Exploring species-environment relationships is important for amongst others habitat mapping, biogeographical classification, conservation, and management. And it has become easier with (i) the advance of a wide range of tools, including many open source tools, and (ii) availability of more relevant data sources. For example, there are many tools with which it is relatively easy to create a wide range of derived terrain variables using digital elevation (DEM) or bathymetric (DBM) models. However, the ease of use of many of these tools, especially when used by non-experts, may lead to the selection of arbitrary or sub-optimal set of variables. In addition, derived variables will often be highly correlated (Lecours et al. 2017).
For species or vegetation modelling, one of the first choices to make is the selection of explanatory variables, which in most cases will include climatic or bioclimatic data sets. One of the most widely used global climate data sets in biogeographic and ecological research is from Worldclim (Hijmans et al., 2005). Alternative global rainfall data sets are from TAMSAT TARCAT (Maidment et al., 2014) and CHIRPS (Funk et al., 2014). The Worldclim data layers are based on an interpolation of average monthly climate data from weather stations. The other two data sets combine weather station data with satellite observations to improve accuracy where in situ rainfall measurements are sparse. All three data sets are available from the KITE resources website as part of the Africlim dataset (Platts et al. 2015). Continue reading “Climate data sets, which one to select?”
The new QGIS 2.10 (Pisa) has been released, with many great new features, tweaks and enhancements. Check out the changelog for the highlights (you’ll need some time, it is again an impressive list of improvements and new features).
The source code and binaries for Windows, Debian and Ubuntu are already available via the large download link on the QGIS home page. More packages will follow as soon as the package maintainers finish their work.
A big thanks to the developers, this is again an impressive piece of work!
Riitters et al. (2000) proposed a quick approach to measure the degree of forest fragmentation that could be relatively easily implemented and which only required a map with forest and non-forest. Following their approach, Sylla consult created a shell script for GRASS GIS 6.4 to create a raster layer with six categories (non-forest, patch, transitional, edge, perforated, interior and undetermined) as a measure of forest fragmentation. See their blog post with an explanation how the script works or the above-cited article for a more in-depth description.
I adapted the script to make it work on GRASS 7.0, including some further improvements, such as the option to select the size of the moving window, the option to trim the output layer to avoid the edge effect that comes with moving-window calculations and the option to keep intermediate layers.
I have now rewritten the script as a Python addon. See here for the manual page. Continue reading “Update of r.forestfrag addon for GRASS GIS 7.0”
GRASS GIS always has been lacking proper meta data support. But that has changed with the (relative) new wx.metadata add-on, which includes advanced tools for metadata management according to ISO 19115. It was developed during the Google Summer Code 2014 by Matej Krejci and is available through g.extension for GRASS GIS 7.1. The main tool to create or edit meta data is the g.gui.metadata function.
Below I’ll walk you through the main steps to get and use this tool. For a more detailed explanation of the different options available in wx.metadata and the installation requirements, see the grass wiki page.
Continue reading “Metadata support in GRASS GIS”
I wrote before about the MBtiles format, a convenient format to store tiled maps in a single portable sqlite database. Probably the easiest way to create them is with Tilemill, as described here. The format is supported by amongst others QGIS, but it is especially suitable for use with map viewers on your mobile device.
The format is now supported by various mobile map viewers, including e.g., Geopaparazzi and OruxMaps. One I like for its rich set of features is Locus Map Pro. I normally only write about open source, but I think the developer of this app deserves some credit for being one of the first (as far as I am aware of) to support the MBtiles format. Up to very recently you could only view maps in MBtiles format (similar to the other viewers mentioned above), but with the latest update support for the UTFgrid feature has been added.This basically adds interactivity to your map.
Continue reading “Locus Map Pro carries its support for MBTiles to the next level”
Sometimes you want to rescale a raster layer, e.g., to reduce the number of categories, or to create a common scale for different raster layers. Very basic of course, so you can expect to find an appropriate function in any self-respecting GIS software. Just be aware that different terms are being used for the same thing, e.g., scale in gdal, rescale in GRASS and normalize in SAGA GIS. Below a few ways to do this using my favourite GIS programs: GRASS GIS, QGIS, SAGA GIS or gdal. Continue reading “Rescale your raster data layer”