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Publication quality maps using Earth Engine and Cartopy
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KMarkert/cartoee
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Publication quality maps usingEarth Engine andCartopy!
cartoee is available to install viapip. To install the package, you can use pip install for your Python environment:
pip install cartoeeOr, you can install the package manually from source code using the following commands:
git clone https://github.com/kmarkert/cartoee.gitcd cartoeepython setup.py installPlease see thedocumentation for instructions on installing dependencies.
cartoee aims to do only one thing well: getting processing results from Earth Engine into a publication quality mapping interface.cartoee simply gets results from Earth Engine and plots it with the correct geographic projections leavingee andcartopy to do more of the processing and visualization.
Here is what a simple workflow looks like to visualize SRTM data on a map:
import cartoee as ceeimport eeee.Initialize()# get an earth engine imagesrtm = ee.Image("CGIAR/SRTM90_V4")# plot the result using cartoeeax = cee.getMap(srtm,region=[-180,-90,180,90],visParams={'min':0,'max':3000})ax.coastlines()plt.show()
Now that we have our EE image as a cartopy/matplotlib object, we can start styling our plot for the publication using thecartopy API.
import cartopy.crs as ccrsfrom cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER# set gridlines and spacingxticks = [-180,-120,-60,0,60,120,180]yticks = [-90,-60,-30,0,30,60,90]ax.gridlines(xlocs=xticks, ylocs=yticks,linestyle='--')# set custom formatting for the tick labelsax.xaxis.set_major_formatter(LONGITUDE_FORMATTER)ax.yaxis.set_major_formatter(LATITUDE_FORMATTER)# set tick labelsax.set_xticks([-180,-120,-60, 0, 60, 120, 180], crs=ccrs.PlateCarree())ax.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())
Now that we have a grasp on a simple example, we can use Earth Engine to to some processing and make a pretty map.
# function to add NDVI band to imagerydef calc_ndvi(img): ndvi = img.normalizedDifference(['Nadir_Reflectance_Band2','Nadir_Reflectance_Band1']) return img.addBands(ndvi.rename('ndvi'))# MODIS Nadir BRDF-Adjusted Reflectance with NDVI bandmodis = ee.ImageCollection('MODIS/006/MCD43A4')\ .filterDate('2010-01-01','2016-01-01')\ .map(calc_ndvi)# get the cartopy map with EE resultsax = cee.getMap(modis.mean(),cmap='YlGn' visParams={'min':-0.5,'max':0.85,'bands':'ndvi',}, region=[-180,-90,180,90])ax.coastlines()cb = cee.addColorbar(ax,loc='right',cmap='YlGn',visParams={'min':0,'max':1,'bands':'ndvi'})
You can see from the example that we calculated NDVI on MODIS imagery from 2010-2015 and created a global map with the mean value per pixel.
What if we want to make multiple maps with some different projections? We can do that by creating our figure and supplying the axes to plot on.
# get land mass feature collectionland = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')# get seasonal averages and clip to land featuresdjf = modis.filter(ee.Filter.calendarRange(12,3,'month')).mean().clip(land)mam = modis.filter(ee.Filter.calendarRange(3,6,'month')).mean().clip(land)jja = modis.filter(ee.Filter.calendarRange(6,9,'month')).mean().clip(land)son = modis.filter(ee.Filter.calendarRange(9,12,'month')).mean().clip(land)fig,ax = plt.subplots(ncols=2,nrows=2,subplot_kw={'projection': ccrs.Orthographic(-80,35)})imgs = np.array([[djf,mam],[jja,son]])titles = np.array([['DJF','MAM'],['JJA','SON']])for i in range(len(imgs)): for j in range(len(imgs[i])): ax[i,j] = cee.addLayer(imgs[i,j],ax=ax[i,j], region=bbox,dims=500, visParams=ndviVis,cmap='YlGn' ) ax[i,j].coastlines() ax[i,j].gridlines(linestyle='--') ax[i,j].set_title(titles[i,j])cax = fig.add_axes([0.9, 0.2, 0.02, 0.6])cb = cee.addColorbar(ax[i,j],cax=cax,cmap='YlGn',visParams=ndviVis)
To see more examples, go to the documentation athttps://cartoee.readthedocs.io!
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Publication quality maps using Earth Engine and Cartopy