cartoee

cartoee: Publication quality maps using Earth Engine - Published in JOSS (2019)

https://github.com/kmarkert/cartoee

Science Score: 93.0%

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Keywords

cartopy earth-engine plot visualization

Last synced: 6 months ago · JSON representation

Repository

Publication quality maps using Earth Engine and Cartopy

Basic Info

Host: GitHub
Owner: KMarkert
License: gpl-3.0
Language: Python
Default Branch: master
Homepage: https://cartoee.readthedocs.io/en/latest/index.html
Size: 13.1 MB

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Stars: 47
Watchers: 2
Forks: 7
Open Issues: 3
Releases: 2

Topics

cartopy earth-engine plot visualization

Created about 7 years ago · Last pushed about 7 years ago

Metadata Files

Readme Contributing License Authors

cartoee

Publication quality maps using Earth Engine and Cartopy! alt-text

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 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

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 LATITUDEFORMATTER, LONGITUDEFORMATTER

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.setmajorformatter(LONGITUDEFORMATTER) ax.yaxis.setmajorformatter(LATITUDEFORMATTER)

set tick labels

ax.setxticks([-180,-120,-60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) ax.setyticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree()) ``` alt-text

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 calcndvi(img): ndvi = img.normalizedDifference(['NadirReflectanceBand2','NadirReflectance_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

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

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

Owner

Name: Kel Markert
Login: KMarkert
Kind: user
Location: Huntsville, AL
Company: @google

Website: https://kmarkert.github.io
Twitter: KelMarkert
Repositories: 46
Profile: https://github.com/KMarkert

Cloud Geographer @google. PhD candidate @BYU-Hydroinformatics

JOSS Publication

cartoee: Publication quality maps using Earth Engine

Published

January 30, 2019

DOI

10.21105/joss.01207

Volume 4, Issue 33, Page 1207

Authors

Kel N. Markert

Earth System Science Center, The University of Alabama in Huntsville, SERVIR Science Coordination Office, NASA Marshall Space Flight Center

Editor

Lindsey Heagy

GitHub Events

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Total packages: 1
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- pypi 55 last-month

Total dependent packages: 0
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Total versions: 4
Total maintainers: 1

pypi.org: cartoee

Publication quality maps using Earth Engine and Cartopy!

Homepage: http://github.com/kmarkert/cartoee
Documentation: https://cartoee.readthedocs.io/
License: GNU GPL v3
Latest release: 0.0.5
published about 7 years ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 55 Last month

Rankings

Stargazers count: 10.1%

Dependent packages count: 10.1%

Forks count: 12.5%

Downloads: 21.5%

Average: 24.3%

Dependent repos count: 67.3%

Maintainers (1)

kmarkert

Last synced: 6 months ago

Dependencies

environment.yml pypi

earthengine-api ==0.1.145
google-api-core ==1.3.0
google-api-python-client ==1.7.3
google-auth ==1.5.0
google-auth-httplib2 ==0.0.3
ipykernel *
nbsphinx *
oauth2client ==4.1.2
sphinxcontrib-napoleon *

requirements.txt pypi

ipykernel *
nbsphinx *
sphinx >=1.4
sphinxcontrib-napoleon *

setup.py pypi

Cython *
cartopy ==0.16.0
earthengine-api *
geos *
google-api-python-client *
matplotlib *
oauth2client *
pyproj *

cartoee

Science Score: 93.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

cartoee

Installation

Working with cartoee

A simple case

get an earth engine image

plot the result using cartoee

set gridlines and spacing

set custom formatting for the tick labels

set tick labels

Doing more...

function to add NDVI band to imagery

MODIS Nadir BRDF-Adjusted Reflectance with NDVI band

get the cartopy map with EE results

get land mass feature collection

get seasonal averages and clip to land features

Owner

JOSS Publication

cartoee: Publication quality maps using Earth Engine

Authors

Editor

Tags

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Issues and Pull Requests

All Time

Past Year

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Issue Authors

Pull Request Authors

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Issue Labels

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Packages

pypi.org: cartoee

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Maintainers (1)

Dependencies