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?”
One of the new modules in GRASS GIS 7.03 (RC1) is g.gui.tplot. It is part of the temporal data processing framework (TGRASS) introduced with GRASS GIS 7.0 and lets you plot the values of one or more temporal datasets for a specific point. It furthermore allows plotting data of vector dataset for a defined categories and attribute. In this post I’ll use the tool to plot changes in the annual mean temperature and annual minimum temperature of the coldest month in the last century, using the CRU climate data set. First I’ll provide a brief overview of how to import and prepare the data and finally how to plot a time series using the g.gui.tplot module. Continue reading “Plot of temporal data sets in GRASS GIS”
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”
If you need MODIS satellite data, you should check Nasa’s Reverb web portal. It is a, and I quote, “next generation metadata and service discovery tool”. But, as it turns out, is is already available for our generation ;-), just head over to http://reverb.echo.nasa.gov/.
It has indeed a fairly convenient interface in the form of an online map. You just zoom in to the area of interest and than click and drag a bounding rectangle.
Continue reading “Reverb makes it easy to find and download satellite data”