Uncomment the following line to install geemap and cartopy if needed. Keep in mind that cartopy can be challenging to install. If you are unable to install cartopy on your computer, you can try Google Colab with this the notebook example.

See below the commands to install cartopy and geemap using conda/mamba:

conda create -n carto python=3.8
conda activate carto
conda install mamba -c conda-forge
mamba install cartopy scipy -c conda-forge
mamba install geemap -c conda-forge
jupyter notebook

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# !pip install cartopy scipy
# !pip install geemap

# !pip install cartopy scipy # !pip install geemap

How to create publication quality maps using cartoee¶

cartoee is a lightweight module to aid in creating publication quality maps from Earth Engine processing results without having to download data. The cartoee package does this by requesting png images from EE results (which are usually good enough for visualization) and cartopy is used to create the plots. Utility functions are available to create plot aesthetics such as gridlines or color bars. The notebook and the geemap cartoee module (cartoee.py) were contributed by Kel Markert. A huge thank you to him.

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%pylab inline

import ee
import geemap
import matplotlib.pyplot as plt

# import the cartoee functionality from geemap
from geemap import cartoee

%pylab inline import ee import geemap import matplotlib.pyplot as plt # import the cartoee functionality from geemap from geemap import cartoee

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geemap.ee_initialize()

geemap.ee_initialize()

Plotting an image¶

In this first example we will explore the most basic functionality including plotting and image, adding a colorbar, and adding visual aethetic features. Here we will use SRTM data to plot global elevation.

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# get an image
srtm = ee.Image("CGIAR/SRTM90_V4")

# get an image srtm = ee.Image("CGIAR/SRTM90_V4")

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# geospatial region in format [E,S,W,N]
region = [180, -60, -180, 85]  # define bounding box to request data
vis = {"min": 0, "max": 3000}  # define visualization parameters for image

# geospatial region in format [E,S,W,N] region = [180, -60, -180, 85] # define bounding box to request data vis = {"min": 0, "max": 3000} # define visualization parameters for image

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fig = plt.figure(figsize=(15, 10))

# use cartoee to get a map
ax = cartoee.get_map(srtm, region=region, vis_params=vis)

# add a colorbar to the map using the visualization params we passed to the map
cartoee.add_colorbar(ax, vis, loc="bottom", label="Elevation", orientation="horizontal")

# add gridlines to the map at a specified interval
cartoee.add_gridlines(ax, interval=[60, 30], linestyle=":")

# add coastlines using the cartopy api
ax.coastlines(color="red")

plt.show()

fig = plt.figure(figsize=(15, 10)) # use cartoee to get a map ax = cartoee.get_map(srtm, region=region, vis_params=vis) # add a colorbar to the map using the visualization params we passed to the map cartoee.add_colorbar(ax, vis, loc="bottom", label="Elevation", orientation="horizontal") # add gridlines to the map at a specified interval cartoee.add_gridlines(ax, interval=[60, 30], linestyle=":") # add coastlines using the cartopy api ax.coastlines(color="red") plt.show()

This is a decent map for minimal amount of code. But we can also easily use matplotlib colormaps to visualize our EE results to add more color. Here we add a cmap keyword to the .get_map() and .add_colorbar() functions.

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fig = plt.figure(figsize=(15, 10))

cmap = "gist_earth"  # colormap we want to use
# cmap = "terrain"

# use cartoee to get a map
ax = cartoee.get_map(srtm, region=region, vis_params=vis, cmap=cmap)

# add a colorbar to the map using the visualization params we passed to the map
cartoee.add_colorbar(
    ax, vis, cmap=cmap, loc="right", label="Elevation", orientation="vertical"
)

# add gridlines to the map at a specified interval
cartoee.add_gridlines(ax, interval=[60, 30], linestyle="--")

# add coastlines using the cartopy api
ax.coastlines(color="red")

ax.set_title(label="Global Elevation Map", fontsize=15)

plt.show()

fig = plt.figure(figsize=(15, 10)) cmap = "gist_earth" # colormap we want to use # cmap = "terrain" # use cartoee to get a map ax = cartoee.get_map(srtm, region=region, vis_params=vis, cmap=cmap) # add a colorbar to the map using the visualization params we passed to the map cartoee.add_colorbar( ax, vis, cmap=cmap, loc="right", label="Elevation", orientation="vertical" ) # add gridlines to the map at a specified interval cartoee.add_gridlines(ax, interval=[60, 30], linestyle="--") # add coastlines using the cartopy api ax.coastlines(color="red") ax.set_title(label="Global Elevation Map", fontsize=15) plt.show()

Plotting an RGB image¶

cartoee also allows for plotting of RGB image results directly. Here is an example of plotting a Landsat false-color scene.

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# get a landsat image to visualize
image = ee.Image("LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318")

# define the visualization parameters to view
vis = {"bands": ["B5", "B4", "B3"], "min": 0, "max": 5000, "gamma": 1.3}

# get a landsat image to visualize image = ee.Image("LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318") # define the visualization parameters to view vis = {"bands": ["B5", "B4", "B3"], "min": 0, "max": 5000, "gamma": 1.3}

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fig = plt.figure(figsize=(15, 10))

# use cartoee to get a map
ax = cartoee.get_map(image, vis_params=vis)

# pad the view for some visual appeal
cartoee.pad_view(ax)

# add the gridlines and specify that the xtick labels be rotated 45 degrees
cartoee.add_gridlines(ax, interval=0.5, xtick_rotation=45, linestyle=":")

# add the coastline
ax.coastlines(color="yellow")

plt.show()

fig = plt.figure(figsize=(15, 10)) # use cartoee to get a map ax = cartoee.get_map(image, vis_params=vis) # pad the view for some visual appeal cartoee.pad_view(ax) # add the gridlines and specify that the xtick labels be rotated 45 degrees cartoee.add_gridlines(ax, interval=0.5, xtick_rotation=45, linestyle=":") # add the coastline ax.coastlines(color="yellow") plt.show()

By default, if a region is not provided via the region keyword the whole extent of the image will be plotted as seen in the previous Landsat example. We can also zoom to a specific region of an image by defining the region to plot.

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fig = plt.figure(figsize=(15, 10))

# here is the bounding box of the map extent we want to use
# formatted a [E,S,W,N]
zoom_region = [-121.8025, 37.3458, -122.6265, 37.9178]

# plot the map over the region of interest
ax = cartoee.get_map(image, vis_params=vis, region=zoom_region)

# add the gridlines and specify that the xtick labels be rotated 45 degrees
cartoee.add_gridlines(ax, interval=0.15, xtick_rotation=45, linestyle=":")

# add coastline
ax.coastlines(color="yellow")

plt.show()

fig = plt.figure(figsize=(15, 10)) # here is the bounding box of the map extent we want to use # formatted a [E,S,W,N] zoom_region = [-121.8025, 37.3458, -122.6265, 37.9178] # plot the map over the region of interest ax = cartoee.get_map(image, vis_params=vis, region=zoom_region) # add the gridlines and specify that the xtick labels be rotated 45 degrees cartoee.add_gridlines(ax, interval=0.15, xtick_rotation=45, linestyle=":") # add coastline ax.coastlines(color="yellow") plt.show()

Adding north arrow and scale bar¶

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fig = plt.figure(figsize=(15, 10))

# here is the bounding box of the map extent we want to use
# formatted a [E,S,W,N]
zoom_region = [-121.8025, 37.3458, -122.6265, 37.9178]

# plot the map over the region of interest
ax = cartoee.get_map(image, vis_params=vis, region=zoom_region)

# add the gridlines and specify that the xtick labels be rotated 45 degrees
cartoee.add_gridlines(ax, interval=0.15, xtick_rotation=45, linestyle=":")

# add coastline
ax.coastlines(color="yellow")

# add north arrow
cartoee.add_north_arrow(
    ax, text="N", xy=(0.05, 0.25), text_color="white", arrow_color="white", fontsize=20
)

# add scale bar
cartoee.add_scale_bar_lite(
    ax, length=10, xy=(0.1, 0.05), fontsize=20, color="white", unit="km"
)

ax.set_title(label="Landsat False Color Composite (Band 5/4/3)", fontsize=15)

plt.show()

fig = plt.figure(figsize=(15, 10)) # here is the bounding box of the map extent we want to use # formatted a [E,S,W,N] zoom_region = [-121.8025, 37.3458, -122.6265, 37.9178] # plot the map over the region of interest ax = cartoee.get_map(image, vis_params=vis, region=zoom_region) # add the gridlines and specify that the xtick labels be rotated 45 degrees cartoee.add_gridlines(ax, interval=0.15, xtick_rotation=45, linestyle=":") # add coastline ax.coastlines(color="yellow") # add north arrow cartoee.add_north_arrow( ax, text="N", xy=(0.05, 0.25), text_color="white", arrow_color="white", fontsize=20 ) # add scale bar cartoee.add_scale_bar_lite( ax, length=10, xy=(0.1, 0.05), fontsize=20, color="white", unit="km" ) ax.set_title(label="Landsat False Color Composite (Band 5/4/3)", fontsize=15) plt.show()

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