Google Earth Engine #

Contents

Google Earth Engine

This notebook is a collection of examples of how to use the Google Earth Engine Python API. The examples are based on the Google Earth Engine Python API Tutorial and the Google Earth Engine Python API Cookbook.

This notebook was developed for the geemap workshop at the GeoPython 2021 Conference.

Authors: Qiusheng Wu, Kel Markert

Link to this notebook: https://gishub.org/geopython

Recorded video: https://www.youtube.com/watch?v=wGjpjh9IQ5I

Google Earth Engine (GEE) is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. It enables scientists, researchers, and developers to analyze and visualize changes on the Earth’s surface. The geemap Python package provides GEE users with an intuitive interface to manipulate, analyze, and visualize geospatial big data interactively in a Jupyter-based environment. The topics will be covered in this workshop include:

Introducing geemap and the Earth Engine Python API
Creating interactive maps
Searching GEE data catalog
Displaying GEE datasets
Classifying images using machine learning algorithms
Computing statistics and exporting results
Producing publication-quality maps
Building and deploying interactive web apps, among others

This workshop is intended for scientific programmers, data scientists, geospatial analysts, and concerned citizens of Earth. The attendees are expected to have a basic understanding of Python and the Jupyter ecosystem. Familiarity with Earth science and geospatial datasets is useful but not required.

Useful links #

GeoPython 2021 Conference website
Google Earth Engine
geemap.org
Google Earth Engine and geemap Python Tutorials (55 videos with a total length of 15 hours)
Spatial Data Management with Google Earth Engine (19 videos with a total length of 9 hours)
Ask geemap questions on GitHub

Prerequisite #

A Google Earth Engine account. Sign up here if needed.
Miniconda or Anaconda

Set up a conda environment #

conda create -n geo python=3.8
conda activate geo
conda install geemap -c conda-forge
conda install jupyter_contrib_nbextensions -c conda-forge
jupyter contrib nbextension install --user

geemap basics #

Import libraries #

import os
import ee
import geemap

import pandas as pd

Create an interactive map #

Map = geemap.Map()
Map

Map = geemap.Map()
Map

Map = geemap.Map()
Map

Customize the default map #

You can specify the center(lat, lon) and zoom for the default map. The lite mode will only show the zoom in/out tool.

Map = geemap.Map(center=(40, -100), zoom=4, lite_mode=True)
Map

Add basemaps #

Map = geemap.Map()
Map.add_basemap('HYBRID')
Map

from geemap.basemaps import basemaps

Map.add_basemap(basemaps.OpenTopoMap)

For more info, see The interactive map displayed in Google Colab.. For more info, see Fig. 1.

See [Holdgraf et al., 2014, Holdgraf et al., 2016]

Change basemaps without coding #

https://i.imgur.com/PXURCSP.png — Fig. 1 The interactive map displayed in Google Colab.#

Map = geemap.Map()
Map

Add WMS and XYZ tile layers #

Examples: https://viewer.nationalmap.gov/services/

Map = geemap.Map()

url = 'https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}'
Map.add_tile_layer(url, name='Google Terrain', attribution='Google')
Map

naip_url = 'https://services.nationalmap.gov/arcgis/services/USGSNAIPImagery/ImageServer/WMSServer?'
Map.add_wms_layer(
    url=naip_url, layers='0', name='NAIP Imagery', format='image/png', shown=True
)

Use drawing tools #

Map = geemap.Map()
Map

# Map.user_roi.getInfo()

# Map.user_rois.getInfo()

Convert GEE JavaScript to Python #

https://developers.google.com/earth-engine/guides/image_visualization

js_snippet = """
// Load an image.
var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318');

// Define the visualization parameters.
var vizParams = {
  bands: ['B5', 'B4', 'B3'],
  min: 0,
  max: 0.5,
  gamma: [0.95, 1.1, 1]
};

// Center the map and display the image.
Map.setCenter(-122.1899, 37.5010, 10); // San Francisco Bay
Map.addLayer(image, vizParams, 'false color composite');

"""

geemap.js_snippet_to_py(
    js_snippet, add_new_cell=True, import_ee=True, import_geemap=True, show_map=True
)

You can also convert GEE JavaScript to Python without coding.

Map = geemap.Map()
Map

Earth Engine datasets #

Load Earth Engine datasets #

Map = geemap.Map()

# Add Earth Engine datasets
dem = ee.Image('USGS/SRTMGL1_003')
landcover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover')
landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')
states = ee.FeatureCollection("TIGER/2018/States")

# Set visualization parameters.
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

# Add Earth Engine layers to Map
Map.addLayer(dem, vis_params, 'SRTM DEM', True, 0.5)
Map.addLayer(landcover, {}, 'Land cover')
Map.addLayer(
    landsat7,
    {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200, 'gamma': 1.5},
    'Landsat 7',
)
Map.addLayer(states, {}, "US States")

Map

Search the Earth Engine Data Catalog #

Map = geemap.Map()
Map

dem = ee.Image('CGIAR/SRTM90_V4')
Map.addLayer(dem, {}, "CGIAR/SRTM90_V4")

vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

Map.addLayer(dem, vis_params, "DEM")

Use the datasets module #

from geemap.datasets import DATA

Map = geemap.Map()

dem = ee.Image(DATA.USGS_SRTMGL1_003)

vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

Map.addLayer(dem, vis_params, 'SRTM DEM')
Map

Use the Inspector tool #

Map = geemap.Map()

# Add Earth Engine datasets
dem = ee.Image('USGS/SRTMGL1_003')
landcover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover')
landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(
    ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']
)
states = ee.FeatureCollection("TIGER/2018/States")

# Set visualization parameters.
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

# Add Earth Engine layers to Map
Map.addLayer(dem, vis_params, 'SRTM DEM', True, 0.5)
Map.addLayer(landcover, {}, 'Land cover')
Map.addLayer(
    landsat7,
    {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200, 'gamma': 1.5},
    'Landsat 7',
)
Map.addLayer(states, {}, "US States")

Map

Data visualization #

Use the Plotting tool #

Map = geemap.Map()

landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(
    ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']
)

landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(landsat7, landsat_vis, "Landsat")

hyperion = ee.ImageCollection('EO1/HYPERION').filter(
    ee.Filter.date('2016-01-01', '2017-03-01')
)

hyperion_vis = {
    'min': 1000.0,
    'max': 14000.0,
    'gamma': 2.5,
}
Map.addLayer(hyperion, hyperion_vis, 'Hyperion')

Map

Change layer opacity #

Map = geemap.Map(center=(40, -100), zoom=4)

dem = ee.Image('USGS/SRTMGL1_003')
states = ee.FeatureCollection("TIGER/2018/States")

vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

Map.addLayer(dem, vis_params, 'SRTM DEM', True, 1)
Map.addLayer(states, {}, "US States", True)

Map

Visualize raster data #

Map = geemap.Map(center=(40, -100), zoom=4)

# Add Earth Engine dataset
dem = ee.Image('USGS/SRTMGL1_003')
landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(
    ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']
)

vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

Map.addLayer(dem, vis_params, 'SRTM DEM', True, 1)
Map.addLayer(
    landsat7,
    {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200, 'gamma': 2},
    'Landsat 7',
)
Map

Visualize vector data #

Map = geemap.Map()

states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, "US States")
Map

vis_params = {
    'color': '000000',
    'colorOpacity': 1,
    'pointSize': 3,
    'pointShape': 'circle',
    'width': 2,
    'lineType': 'solid',
    'fillColorOpacity': 0.66,
}

palette = ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']

Map.add_styled_vector(
    states, column="NAME", palette=palette, layer_name="Styled vector", **vis_params
)

Add a legend #

legends = geemap.builtin_legends
for legend in legends:
    print(legend)

Map = geemap.Map()
Map.add_basemap('HYBRID')
landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover')
Map.addLayer(landcover, {}, 'NLCD Land Cover')
Map.add_legend(builtin_legend='NLCD')
Map

Map = geemap.Map()

legend_dict = {
    '11 Open Water': '466b9f',
    '12 Perennial Ice/Snow': 'd1def8',
    '21 Developed, Open Space': 'dec5c5',
    '22 Developed, Low Intensity': 'd99282',
    '23 Developed, Medium Intensity': 'eb0000',
    '24 Developed High Intensity': 'ab0000',
    '31 Barren Land (Rock/Sand/Clay)': 'b3ac9f',
    '41 Deciduous Forest': '68ab5f',
    '42 Evergreen Forest': '1c5f2c',
    '43 Mixed Forest': 'b5c58f',
    '51 Dwarf Scrub': 'af963c',
    '52 Shrub/Scrub': 'ccb879',
    '71 Grassland/Herbaceous': 'dfdfc2',
    '72 Sedge/Herbaceous': 'd1d182',
    '73 Lichens': 'a3cc51',
    '74 Moss': '82ba9e',
    '81 Pasture/Hay': 'dcd939',
    '82 Cultivated Crops': 'ab6c28',
    '90 Woody Wetlands': 'b8d9eb',
    '95 Emergent Herbaceous Wetlands': '6c9fb8',
}

landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover')
Map.addLayer(landcover, {}, 'NLCD Land Cover')

Map.add_legend(legend_title="NLCD Land Cover Classification", legend_dict=legend_dict)
Map

Add a colorbar #

Map = geemap.Map()

dem = ee.Image('USGS/SRTMGL1_003')
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

Map.addLayer(dem, vis_params, 'SRTM DEM')

colors = vis_params['palette']
vmin = vis_params['min']
vmax = vis_params['max']

Map.add_colorbar(vis_params, label="Elevation (m)", layer_name="SRTM DEM")
Map

Map.add_colorbar(
    vis_params, label="Elevation (m)", layer_name="SRTM DEM", orientation="vertical"
)

Map.add_colorbar(
    vis_params,
    label="Elevation (m)",
    layer_name="SRTM DEM",
    orientation="vertical",
    transparent_bg=True,
)

Map.add_colorbar(
    vis_params,
    label="Elevation (m)",
    layer_name="SRTM DEM",
    orientation="vertical",
    transparent_bg=True,
    discrete=True,
)

Create a split-panel map #

Map = geemap.Map()
Map.split_map(left_layer='HYBRID', right_layer='TERRAIN')
Map

Map = geemap.Map()
Map.split_map(
    left_layer='NLCD 2016 CONUS Land Cover', right_layer='NLCD 2001 CONUS Land Cover'
)
Map

nlcd_2001 = ee.Image('USGS/NLCD/NLCD2001').select('landcover')
nlcd_2016 = ee.Image('USGS/NLCD/NLCD2016').select('landcover')

left_layer = geemap.ee_tile_layer(nlcd_2001, {}, 'NLCD 2001')
right_layer = geemap.ee_tile_layer(nlcd_2016, {}, 'NLCD 2016')

Map = geemap.Map()
Map.split_map(left_layer, right_layer)
Map

Create linked maps #

image = (
    ee.ImageCollection('COPERNICUS/S2')
    .filterDate('2018-09-01', '2018-09-30')
    .map(lambda img: img.divide(10000))
    .median()
)

vis_params = [
    {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 0.3, 'gamma': 1.3},
    {'bands': ['B8', 'B11', 'B4'], 'min': 0, 'max': 0.3, 'gamma': 1.3},
    {'bands': ['B8', 'B4', 'B3'], 'min': 0, 'max': 0.3, 'gamma': 1.3},
    {'bands': ['B12', 'B12', 'B4'], 'min': 0, 'max': 0.3, 'gamma': 1.3},
]

labels = [
    'Natural Color (B4/B3/B2)',
    'Land/Water (B8/B11/B4)',
    'Color Infrared (B8/B4/B3)',
    'Vegetation (B12/B11/B4)',
]

geemap.linked_maps(
    rows=2,
    cols=2,
    height="400px",
    center=[38.4151, 21.2712],
    zoom=12,
    ee_objects=[image],
    vis_params=vis_params,
    labels=labels,
    label_position="topright",
)

Create timelapse animations #

geemap.show_youtube('https://youtu.be/mA21Us_3m28')

Create time-series composites #

geemap.show_youtube('https://youtu.be/kEltQkNia6o')

Data analysis #

Descriptive statistics #

Map = geemap.Map()

centroid = ee.Geometry.Point([-122.4439, 37.7538])

image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filterBounds(centroid).first()

vis = {'min': 0, 'max': 3000, 'bands': ['B5', 'B4', 'B3']}

Map.centerObject(centroid, 8)
Map.addLayer(image, vis, "Landsat-8")
Map

image.propertyNames().getInfo()

image.get('CLOUD_COVER').getInfo()

props = geemap.image_props(image)
props.getInfo()

stats = geemap.image_stats(image, scale=90)
stats.getInfo()

Zonal statistics #

Map = geemap.Map()

# Add Earth Engine dataset
dem = ee.Image('USGS/SRTMGL1_003')

# Set visualization parameters.
dem_vis = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

# Add Earth Engine DEM to map
Map.addLayer(dem, dem_vis, 'SRTM DEM')

# Add Landsat data to map
landsat = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')

landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(landsat, landsat_vis, "LE7_TOA_5YEAR/1999_2003")

states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, 'US States')
Map

out_dir = os.path.expanduser('~/Downloads')
out_dem_stats = os.path.join(out_dir, 'dem_stats.csv')

if not os.path.exists(out_dir):
    os.makedirs(out_dir)

# Allowed output formats: csv, shp, json, kml, kmz
# Allowed statistics type: MEAN, MAXIMUM, MINIMUM, MEDIAN, STD, MIN_MAX, VARIANCE, SUM
geemap.zonal_statistics(dem, states, out_dem_stats, statistics_type='MEAN', scale=1000)

out_landsat_stats = os.path.join(out_dir, 'landsat_stats.csv')
geemap.zonal_statistics(
    landsat, states, out_landsat_stats, statistics_type='SUM', scale=1000
)

Zonal statistics by group #

Map = geemap.Map()

dataset = ee.Image('USGS/NLCD/NLCD2016')
landcover = ee.Image(dataset.select('landcover'))
Map.addLayer(landcover, {}, 'NLCD 2016')

states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, 'US States')
Map.add_legend(builtin_legend='NLCD')
Map

out_dir = os.path.expanduser('~/Downloads')
nlcd_stats = os.path.join(out_dir, 'nlcd_stats.csv')

if not os.path.exists(out_dir):
    os.makedirs(out_dir)

# statistics_type can be either 'SUM' or 'PERCENTAGE'
# denominator can be used to convert square meters to other areal units, such as square kilimeters
geemap.zonal_statistics_by_group(
    landcover,
    states,
    nlcd_stats,
    statistics_type='SUM',
    denominator=1000000,
    decimal_places=2,
)

Unsupervised classification #

Source: https://developers.google.com/earth-engine/guides/clustering

The ee.Clusterer package handles unsupervised classification (or clustering) in Earth Engine. These algorithms are currently based on the algorithms with the same name in Weka. More details about each Clusterer are available in the reference docs in the Code Editor.

Clusterers are used in the same manner as classifiers in Earth Engine. The general workflow for clustering is:

Assemble features with numeric properties in which to find clusters.
Instantiate a clusterer. Set its parameters if necessary.
Train the clusterer using the training data.
Apply the clusterer to an image or feature collection.
Label the clusters.

The training data is a FeatureCollection with properties that will be input to the clusterer. Unlike classifiers, there is no input class value for an Clusterer. Like classifiers, the data for the train and apply steps are expected to have the same number of values. When a trained clusterer is applied to an image or table, it assigns an integer cluster ID to each pixel or feature.

Here is a simple example of building and using an ee.Clusterer:

Add data to the map

Map = geemap.Map()

point = ee.Geometry.Point([-87.7719, 41.8799])

image = (
    ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
    .filterBounds(point)
    .filterDate('2019-01-01', '2019-12-31')
    .sort('CLOUD_COVER')
    .first()
    .select('B[1-7]')
)

vis_params = {'min': 0, 'max': 3000, 'bands': ['B5', 'B4', 'B3']}

Map.centerObject(point, 8)
Map.addLayer(image, vis_params, "Landsat-8")

Map

Make training dataset

There are several ways you can create a region for generating the training dataset.

Draw a shape (e.g., rectangle) on the map and the use region = Map.user_roi
Define a geometry, such as region = ee.Geometry.Rectangle([-122.6003, 37.4831, -121.8036, 37.8288])
Create a buffer zone around a point, such as region = ee.Geometry.Point([-122.4439, 37.7538]).buffer(10000)
If you don’t define a region, it will use the image footprint by default

training = image.sample(
    **{
        #     'region': region,
        'scale': 30,
        'numPixels': 5000,
        'seed': 0,
        'geometries': True,  # Set this to False to ignore geometries
    }
)

Map.addLayer(training, {}, 'training', False)

Train the clusterer

# Instantiate the clusterer and train it.
n_clusters = 5
clusterer = ee.Clusterer.wekaKMeans(n_clusters).train(training)

Classify the image

# Cluster the input using the trained clusterer.
result = image.cluster(clusterer)

# # Display the clusters with random colors.
Map.addLayer(result.randomVisualizer(), {}, 'clusters')
Map

Label the clusters

legend_keys = ['One', 'Two', 'Three', 'Four', 'ect']
legend_colors = ['#8DD3C7', '#FFFFB3', '#BEBADA', '#FB8072', '#80B1D3']

# Reclassify the map
result = result.remap([0, 1, 2, 3, 4], [1, 2, 3, 4, 5])

Map.addLayer(
    result, {'min': 1, 'max': 5, 'palette': legend_colors}, 'Labelled clusters'
)
Map.add_legend(
    legend_keys=legend_keys, legend_colors=legend_colors, position='bottomright'
)

Visualize the result

print('Change layer opacity:')
cluster_layer = Map.layers[-1]
cluster_layer.interact(opacity=(0, 1, 0.1))

Map

Export the result

out_dir = os.path.expanduser('~/Downloads')
out_file = os.path.join(out_dir, 'cluster.tif')
geemap.ee_export_image(result, filename=out_file, scale=90)

# geemap.ee_export_image_to_drive(result, description='clusters', folder='export', scale=90)

Supervised classification #

Source: https://developers.google.com/earth-engine/guides/classification

The Classifier package handles supervised classification by traditional ML algorithms running in Earth Engine. These classifiers include CART, RandomForest, NaiveBayes and SVM. The general workflow for classification is:

Collect training data. Assemble features which have a property that stores the known class label and properties storing numeric values for the predictors.
Instantiate a classifier. Set its parameters if necessary.
Train the classifier using the training data.
Classify an image or feature collection.
Estimate classification error with independent validation data.

The training data is a FeatureCollection with a property storing the class label and properties storing predictor variables. Class labels should be consecutive, integers starting from 0. If necessary, use remap() to convert class values to consecutive integers. The predictors should be numeric.

Add data to the map

Map = geemap.Map()

point = ee.Geometry.Point([-122.4439, 37.7538])

image = (
    ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
    .filterBounds(point)
    .filterDate('2016-01-01', '2016-12-31')
    .sort('CLOUD_COVER')
    .first()
    .select('B[1-7]')
)

vis_params = {'min': 0, 'max': 3000, 'bands': ['B5', 'B4', 'B3']}

Map.centerObject(point, 8)
Map.addLayer(image, vis_params, "Landsat-8")

Map

Make training dataset

There are several ways you can create a region for generating the training dataset.

Draw a shape (e.g., rectangle) on the map and the use region = Map.user_roi
Define a geometry, such as region = ee.Geometry.Rectangle([-122.6003, 37.4831, -121.8036, 37.8288])
Create a buffer zone around a point, such as region = ee.Geometry.Point([-122.4439, 37.7538]).buffer(10000)
If you don’t define a region, it will use the image footprint by default

# region = Map.user_roi
# region = ee.Geometry.Rectangle([-122.6003, 37.4831, -121.8036, 37.8288])
# region = ee.Geometry.Point([-122.4439, 37.7538]).buffer(10000)

In this example, we are going to use the USGS National Land Cover Database (NLCD) to create label dataset for training

nlcd = ee.Image('USGS/NLCD/NLCD2016').select('landcover').clip(image.geometry())
Map.addLayer(nlcd, {}, 'NLCD')
Map

# Make the training dataset.
points = nlcd.sample(
    **{
        'region': image.geometry(),
        'scale': 30,
        'numPixels': 5000,
        'seed': 0,
        'geometries': True,  # Set this to False to ignore geometries
    }
)

Map.addLayer(points, {}, 'training', False)

Train the classifier

# Use these bands for prediction.
bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']


# This property of the table stores the land cover labels.
label = 'landcover'

# Overlay the points on the imagery to get training.
training = image.select(bands).sampleRegions(
    **{'collection': points, 'properties': [label], 'scale': 30}
)

# Train a CART classifier with default parameters.
trained = ee.Classifier.smileCart().train(training, label, bands)

Classify the image

# Classify the image with the same bands used for training.
result = image.select(bands).classify(trained)

# # Display the clusters with random colors.
Map.addLayer(result.randomVisualizer(), {}, 'classfied')
Map

Render categorical map

To render a categorical map, we can set two image properties: landcover_class_values and landcover_class_palette. We can use the same style as the NLCD so that it is easy to compare the two maps.

class_values = nlcd.get('landcover_class_values').getInfo()
class_palette = nlcd.get('landcover_class_palette').getInfo()

landcover = result.set('classification_class_values', class_values)
landcover = landcover.set('classification_class_palette', class_palette)

Map.addLayer(landcover, {}, 'Land cover')
Map.add_legend(builtin_legend='NLCD')
Map

Visualize the result

print('Change layer opacity:')
cluster_layer = Map.layers[-1]
cluster_layer.interact(opacity=(0, 1, 0.1))

Export the result

out_dir = os.path.expanduser('~/Downloads')
out_file = os.path.join(out_dir, 'landcover.tif')

geemap.ee_export_image(landcover, filename=out_file, scale=900)

# geemap.ee_export_image_to_drive(landcover, description='landcover', folder='export', scale=900)

Training sample creation #

geemap.show_youtube('https://youtu.be/VWh5PxXPZw0')

Map = geemap.Map()
Map

WhiteboxTools #

import whiteboxgui

whiteboxgui.show()

whiteboxgui.show(tree=True)

Map = geemap.Map()
Map

Map making #

Plot a single band image #

import matplotlib.pyplot as plt
from geemap import cartoee

geemap.ee_initialize()

srtm = ee.Image("CGIAR/SRTM90_V4")
region = [-180, -60, 180, 85]  # define bounding box to request data
vis = {'min': 0, 'max': 3000}  # define visualization parameters for image

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()

Plot an RGB image #

# 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}

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

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

Add map elements #

from matplotlib.lines import Line2D

# 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}

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

# here is the bounding box of the map extent we want to use
# formatted a [W,S,E,N]
zoom_region = [-122.6265, 37.3458, -121.8025, 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=0, linestyle=":")

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

# 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)

# add legend
legend_elements = [
    Line2D([], [], color='#00ffff', lw=2, label='Coastline'),
    Line2D(
        [],
        [],
        marker='o',
        color='#A8321D',
        label='City',
        markerfacecolor='#A8321D',
        markersize=10,
        ls='',
    ),
]

cartoee.add_legend(ax, legend_elements, loc='lower right')

plt.show()

Plot multiple layers #

Map = geemap.Map()

image = (
    ee.ImageCollection('MODIS/MCD43A4_006_NDVI')
    .filter(ee.Filter.date('2018-04-01', '2018-05-01'))
    .select("NDVI")
    .first()
)

vis_params = {
    'min': 0.0,
    'max': 1.0,
    'palette': [
        'FFFFFF',
        'CE7E45',
        'DF923D',
        'F1B555',
        'FCD163',
        '99B718',
        '74A901',
        '66A000',
        '529400',
        '3E8601',
        '207401',
        '056201',
        '004C00',
        '023B01',
        '012E01',
        '011D01',
        '011301',
    ],
}
Map.setCenter(-7.03125, 31.0529339857, 2)
Map.addLayer(image, vis_params, 'MODIS NDVI')

countries = geemap.shp_to_ee("../data/countries.shp")
style = {"color": "00000088", "width": 1, "fillColor": "00000000"}
Map.addLayer(countries.style(**style), {}, "Countries")

ndvi = image.visualize(**vis_params)
blend = ndvi.blend(countries.style(**style))

Map.addLayer(blend, {}, "Blend")

Map

# specify region to focus on
bbox = [-180, -88, 180, 88]

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

# plot the result with cartoee using a PlateCarre projection (default)
ax = cartoee.get_map(blend, region=bbox)
cb = cartoee.add_colorbar(ax, vis_params=vis_params, loc='right')

ax.set_title(label='MODIS NDVI', fontsize=15)

# ax.coastlines()
plt.show()

import cartopy.crs as ccrs

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

projection = ccrs.EqualEarth(central_longitude=-180)

# plot the result with cartoee using a PlateCarre projection (default)
ax = cartoee.get_map(blend, region=bbox, proj=projection)
cb = cartoee.add_colorbar(ax, vis_params=vis_params, loc='right')

ax.set_title(label='MODIS NDVI', fontsize=15)

# ax.coastlines()
plt.show()

Use custom projections #

import cartopy.crs as ccrs

# get an earth engine image of ocean data for Jan-Mar 2018
ocean = (
    ee.ImageCollection('NASA/OCEANDATA/MODIS-Terra/L3SMI')
    .filter(ee.Filter.date('2018-01-01', '2018-03-01'))
    .median()
    .select(["sst"], ["SST"])
)

# set parameters for plotting
# will plot the Sea Surface Temp with specific range and colormap
visualization = {'bands': "SST", 'min': -2, 'max': 30}
# specify region to focus on
bbox = [-180, -88, 180, 88]

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

# plot the result with cartoee using a PlateCarre projection (default)
ax = cartoee.get_map(ocean, cmap='plasma', vis_params=visualization, region=bbox)
cb = cartoee.add_colorbar(ax, vis_params=visualization, loc='right', cmap='plasma')

ax.set_title(label='Sea Surface Temperature', fontsize=15)

ax.coastlines()
plt.show()

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

# create a new Mollweide projection centered on the Pacific
projection = ccrs.Mollweide(central_longitude=-180)

# plot the result with cartoee using the Mollweide projection
ax = cartoee.get_map(
    ocean, vis_params=visualization, region=bbox, cmap='plasma', proj=projection
)
cb = cartoee.add_colorbar(
    ax, vis_params=visualization, loc='bottom', cmap='plasma', orientation='horizontal'
)

ax.set_title("Mollweide projection")

ax.coastlines()
plt.show()

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

# create a new Robinson projection centered on the Pacific
projection = ccrs.Robinson(central_longitude=-180)

# plot the result with cartoee using the Goode homolosine projection
ax = cartoee.get_map(
    ocean, vis_params=visualization, region=bbox, cmap='plasma', proj=projection
)
cb = cartoee.add_colorbar(
    ax, vis_params=visualization, loc='bottom', cmap='plasma', orientation='horizontal'
)

ax.set_title("Robinson projection")

ax.coastlines()
plt.show()

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

# create a new equal Earth projection focused on the Pacific
projection = ccrs.EqualEarth(central_longitude=-180)

# plot the result with cartoee using the orographic projection
ax = cartoee.get_map(
    ocean, vis_params=visualization, region=bbox, cmap='plasma', proj=projection
)
cb = cartoee.add_colorbar(
    ax, vis_params=visualization, loc='right', cmap='plasma', orientation='vertical'
)

ax.set_title("Equal Earth projection")

ax.coastlines()
plt.show()

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

# create a new orographic projection focused on the Pacific
projection = ccrs.Orthographic(-130, -10)

# plot the result with cartoee using the orographic projection
ax = cartoee.get_map(
    ocean, vis_params=visualization, region=bbox, cmap='plasma', proj=projection
)
cb = cartoee.add_colorbar(
    ax, vis_params=visualization, loc='right', cmap='plasma', orientation='vertical'
)

ax.set_title("Orographic projection")

ax.coastlines()
plt.show()

Create timelapse animations #

Map = geemap.Map()

lon = -115.1585
lat = 36.1500
start_year = 1984
end_year = 2000

point = ee.Geometry.Point(lon, lat)
years = ee.List.sequence(start_year, end_year)


def get_best_image(year):

    start_date = ee.Date.fromYMD(year, 1, 1)
    end_date = ee.Date.fromYMD(year, 12, 31)
    image = (
        ee.ImageCollection("LANDSAT/LT05/C01/T1_SR")
        .filterBounds(point)
        .filterDate(start_date, end_date)
        .sort("CLOUD_COVER")
        .first()
    )
    return ee.Image(image)


collection = ee.ImageCollection(years.map(get_best_image))

vis_params = {"bands": ['B4', 'B3', 'B2'], "min": 0, "max": 5000}

image = ee.Image(collection.first())
Map.addLayer(image, vis_params, 'First image')
Map.setCenter(lon, lat, 8)
Map

w = 0.4
h = 0.3

region = [lon - w, lat - h, lon + w, lat + h]

fig = plt.figure(figsize=(10, 8))

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

# add gridlines to the map at a specified interval
cartoee.add_gridlines(ax, interval=[0.2, 0.2], linestyle=":")

# add north arrow
north_arrow_dict = {
    "text": "N",
    "xy": (0.1, 0.3),
    "arrow_length": 0.15,
    "text_color": "white",
    "arrow_color": "white",
    "fontsize": 20,
    "width": 5,
    "headwidth": 15,
    "ha": "center",
    "va": "center",
}
cartoee.add_north_arrow(ax, **north_arrow_dict)

# add scale bar
scale_bar_dict = {
    "length": 10,
    "xy": (0.1, 0.05),
    "linewidth": 3,
    "fontsize": 20,
    "color": "white",
    "unit": "km",
    "ha": "center",
    "va": "bottom",
}
cartoee.add_scale_bar_lite(ax, **scale_bar_dict)

ax.set_title(label='Las Vegas, NV', fontsize=15)

plt.show()

cartoee.get_image_collection_gif(
    ee_ic=collection,
    out_dir=os.path.expanduser("~/Downloads/timelapse"),
    out_gif="animation.gif",
    vis_params=vis_params,
    region=region,
    fps=5,
    mp4=True,
    grid_interval=(0.2, 0.2),
    plot_title="Las Vegas, NV",
    date_format='YYYY-MM-dd',
    fig_size=(10, 8),
    dpi_plot=100,
    file_format="png",
    north_arrow_dict=north_arrow_dict,
    scale_bar_dict=scale_bar_dict,
    verbose=True,
)

Data export #

Export ee.Image #

Map = geemap.Map()

image = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')

landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(image, landsat_vis, "LE7_TOA_5YEAR/1999_2003", True, 1)

Map

# Draw any shapes on the map using the Drawing tools before executing this code block
roi = Map.user_roi

if roi is None:
    roi = ee.Geometry.Polygon(
        [
            [
                [-115.413031, 35.889467],
                [-115.413031, 36.543157],
                [-114.034328, 36.543157],
                [-114.034328, 35.889467],
                [-115.413031, 35.889467],
            ]
        ]
    )

# Set output directory
out_dir = os.path.expanduser('~/Downloads')

if not os.path.exists(out_dir):
    os.makedirs(out_dir)

filename = os.path.join(out_dir, 'landsat.tif')

Exporting all bands as one single image

image = image.clip(roi).unmask()
geemap.ee_export_image(
    image, filename=filename, scale=90, region=roi, file_per_band=False
)

Exporting each band as one image

geemap.ee_export_image(
    image, filename=filename, scale=90, region=roi, file_per_band=True
)

Export an image to Google Drive¶

# geemap.ee_export_image_to_drive(image, description='landsat', folder='export', region=roi, scale=30)

Export ee.ImageCollection #

loc = ee.Geometry.Point(-99.2222, 46.7816)
collection = (
    ee.ImageCollection('USDA/NAIP/DOQQ')
    .filterBounds(loc)
    .filterDate('2008-01-01', '2020-01-01')
    .filter(ee.Filter.listContains("system:band_names", "N"))
)

collection.aggregate_array('system:index').getInfo()

geemap.ee_export_image_collection(collection, out_dir=out_dir)

# geemap.ee_export_image_collection_to_drive(collection, folder='export', scale=10)

Extract pixels as a numpy array #

import matplotlib.pyplot as plt

img = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_038029_20180810').select(['B4', 'B5', 'B6'])

aoi = ee.Geometry.Polygon(
    [[[-110.8, 44.7], [-110.8, 44.6], [-110.6, 44.6], [-110.6, 44.7]]], None, False
)

rgb_img = geemap.ee_to_numpy(img, region=aoi)
print(rgb_img.shape)

rgb_img_test = (255 * ((rgb_img[:, :, 0:3] - 100) / 3500)).astype('uint8')
plt.imshow(rgb_img_test)
plt.show()

Export pixel values to points #

Map = geemap.Map()

# Add Earth Engine dataset
dem = ee.Image('USGS/SRTMGL1_003')
landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')

# Set visualization parameters.
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

# Add Earth Engine layers to Map
Map.addLayer(
    landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200}, 'Landsat 7'
)
Map.addLayer(dem, vis_params, 'SRTM DEM', True, 1)
Map

Download sample data

work_dir = os.path.expanduser('~/Downloads')
in_shp = os.path.join(work_dir, 'us_cities.shp')
if not os.path.exists(in_shp):
    data_url = 'https://github.com/giswqs/data/raw/main/us/us_cities.zip'
    geemap.download_from_url(data_url, out_dir=work_dir)

in_fc = geemap.shp_to_ee(in_shp)
Map.addLayer(in_fc, {}, 'Cities')

Export pixel values as a shapefile

out_shp = os.path.join(work_dir, 'dem.shp')
geemap.extract_values_to_points(in_fc, dem, out_shp)

Export pixel values as a csv

out_csv = os.path.join(work_dir, 'landsat.csv')
geemap.extract_values_to_points(in_fc, landsat7, out_csv)

Export ee.FeatureCollection #

Map = geemap.Map()

fc = ee.FeatureCollection('users/giswqs/public/countries')
Map.addLayer(fc, {}, "Countries")
Map

out_dir = os.path.expanduser('~/Downloads')
out_shp = os.path.join(out_dir, 'countries.shp')

geemap.ee_to_shp(fc, filename=out_shp)

out_csv = os.path.join(out_dir, 'countries.csv')
geemap.ee_export_vector(fc, filename=out_csv)

out_kml = os.path.join(out_dir, 'countries.kml')
geemap.ee_export_vector(fc, filename=out_kml)

# geemap.ee_export_vector_to_drive(fc, description="countries", folder="export", file_format="shp")

Web apps #

Deploy web apps using ngrok #

Steps to deploy an Earth Engine App:

Install ngrok by following the instruction
Download the notebook 71_timelapse.ipynb
Run this from the command line: voila --no-browser 71_timelapse.ipynb
Run this from the command line: ngrok http 8866
Copy the link from the ngrok terminal window. The links looks like the following: https://randomstring.ngrok.io
Share the link with anyone.

Optional steps:

To show code cells from you app, run this from the command line: voila --no-browser --strip_sources=False 71_timelapse.ipynb
To protect your app with a password, run this: ngrok http -auth="username:password" 8866
To run python simple http server in the directory, run this:sudo python -m http.server 80

geemap.show_youtube("https://youtu.be/eRDZBVJcNCk")

Deploy web apps using Heroku #

Steps to deploy an Earth Engine App:

Sign up for a free heroku account.
Follow the instructions to install Git and Heroku Command Line Interface (CLI).
Authenticate heroku using the heroku login command.
Clone this repository: https://github.com/giswqs/geemap-heroku
Create your own Earth Engine notebook and put it under the notebooks directory.
Add Python dependencies in the requirements.txt file if needed.
Edit the Procfile file by replacing notebooks/geemap.ipynb with the path to your own notebook.
Commit changes to the repository by using git add . && git commit -am "message".
Create a heroku app: heroku create
Run the config_vars.py script to extract Earth Engine token from your computer and set it as an environment variable on heroku: python config_vars.py
Deploy your code to heroku: git push heroku master
Open your heroku app: heroku open

Optional steps:

To specify a name for your app, use heroku apps:create example
To preview your app locally, use heroku local web
To hide code cells from your app, you can edit the Procfile file and set --strip_sources=True
To periodically check for idle kernels, you can edit the Procfile file and set --MappingKernelManager.cull_interval=60 --MappingKernelManager.cull_idle_timeout=120
To view information about your running app, use heroku logs --tail
To set an environment variable on heroku, use heroku config:set NAME=VALUE
To view environment variables for your app, use heroku config

geemap.show_youtube("https://youtu.be/nsIjfD83ggA")

Geospatial Data Science

Google Earth Engine

Contents