Image visualization

Image Visualization

This notebook was adapted from the Earth Engine JavaScript API Documentation.

The Get Started page illustrates how to visualize an image using Map.addLayer(). If you add a layer to the map without any additional parameters, by default the Code Editor assigns the first three bands to red, green and blue, respectively. The default stretch is based on the type of data in the band (e.g. floats are stretched in [0,1], 16-bit data are stretched to the full range of possible values), which may or may not be suitable. To achieve desirable visualization effects, you can provide visualization parameters to Map.addLayer().

Import libraries¶

In [1]:

# !pip install geemap

# !pip install geemap

In [2]:

import ee
import geemap

import ee import geemap

RGB composites¶

The following illustrates the use of parameters to style a Landsat 8 image as a false-color composite:

In [3]:

# Create a default map
Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Define the visualization parameters.
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, 8)  # San Francisco Bay
Map.addLayer(image, vizParams, 'false color composite')

# Display the map
Map

# Create a default map Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Define the visualization parameters. 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, 8) # San Francisco Bay Map.addLayer(image, vizParams, 'false color composite') # Display the map Map

Out[3]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Color palettes¶

To display a single band of an image in color, set the parameter with a color ramp represented by a list of CSS-style color strings. (See this reference for more information). The following example illustrates how to use colors from cyan (00FFFF) to blue (0000FF) to render a Normalized Difference Water Index (NDWI) image.

In this example, note that the min and max parameters indicate the range of pixel values to which the palette should be applied. Intermediate values are linearly stretched. Also note that the opt_show parameter is set to False. This results in the visibility of the layer being off when it is added to the map. It can always be turned on again using the Layer Manager in the upper right corner of the map. The result should look something like below.

In [4]:

# Create a default map
Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Create an NDWI image, define visualization parameters and display.
ndwi = image.normalizedDifference(['B3', 'B5'])
ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
Map.setCenter(-122.1899, 37.5010, 10)  # San Francisco Bay
Map.addLayer(ndwi, ndwiViz, 'NDWI', False)

# Display the map
Map

# Create a default map Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.setCenter(-122.1899, 37.5010, 10) # San Francisco Bay Map.addLayer(ndwi, ndwiViz, 'NDWI', False) # Display the map Map

Out[4]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Masking¶

You can use image.updateMask() to set the opacity of individual pixels based on where pixels in a mask image are non-zero. Pixels equal to zero in the mask are excluded from computations and the opacity is set to 0 for display. The following example uses an NDWI threshold (see the Relational Operations section for information on thresholds) to update the mask on the NDWI layer created previously:

In [5]:

# Create a default map
Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Create an NDWI image, define visualization parameters and display.
ndwi = image.normalizedDifference(['B3', 'B5'])
ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
Map.setCenter(-122.1899, 37.5010, 10)  # San Francisco Bay
Map.addLayer(ndwi, ndwiViz, 'NDWI', False)

# Mask the non-watery parts of the image, where NDWI < 0.4.
ndwiMasked = ndwi.updateMask(ndwi.gte(0.4))
Map.addLayer(ndwiMasked, ndwiViz, 'NDWI masked')

# Display the map
Map

# Create a default map Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.setCenter(-122.1899, 37.5010, 10) # San Francisco Bay Map.addLayer(ndwi, ndwiViz, 'NDWI', False) # Mask the non-watery parts of the image, where NDWI < 0.4. ndwiMasked = ndwi.updateMask(ndwi.gte(0.4)) Map.addLayer(ndwiMasked, ndwiViz, 'NDWI masked') # Display the map Map

Out[5]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Visualization images¶

Use the image.visualize() method to convert an image into an 8-bit RGB image for display or export. For example, to convert the false-color composite and NDWI to 3-band display images, use:

In [6]:

# Create a default map
Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Create an NDWI image, define visualization parameters and display.
ndwi = image.normalizedDifference(['B3', 'B5'])
ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
Map.setCenter(-122.1899, 37.5010, 10)  # San Francisco Bay
Map.addLayer(ndwi, ndwiViz, 'NDWI', False)

# Mask the non-watery parts of the image, where NDWI < 0.4.
ndwiMasked = ndwi.updateMask(ndwi.gte(0.4))
Map.addLayer(ndwiMasked, ndwiViz, 'NDWI masked', False)

# Create visualization layers.
imageRGB = image.visualize(**{'bands': ['B5', 'B4', 'B3'], 'max': 0.5})
ndwiRGB = ndwiMasked.visualize(
    **{'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
)

Map.addLayer(imageRGB, {}, 'imageRGB')
Map.addLayer(ndwiRGB, {}, 'ndwiRGB')

# Display the map
Map

# Create a default map Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.setCenter(-122.1899, 37.5010, 10) # San Francisco Bay Map.addLayer(ndwi, ndwiViz, 'NDWI', False) # Mask the non-watery parts of the image, where NDWI < 0.4. ndwiMasked = ndwi.updateMask(ndwi.gte(0.4)) Map.addLayer(ndwiMasked, ndwiViz, 'NDWI masked', False) # Create visualization layers. imageRGB = image.visualize(**{'bands': ['B5', 'B4', 'B3'], 'max': 0.5}) ndwiRGB = ndwiMasked.visualize( **{'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} ) Map.addLayer(imageRGB, {}, 'imageRGB') Map.addLayer(ndwiRGB, {}, 'ndwiRGB') # Display the map Map

Out[6]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Mosaicking¶

You can use masking and imageCollection.mosaic() (see the Mosaicking section for information on mosaicking) to achieve various cartographic effects. The mosaic() method renders layers in the output image according to their order in the input collection. The following example uses mosaic() to combine the masked NDWI and the false color composite and obtain a new visualization.

In this example, observe that a list of the two visualization images is provided to the ImageCollection constructor. The order of the list determines the order in which the images are rendered on the map. The result should look something like below.

In [7]:

# Create a default map
Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Create an NDWI image, define visualization parameters and display.
ndwi = image.normalizedDifference(['B3', 'B5'])
ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
Map.setCenter(-122.1899, 37.5010, 10)  # San Francisco Bay
Map.addLayer(ndwi, ndwiViz, 'NDWI', False)

# Mask the non-watery parts of the image, where NDWI < 0.4.
ndwiMasked = ndwi.updateMask(ndwi.gte(0.4))
Map.addLayer(ndwiMasked, ndwiViz, 'NDWI masked', False)

# Create visualization layers.
imageRGB = image.visualize(**{'bands': ['B5', 'B4', 'B3'], 'max': 0.5})
ndwiRGB = ndwiMasked.visualize(
    **{'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
)

Map.addLayer(imageRGB, {}, 'imageRGB', False)
Map.addLayer(ndwiRGB, {}, 'ndwiRGB', False)

# Mosaic the visualization layers and display (or export).
mosaic = ee.ImageCollection([imageRGB, ndwiRGB]).mosaic()
Map.addLayer(mosaic, {}, 'mosaic')

# Display the map
Map

# Create a default map Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.setCenter(-122.1899, 37.5010, 10) # San Francisco Bay Map.addLayer(ndwi, ndwiViz, 'NDWI', False) # Mask the non-watery parts of the image, where NDWI < 0.4. ndwiMasked = ndwi.updateMask(ndwi.gte(0.4)) Map.addLayer(ndwiMasked, ndwiViz, 'NDWI masked', False) # Create visualization layers. imageRGB = image.visualize(**{'bands': ['B5', 'B4', 'B3'], 'max': 0.5}) ndwiRGB = ndwiMasked.visualize( **{'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} ) Map.addLayer(imageRGB, {}, 'imageRGB', False) Map.addLayer(ndwiRGB, {}, 'ndwiRGB', False) # Mosaic the visualization layers and display (or export). mosaic = ee.ImageCollection([imageRGB, ndwiRGB]).mosaic() Map.addLayer(mosaic, {}, 'mosaic') # Display the map Map

Out[7]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Clipping¶

The image.clip() method is useful for achieving cartographic effects. The following example clips the mosaic shown above to an arbitrary buffer zone around the city of San Francisco.

Note that the coordinates are provided to the Geometry constructor and the buffer length is specified as 20,000 meters. Learn more about geometries on the Geometries page. The result, shown with the map in the background, should look something like below.

In [8]:

# Create a default map
Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Create an NDWI image, define visualization parameters and display.
ndwi = image.normalizedDifference(['B3', 'B5'])
ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
Map.setCenter(-122.4344, 37.7599, 10)  # San Francisco Bay
Map.addLayer(ndwi, ndwiViz, 'NDWI', False)

# Mask the non-watery parts of the image, where NDWI < 0.4.
ndwiMasked = ndwi.updateMask(ndwi.gte(0.4))
Map.addLayer(ndwiMasked, ndwiViz, 'NDWI masked', False)

# Create visualization layers.
imageRGB = image.visualize(**{'bands': ['B5', 'B4', 'B3'], 'max': 0.5})
ndwiRGB = ndwiMasked.visualize(
    **{'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
)

Map.addLayer(imageRGB, {}, 'imageRGB', False)
Map.addLayer(ndwiRGB, {}, 'ndwiRGB', False)

# Mosaic the visualization layers and display (or export).
mosaic = ee.ImageCollection([imageRGB, ndwiRGB]).mosaic()
Map.addLayer(mosaic, {}, 'mosaic', False)

# Create a circle by drawing a 20000 meter buffer around a point.
roi = ee.Geometry.Point([-122.4481, 37.7599]).buffer(20000)
clipped = mosaic.clip(roi)

# Display a clipped version of the mosaic.
Map.addLayer(clipped, {}, 'Clipped image')

# Display the map
Map

# Create a default map Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.setCenter(-122.4344, 37.7599, 10) # San Francisco Bay Map.addLayer(ndwi, ndwiViz, 'NDWI', False) # Mask the non-watery parts of the image, where NDWI < 0.4. ndwiMasked = ndwi.updateMask(ndwi.gte(0.4)) Map.addLayer(ndwiMasked, ndwiViz, 'NDWI masked', False) # Create visualization layers. imageRGB = image.visualize(**{'bands': ['B5', 'B4', 'B3'], 'max': 0.5}) ndwiRGB = ndwiMasked.visualize( **{'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} ) Map.addLayer(imageRGB, {}, 'imageRGB', False) Map.addLayer(ndwiRGB, {}, 'ndwiRGB', False) # Mosaic the visualization layers and display (or export). mosaic = ee.ImageCollection([imageRGB, ndwiRGB]).mosaic() Map.addLayer(mosaic, {}, 'mosaic', False) # Create a circle by drawing a 20000 meter buffer around a point. roi = ee.Geometry.Point([-122.4481, 37.7599]).buffer(20000) clipped = mosaic.clip(roi) # Display a clipped version of the mosaic. Map.addLayer(clipped, {}, 'Clipped image') # Display the map Map

Out[8]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Rendering categorical maps¶

Palettes are also useful for rendering discrete valued maps, for example a land cover map. In the case of multiple classes, use the palette to supply a different color for each class. (The image.remap() method may be useful in this context, to convert arbitrary labels to consecutive integers). The following example uses a palette to render land cover categories:

In [9]:

# Create a default map
Map = geemap.Map()

# Load 2012 MODIS land cover and select the IGBP classification.
cover = ee.Image('MODIS/051/MCD12Q1/2012_01_01').select('Land_Cover_Type_1')

# Define a palette for the 18 distinct land cover classes.
igbpPalette = [
    'aec3d4',  # water
    '152106',
    '225129',
    '369b47',
    '30eb5b',
    '387242',  # forest
    '6a2325',
    'c3aa69',
    'b76031',
    'd9903d',
    '91af40',  # shrub, grass
    '111149',  # wetlands
    'cdb33b',  # croplands
    'cc0013',  # urban
    '33280d',  # crop mosaic
    'd7cdcc',  # snow and ice
    'f7e084',  # barren
    '6f6f6f',  # tundra
]

# Specify the min and max labels and the color palette matching the labels.
Map.setCenter(-99.229, 40.413, 5)
Map.addLayer(
    cover, {'min': 0, 'max': 17, 'palette': igbpPalette}, 'IGBP classification'
)

Map

# Create a default map Map = geemap.Map() # Load 2012 MODIS land cover and select the IGBP classification. cover = ee.Image('MODIS/051/MCD12Q1/2012_01_01').select('Land_Cover_Type_1') # Define a palette for the 18 distinct land cover classes. igbpPalette = [ 'aec3d4', # water '152106', '225129', '369b47', '30eb5b', '387242', # forest '6a2325', 'c3aa69', 'b76031', 'd9903d', '91af40', # shrub, grass '111149', # wetlands 'cdb33b', # croplands 'cc0013', # urban '33280d', # crop mosaic 'd7cdcc', # snow and ice 'f7e084', # barren '6f6f6f', # tundra ] # Specify the min and max labels and the color palette matching the labels. Map.setCenter(-99.229, 40.413, 5) Map.addLayer( cover, {'min': 0, 'max': 17, 'palette': igbpPalette}, 'IGBP classification' ) Map

Out[9]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Thumbnail images¶

Use the ee.Image.getThumbURL() method to generate a PNG or JPEG thumbnail image for an ee.Image object. Printing the outcome of an expression ending with a call to getThumbURL() results in a URL being printed to the console. Visiting the URL sets Earth Engine servers to work on generating the requested thumbnail on-the-fly. The image is displayed in the browser when processing completes. It can be downloaded by selecting appropriate options from the image’s right-click context menu.

The getThumbURL() method shares parameters with Map.addLayer(), described in the visualization parameters table above. Additionally, it takes optional dimension, region, and crs arguments that control the spatial extent, size, and display projection of the thumbnail.

A single-band image will default to grayscale unless a palette argument is supplied. A multi-band image will default to RGB visualization of the first three bands, unless a bands argument is supplied. If only two bands are provided, the first band will map to red, the second to blue, and the green channel will be zero filled.

The following are a series of examples demonstrating various combinations of getThumbURL() parameter arguments. Visit the URLs printed to the console when you run this script to view the thumbnails.

In [10]:

# Create a default map
Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Define the visualization parameters.
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, 8)  # San Francisco Bay
Map.addLayer(image, vizParams, 'false color composite')

# Display the map
Map

# Create a default map Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Define the visualization parameters. 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, 8) # San Francisco Bay Map.addLayer(image, vizParams, 'false color composite') # Display the map Map

Out[10]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [11]:

geemap.get_image_thumbnail(image, "landsat.png", vizParams, dimensions=1000)

geemap.get_image_thumbnail(image, "landsat.png", vizParams, dimensions=1000)

In [12]:

geemap.show_image("landsat.png")

geemap.show_image("landsat.png")

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Last update: 2022-03-25