of values, but the combination of environmental conditions is new. Continue reading