A13f 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:      A1.3 Built Environments
#  Checkpoint:   A13f
#  Author:       Erin Trochim
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Import Tsinghua FROM-GLC Year of Change to Impervious Surface
impervious = ee.Image('Tsinghua/FROM-GLC/GAIA/v10')

# Use the change year values found in the band.
# The change year values is described here:
# https:#developers.google.com/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10#bands
# Select only those areas which were impervious by 2000.
impervious2000 = impervious.gte(19)

# Select only those areas which were impervious by 2018.
impervious2018 = impervious.gte(1)

Map.setCenter(-98.688, 39.134, 5)

# Display the images.
Map.addLayer(
    impervious2000.selfMask(),
    {
        'min': 0,
        'max': 1,
        'palette': ['014352', '856F96']
    },
    'Impervious Surface 2000')

Map.addLayer(
    impervious2018.selfMask(),
    {
        'min': 0,
        'max': 1,
        'palette': ['014352', '1A492C']
    },
    'Impervious Surface 2018')

# Calculate the difference between impervious areas in 2000 and 2018.
imperviousDiff = impervious2018.subtract(impervious2000)

Map.addLayer(
    imperviousDiff.selfMask(),
    {
        'min': 0,
        'max': 1,
        'palette': ['014352', 'FFBF00']
    },
    'Impervious Surface Diff 2000-18')

# Import the Global Flood Database v1 (2000-2018).
gfd = ee.ImageCollection('GLOBAL_FLOOD_DB/MODIS_EVENTS/V1')

# Map all floods to generate the satellite-observed historical flood plain.
gfdFloodedSum = gfd.select('flooded').sum()

# Mask out areas of permanent water.
gfdFloodedSumNoWater = gfdFloodedSum.updateMask(gfd.select(
    'jrc_perm_water').sum().lt(1))

durationPalette = ['C3EFFE', '1341E8', '051CB0', '001133']

Map.addLayer(
    gfdFloodedSumNoWater.selfMask(),
    {
        'min': 0,
        'max': 10,
        'palette': durationPalette
    },
    'GFD Satellite Observed Flood Plain')

# Mask areas in the impervious difference image that are not in flood plains.
imperviousDiffFloods = imperviousDiff \
    .updateMask(gfdFloodedSumNoWater.gte(1))

# Which state has built the most area in the flood plains?
# Import FAO countries with first level administrative units.
countries = ee.FeatureCollection('FAO/GAUL/2015/level1')

# Filter to the United States.
unitedStates = countries.filter(ee.Filter.eq('ADM0_NAME',
    'United States of America'))

# Calculate the image area.
areaImage = imperviousDiffFloods.multiply(ee.Image.pixelArea())

# Sum the area image for each state.
unitedStatesImperviousDiffFlood = areaImage.reduceRegions({
        'collection': unitedStates,
        'reducer': ee.Reducer.sum(),
        'scale': 100,
    }) # Sort descending. \
    .sort('sum', False) \
    .limit(5)

# Print state statistics for change in impervious area in flood plain.
print('Impervious-flood change statistics for states in US',
    unitedStatesImperviousDiffFlood)

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A1.3 Built Environments # Checkpoint: A13f # Author: Erin Trochim # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Import Tsinghua FROM-GLC Year of Change to Impervious Surface impervious = ee.Image('Tsinghua/FROM-GLC/GAIA/v10') # Use the change year values found in the band. # The change year values is described here: # https:#developers.google.com/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10#bands # Select only those areas which were impervious by 2000. impervious2000 = impervious.gte(19) # Select only those areas which were impervious by 2018. impervious2018 = impervious.gte(1) Map.setCenter(-98.688, 39.134, 5) # Display the images. Map.addLayer( impervious2000.selfMask(), { 'min': 0, 'max': 1, 'palette': ['014352', '856F96'] }, 'Impervious Surface 2000') Map.addLayer( impervious2018.selfMask(), { 'min': 0, 'max': 1, 'palette': ['014352', '1A492C'] }, 'Impervious Surface 2018') # Calculate the difference between impervious areas in 2000 and 2018. imperviousDiff = impervious2018.subtract(impervious2000) Map.addLayer( imperviousDiff.selfMask(), { 'min': 0, 'max': 1, 'palette': ['014352', 'FFBF00'] }, 'Impervious Surface Diff 2000-18') # Import the Global Flood Database v1 (2000-2018). gfd = ee.ImageCollection('GLOBAL_FLOOD_DB/MODIS_EVENTS/V1') # Map all floods to generate the satellite-observed historical flood plain. gfdFloodedSum = gfd.select('flooded').sum() # Mask out areas of permanent water. gfdFloodedSumNoWater = gfdFloodedSum.updateMask(gfd.select( 'jrc_perm_water').sum().lt(1)) durationPalette = ['C3EFFE', '1341E8', '051CB0', '001133'] Map.addLayer( gfdFloodedSumNoWater.selfMask(), { 'min': 0, 'max': 10, 'palette': durationPalette }, 'GFD Satellite Observed Flood Plain') # Mask areas in the impervious difference image that are not in flood plains. imperviousDiffFloods = imperviousDiff \ .updateMask(gfdFloodedSumNoWater.gte(1)) # Which state has built the most area in the flood plains? # Import FAO countries with first level administrative units. countries = ee.FeatureCollection('FAO/GAUL/2015/level1') # Filter to the United States. unitedStates = countries.filter(ee.Filter.eq('ADM0_NAME', 'United States of America')) # Calculate the image area. areaImage = imperviousDiffFloods.multiply(ee.Image.pixelArea()) # Sum the area image for each state. unitedStatesImperviousDiffFlood = areaImage.reduceRegions({ 'collection': unitedStates, 'reducer': ee.Reducer.sum(), 'scale': 100, }) # Sort descending. \ .sort('sum', False) \ .limit(5) # Print state statistics for change in impervious area in flood plain. print('Impervious-flood change statistics for states in US', unitedStatesImperviousDiffFlood) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map