The data set
The Global Land Cover Facility offers, amongst many other data sets, the MODIS Vegetation Continuous Fields data set for download. These are layers that contain proportional estimates for vegetative cover types (woody vegetation, herbaceous vegetation, and bare ground). As such they are very suitable depict areas of heterogeneous land cover.
Their MODIS products differ from DAAC editions by coming in GeoTIFF format, geographic coordinates, WGS84 datum, and a tiling system designed to fit well with Landsat imagery. Currently the collection 5 is available, which contains proportional estimates for woody cover vegetation for the years 2000 to 2010. It can be downloaded as tiles (195 in total) via a ftp server.
Below I’ll provide an example Continue reading Importing GLCF MODIS woody plant cover
ISRIC, Earth Institute, Columbia University, World Agroforestry Centre (ICRAF) and the International Center for Tropical Agriculture (CIAT) have recently released a new data set of raster layers with various predicted soil properties. This data set is referred to as the “AfSoilGrids250m” data set. It supersedes the SoilGrids1km data set and comes at a resolution of 250 meter. The AfSoilGrids250m data (GeoTIFFs) are available for download under the Attribution 4.0 International (CC BY 4.0) license. See this page for download information.
In this post I’ll show you how you can import this data set in a GRASS GIS database. Continue reading Importing data in GRASS GIS – an example
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
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 Exporting your GRASS raster using r.out.gdal? Check the createopt options!
NASA offers access to its MODIS and ASTER data sets through Reverb|Echo. The data comes in HDF format and uses the Sinusoidal grid tiling system. If your gdal is compiled with HDF4 support (use ./configure –with-hdf4), you can use gdal, or any software that uses gdal, to open the downloaded MODIS tiles directly. For example in QGIS as explained here or in GRASS GIS.
In GRASS you can use the r.in.gdal function. Continue reading Import MODIS data in GRASS using r.in.gdal
A quick note (to myself mostly) about how to extract lines from a text file that end with a specific set of characters. In Linux, you can very easily do this using ‘grep’ or ‘sed’. But, first a little bit of background. Continue reading Extracting lines ending with specific character using sed or grep
The Multivariate Environmental Similarity Surfaces (MESS) is an index that represents how similar a point in space is to a reference set of points, with respect to a set of predictor variables (Elith et al 2010). The function was first implemented as part of the Maxent software package, and is also available in the dismo package for R (see also here and here).
The latter works well on small and medium sized data sets. However, they take a long time to run on larger data sets, e.g., when working with 1km² raster grids covering eastern Africa. I therefore wrote a small R script to compute MESS in GRASS GIS. Continue reading Multivariate Environmental Similarity Surfaces (MESS) index in GRASS GIS