F60c Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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

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

Add Earth Engine Python script¶

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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      F6.0 Advanced Raster Visualization
#  Checkpoint:   F60c
#  Authors:      Gennadii Donchyts, Fedor Baart
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Advanced remapping using NLCD.
# Import NLCD.
nlcd = ee.ImageCollection("USGS/NLCD_RELEASES/2016_REL")

# Use Filter to select the 2016 dataset.
nlcd2016 = nlcd.filter(ee.Filter.eq("system:index", "2016")).first()

# Select the land cover band.
landcover = nlcd2016.select("landcover")

# Map the NLCD land cover.
Map.addLayer(landcover, None, "NLCD Landcover")


# Now suppose we want to change the color palette.
newPalette = [
    "466b9f",
    "d1def8",
    "dec5c5",
    "ab0000",
    "ab0000",
    "ab0000",
    "b3ac9f",
    "68ab5f",
    "1c5f2c",
    "b5c58f",
    "af963c",
    "ccb879",
    "dfdfc2",
    "d1d182",
    "a3cc51",
    "82ba9e",
    "dcd939",
    "ab6c28",
    "b8d9eb",
    "6c9fb8",
]

# Try mapping with the new color palette.
Map.addLayer(landcover, {"palette": newPalette}, "NLCD New Palette")

# Extract the class values and save them as a list.
values = ee.List(landcover.get("landcover_class_values"))

# Print the class values to console.
print("raw class values", values)

# Determine the maximum index value
maxIndex = values.size().subtract(1)

# Create a new index for the remap
indexes = ee.List.sequence(0, maxIndex)

# Print the updated class values to console.
print("updated class values", indexes)

# Remap NLCD and display it in the map.
colorized = landcover.remap(values, indexes).visualize(
    **{"min": 0, "max": maxIndex, "palette": newPalette}
)
Map.addLayer(colorized, {}, "NLCD Remapped Colors")

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F6.0 Advanced Raster Visualization # Checkpoint: F60c # Authors: Gennadii Donchyts, Fedor Baart # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Advanced remapping using NLCD. # Import NLCD. nlcd = ee.ImageCollection("USGS/NLCD_RELEASES/2016_REL") # Use Filter to select the 2016 dataset. nlcd2016 = nlcd.filter(ee.Filter.eq("system:index", "2016")).first() # Select the land cover band. landcover = nlcd2016.select("landcover") # Map the NLCD land cover. Map.addLayer(landcover, None, "NLCD Landcover") # Now suppose we want to change the color palette. newPalette = [ "466b9f", "d1def8", "dec5c5", "ab0000", "ab0000", "ab0000", "b3ac9f", "68ab5f", "1c5f2c", "b5c58f", "af963c", "ccb879", "dfdfc2", "d1d182", "a3cc51", "82ba9e", "dcd939", "ab6c28", "b8d9eb", "6c9fb8", ] # Try mapping with the new color palette. Map.addLayer(landcover, {"palette": newPalette}, "NLCD New Palette") # Extract the class values and save them as a list. values = ee.List(landcover.get("landcover_class_values")) # Print the class values to console. print("raw class values", values) # Determine the maximum index value maxIndex = values.size().subtract(1) # Create a new index for the remap indexes = ee.List.sequence(0, maxIndex) # Print the updated class values to console. print("updated class values", indexes) # Remap NLCD and display it in the map. colorized = landcover.remap(values, indexes).visualize( **{"min": 0, "max": maxIndex, "palette": newPalette} ) Map.addLayer(colorized, {}, "NLCD Remapped Colors") # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map