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https://github.com/KMarkert/cartoee

Publication quality maps using Earth Engine and Cartopy
https://github.com/KMarkert/cartoee

cartopy earth-engine plot visualization

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Publication quality maps using Earth Engine and Cartopy

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README 

        
# cartoee

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Publication quality maps using [Earth Engine](https://earthengine.google.com/) and [Cartopy](https://scitools.org.uk/cartopy/docs/latest/)!

![alt-text](./docs/_static/intro_fig.png)



### Installation

`cartoee` is available to install via `pip`. To install the package, you can use pip  install for your Python environment:



```

pip install cartoee

```



Or, you can install the package manually from source code using the following commands:



```

git clone https://github.com/kmarkert/cartoee.git

cd cartoee

python setup.py install

```



Please see the [documentation](https://cartoee.readthedocs.io/en/latest/introduction.html#dependencies) for instructions on installing dependencies.



### Working with cartoee

`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 leaving `ee` and `cartopy` to do more of the processing and visualization.



#### A simple case



Here is what a simple workflow looks like to visualize SRTM data on a map:



```

import cartoee as cee

import ee



ee.Initialize()



# get an earth engine image

srtm = ee.Image("CGIAR/SRTM90_V4")



# plot the result using cartoee

ax = cee.getMap(srtm,region=[-180,-90,180,90],visParams={'min':0,'max':3000})



ax.coastlines()

plt.show()

```

![alt-text](./docs/_static/srtm_fig.png)



Now that we have our EE image as a cartopy/matplotlib object, we can start styling our plot for the publication using the `cartopy` API.



```

import cartopy.crs as ccrs

from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER



# set gridlines and spacing

xticks = [-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 labels

ax.xaxis.set_major_formatter(LONGITUDE_FORMATTER)

ax.yaxis.set_major_formatter(LATITUDE_FORMATTER)



# set tick labels

ax.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())

```

![alt-text](./docs/_static/srtm_fig2.png)



#### Doing more...

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 imagery

def 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 band

modis = ee.ImageCollection('MODIS/006/MCD43A4')\

        .filterDate('2010-01-01','2016-01-01')\

        .map(calc_ndvi)



# get the cartopy map with EE results

ax = 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'})

```

![alt-text](./docs/_static/global_ndvi.png)



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 collection

land = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')



# get seasonal averages and clip to land features

djf = 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)

```

![alt-text](./docs/_static/seasonal_ndvi.png)



To see more examples, go to the documentation at https://cartoee.readthedocs.io!