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Srtmmosaic <- mosaic(srtm, srtm2, srtm3, fun=mean) You can get more info on the tile extent here.
#Descargar getdata full#
Lets download two more eastern tiles and mosaic them to get the full extent of Austria. boundaries of Austria together with the SRTM Tile in one plot: plot(srtm)Īs you can see, not all of Austria is covered by this tile.
#Descargar getdata code#
The code above will return one SRTM Tile somewhere around Vienna. Specify Lat: The second argument specifies the lat of the SRTM tile.Specify Lon: The second argument specifies the lon of the SRTM tile.‘SRTM’ returns the SRTM 90 elevation data. Select Dataset: The first argument specifies the dataset.We will use the getData() function one last time: srtm <- getData('SRTM', lon=16, lat=48) Last but not least, lets have a look at the SRTM 90 Data. Plot(climate$bio1, main="Annual Mean Temperature") Lets plot the first indicator “Annual Mean Temperature”: #Plot The code above returns a raster with the 18 bioclimate variables covering the whole world with a resoltion of 2.5 minutes of degrees:īIO2 = Mean Diurnal Range (Mean of monthly (max temp – min temp))īIO4 = Temperature Seasonality (standard deviation *100)īIO7 = Temperature Annual Range (BIO5-BIO6)īIO8 = Mean Temperature of Wettest QuarterīIO9 = Mean Temperature of Driest QuarterīIO10 = Mean Temperature of Warmest QuarterīIO11 = Mean Temperature of Coldest QuarterīIO15 = Precipitation Seasonality (Coefficient of Variation) In the case of res=0.5, you must also provide a lon and lat argument for a tile. Specify resolution: 0.5, 2.5, 5, and 10 (minutes of a degree).Select variable: The second argument specifies the variable: ‘tmin’, ‘tmax’, ‘prec’ and ‘bio’ ( more info here).‘worldclim’ returns the World Climate Data. Lets do the same with the World Climate data, here you also have to specify three arguments: climate <- getData('worldclim', var='bio', res=2.5) Lets compare them to the Level 1 subdivision by plotting both of them: #Get DataĪustria0 <- getData('GADM', country="AUT", level=0)Īustria1 <- getData('GADM', country="AUT", level=1) The code above returns the boundaries for Austria for the level 0. Specify level: The third argument specifies the level of of administrative subdivision (0=country, 1=first level subdivision).Select country: The second argument provides the country name of the boundaries by using its ISO A3 country code ( more info here).‘GADM’ returns the global administrative boundaries. boundaries we have to specify three arguments: install.packages("raster")Īustria0 <- getData('GADM', country='AUT', level=0)
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To be able to use the getData() function to acquire data about global amd.
#Descargar getdata install#
Install the raster package and load it first.
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SRTM 90 (elevation data with 90m resolution between latitude -60 and 60).With the function getData() you can download the following data directly into R and process it: The raster package is not only a great tool for raster processing and calculation but also very useful for data acquisition. Today I will show how powerful the R package is on another example.