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?”
This tutorial contains outdated examples, See the updated tutorial on ecodiv.earth.
The Global Biodiversity Information Facility (GBIF) is an international open data infrastructure that allows anyone, anywhere to access data about all types of life on Earth, shared across national boundaries via the Internet. GBIF provides a single point of access through http://www.gbif.org/ to species records shared freely by hundreds of institutions worldwide. The data accessible through GBIF relate to evidence about more than 1.6 million species, collected over three centuries of natural history exploration and including current observations from citizen scientists, researchers and automated monitoring programs.
There are various ways to import GBIF data, including directly from the website as comma delimited file (csv) and using the v.in.gbif addon for GRASS (I’ll post an example using this addon at a later stage). Here, however, I’ll use the rgbif package for R to obtain the data. In the link section some tutorials are listed that illustrate the use of other R packages. Continue reading “Use R to get gbif data into a GRASS database”
Open data is becoming increasingly important and there are considerable advantages, such as accountability, cost and time savings for users, easier knowledge sharing and increased efficiency in public services.
The importance of open data is more and more recognized (see e.g., this blog article (in Dutch) and this and this report). However, to bank on such advantages, there is a need to increase awareness about open data and make it easy to find and use the open data. Continue reading “Finding open data for the Netherlands”
There is a successor of Cropland Capture, Picture Pile, from the people behind Geo-Wiki. Like Cropland Capture, this tool / game uses a citizen science approach, in this case to track deforestation.
The game presents a series of side-by-side images of the same location several years apart and ask you the question whether “ you see tree loss over time?”. Options are yes, no or maybe.
Perhaps a bit to my surprise, this is fairly addictive and I love the idea behind it. And you can play it on your computer, tablet or phone. If you want to give it a try, go to http://www.geo-wiki.org/games/picturepile/ .
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”
I came across this interesting data source, and though I might as well share it.
Description: A global database of the the width of the large rivers (GWD-LR). The river width is derived from “satellite-based water masks and flow direction maps … by applying the algorithm to the SRTM Water Body Database (WBD) and the HydroSHEDS flow direction map. Both bank-to-bank river width and effective river width excluding islands are calculated for river channels between 60S and 60N”. The results are evaluated against the existing data on the river width of the Congo and Mississippi Rivers. Continue reading “Online data sources: the global width database for large rivers”