A17a Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

In [ ]:

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

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

Add Earth Engine Python script¶

In [ ]:

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

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      A1.7 Humanitarian Applications
#  Checkpoint:   A17a
#  Authors:      Jamon Van Den Hoek, Hannah K. Friedrich
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

###########################
#/ Section One: Seeing refugee settlements from above
###########################
Map.setOptions('SATELLITE')

# Load UNHCR settlement boundary for Pagirinya Refugee Settlement.
pagirinya = ee.Feature(ee.FeatureCollection(
    'projects/gee-book/assets/A1-7/pagirinya_settlement_boundary'
).first())

Map.addLayer(pagirinya, {}, 'Pagirinya Refugee Settlement')
Map.centerObject(pagirinya, 14)

# Create buffered settlement boundary geometry.
# 500 meter buffer size is arbitrary but large enough
# to capture area outside of the study settlement.
bufferSize = 500; # (in meters)

# Buffer and convert to Geometry for spatial filtering and clipping.
bufferedBounds = pagirinya.buffer(bufferSize) \
    .bounds().geometry()

def addIndices(img):
    ndvi = img.normalizedDifference(['nir', 'red']) \
        .rename('NDVI'); 
    ndbi = img.normalizedDifference(['swir1', 'nir']) \
        .rename(['NDBI']); 
    nbr = img.normalizedDifference(['nir', 'swir2']) \
        .rename(['NBR']); 
    imgIndices = img.addBands(ndvi).addBands(ndbi).addBands(nbr)
    return imgIndices


# Create L8 SR Collection 2 band names and new names.
landsat8BandNames = ['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6',
    'SR_B7'
]
landsat8BandRename = ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']

# Create image collection.
landsat8Sr = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
ic = ee.ImageCollection(landsat8Sr.filterDate('2015-01-01',
        '2020-12-31') \
    .filterBounds(bufferedBounds) \
    .filter(ee.Filter.lt('CLOUD_COVER', 40)) \
    .select(landsat8BandNames, landsat8BandRename) \
    .map(addIndices))

# Make annual pre- and post-establishment composites.
preMedian = ic.filterDate('2015-01-01', '2015-12-31').median() \
    .clip(bufferedBounds)
postMedian = ic.filterDate('2017-01-01', '2017-12-31').median() \
    .clip(bufferedBounds)

# Import visualization palettes https:#github.com/gee-community/ee-palettes.
palettes = require('users/gena/packages:palettes')
greenPalette = palettes.colorbrewer.Greens[9]
prGreenPalette = palettes.colorbrewer.PRGn[9]

# Set-up "True color" visualization parameters.
TCImageVisParam = {
    'bands': ['red', 'green', 'blue'],
    'gamma': 1,
    'max': 13600,
    'min': 8400,
    'opacity': 1
}

# Set-up "False color" visualization parameters.
FCImageVisParam = {
    'bands': ['nir', 'red', 'green'],
    'gamma': 1,
    'min': 9000,
    'max': 20000,
    'opacity': 1
}

# Display True-color composites.
Map.addLayer(preMedian, TCImageVisParam,
    'Pre-Establishment Median TC')
Map.addLayer(postMedian, TCImageVisParam,
    'Post-Establishment Median TC')

# Display False-color composites.
Map.addLayer(preMedian, FCImageVisParam,
    'Pre-Establishment Median FC')
Map.addLayer(postMedian, FCImageVisParam,
    'Post-Establishment Median FC')

# Display median NDVI composites.
Map.addLayer(preMedian, {
    'min': 0,
    'max': 0.7,
    'bands': ['NDVI'],
    'palette': greenPalette
}, 'Pre-Establishment Median NDVI')
Map.addLayer(postMedian, {
    'min': 0,
    'max': 0.7,
    'bands': ['NDVI'],
    'palette': greenPalette
}, 'Post-Establishment Median NDVI')

# Create an empty byte Image into which we’ll paint the settlement boundary.
empty = ee.Image().byte()

# Convert settlement boundary’s geometry to an Image for overlay.
pagirinyaOutline = empty.paint({
    'featureCollection': pagirinya,
    'color': 1,
    'width': 2
})

# Display Pagirinya boundary in blue.
Map.addLayer(pagirinyaOutline,
    {
        'palette': '0000FF'
    },
    'Pagirinya Refugee Settlement boundary')

# Compare pre- and post-establishment differences in NDVI.
diffMedian = postMedian.subtract(preMedian)
Map.addLayer(diffMedian,
    {
        'min': -0.1,
        'max': 0.1,
        'bands': ['NDVI'],
        'palette': prGreenPalette
    },
    'Difference Median NDVI')

# Chart the NDVI distributions for pre- and post-establishment.
combinedNDVI = preMedian.select(['NDVI'], ['pre-NDVI']) \
    .addBands(postMedian.select(['NDVI'], ['post-NDVI']))

prePostNDVIFrequencyChart =
    ui.Chart.image.histogram({
        'image': combinedNDVI,
        'region': bufferedBounds,
        'scale': 30
    }).setSeriesNames(['Pre-Establishment', 'Post-Establishment']) \
    .setOptions({
        'title': 'NDVI Frequency Histogram',
        'hAxis': {
            'title': 'NDVI',
            'titleTextStyle': {
                'italic': False,
                'bold': True
            },
        },
        'vAxis':
        {
            'title': 'Count',
            'titleTextStyle': {
                'italic': False,
                'bold': True
            }
        },
        'colors': ['cf513e', '1d6b99']
    })
print(prePostNDVIFrequencyChart)

# Import package to support text annotation.
text = require('users/gena/packages:text')
rgbVisParam = {
    'bands': ['red', 'green', 'blue'],
    'gamma': 1,
    'max': 12011,
    'min': 8114,
    'opacity': 1
}

# Define arguments for animation function parameters.
videoArgs = {
    'region': bufferedBounds,
    'framesPerSecond': 3,
    'scale': 10
}

annotations = [{
    'position': 'left',
    'offset': '5%',
    'margin': '5%',
    'property': 'label',
    'scale': 30
}]

def addText(image):
    date = ee.String(ee.Date(image.get('system:time_start')) \
        .format(' YYYY-MM-dd'))
    # Set a property called label for each image.
    image = image.clip(bufferedBounds).visualize(rgbVisParam) \
        .set({
            'label': date
        })
    # Create a new image with the label overlaid using gena's package.
    annotated = text.annotateImage(image, {}, bufferedBounds,
        annotations)
    return annotated


# Add timestamp annotation to all images in video.
tempCol = ic.map(addText)

# Click the URL to watch the time series video.
print('L8 Time Series Video', tempCol.getVideoThumbURL(videoArgs))

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A1.7 Humanitarian Applications # Checkpoint: A17a # Authors: Jamon Van Den Hoek, Hannah K. Friedrich # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ########################### #/ Section One: Seeing refugee settlements from above ########################### Map.setOptions('SATELLITE') # Load UNHCR settlement boundary for Pagirinya Refugee Settlement. pagirinya = ee.Feature(ee.FeatureCollection( 'projects/gee-book/assets/A1-7/pagirinya_settlement_boundary' ).first()) Map.addLayer(pagirinya, {}, 'Pagirinya Refugee Settlement') Map.centerObject(pagirinya, 14) # Create buffered settlement boundary geometry. # 500 meter buffer size is arbitrary but large enough # to capture area outside of the study settlement. bufferSize = 500; # (in meters) # Buffer and convert to Geometry for spatial filtering and clipping. bufferedBounds = pagirinya.buffer(bufferSize) \ .bounds().geometry() def addIndices(img): ndvi = img.normalizedDifference(['nir', 'red']) \ .rename('NDVI'); ndbi = img.normalizedDifference(['swir1', 'nir']) \ .rename(['NDBI']); nbr = img.normalizedDifference(['nir', 'swir2']) \ .rename(['NBR']); imgIndices = img.addBands(ndvi).addBands(ndbi).addBands(nbr) return imgIndices # Create L8 SR Collection 2 band names and new names. landsat8BandNames = ['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7' ] landsat8BandRename = ['blue', 'green', 'red', 'nir', 'swir1', 'swir2'] # Create image collection. landsat8Sr = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') ic = ee.ImageCollection(landsat8Sr.filterDate('2015-01-01', '2020-12-31') \ .filterBounds(bufferedBounds) \ .filter(ee.Filter.lt('CLOUD_COVER', 40)) \ .select(landsat8BandNames, landsat8BandRename) \ .map(addIndices)) # Make annual pre- and post-establishment composites. preMedian = ic.filterDate('2015-01-01', '2015-12-31').median() \ .clip(bufferedBounds) postMedian = ic.filterDate('2017-01-01', '2017-12-31').median() \ .clip(bufferedBounds) # Import visualization palettes https:#github.com/gee-community/ee-palettes. palettes = require('users/gena/packages:palettes') greenPalette = palettes.colorbrewer.Greens[9] prGreenPalette = palettes.colorbrewer.PRGn[9] # Set-up "True color" visualization parameters. TCImageVisParam = { 'bands': ['red', 'green', 'blue'], 'gamma': 1, 'max': 13600, 'min': 8400, 'opacity': 1 } # Set-up "False color" visualization parameters. FCImageVisParam = { 'bands': ['nir', 'red', 'green'], 'gamma': 1, 'min': 9000, 'max': 20000, 'opacity': 1 } # Display True-color composites. Map.addLayer(preMedian, TCImageVisParam, 'Pre-Establishment Median TC') Map.addLayer(postMedian, TCImageVisParam, 'Post-Establishment Median TC') # Display False-color composites. Map.addLayer(preMedian, FCImageVisParam, 'Pre-Establishment Median FC') Map.addLayer(postMedian, FCImageVisParam, 'Post-Establishment Median FC') # Display median NDVI composites. Map.addLayer(preMedian, { 'min': 0, 'max': 0.7, 'bands': ['NDVI'], 'palette': greenPalette }, 'Pre-Establishment Median NDVI') Map.addLayer(postMedian, { 'min': 0, 'max': 0.7, 'bands': ['NDVI'], 'palette': greenPalette }, 'Post-Establishment Median NDVI') # Create an empty byte Image into which we’ll paint the settlement boundary. empty = ee.Image().byte() # Convert settlement boundary’s geometry to an Image for overlay. pagirinyaOutline = empty.paint({ 'featureCollection': pagirinya, 'color': 1, 'width': 2 }) # Display Pagirinya boundary in blue. Map.addLayer(pagirinyaOutline, { 'palette': '0000FF' }, 'Pagirinya Refugee Settlement boundary') # Compare pre- and post-establishment differences in NDVI. diffMedian = postMedian.subtract(preMedian) Map.addLayer(diffMedian, { 'min': -0.1, 'max': 0.1, 'bands': ['NDVI'], 'palette': prGreenPalette }, 'Difference Median NDVI') # Chart the NDVI distributions for pre- and post-establishment. combinedNDVI = preMedian.select(['NDVI'], ['pre-NDVI']) \ .addBands(postMedian.select(['NDVI'], ['post-NDVI'])) prePostNDVIFrequencyChart = ui.Chart.image.histogram({ 'image': combinedNDVI, 'region': bufferedBounds, 'scale': 30 }).setSeriesNames(['Pre-Establishment', 'Post-Establishment']) \ .setOptions({ 'title': 'NDVI Frequency Histogram', 'hAxis': { 'title': 'NDVI', 'titleTextStyle': { 'italic': False, 'bold': True }, }, 'vAxis': { 'title': 'Count', 'titleTextStyle': { 'italic': False, 'bold': True } }, 'colors': ['cf513e', '1d6b99'] }) print(prePostNDVIFrequencyChart) # Import package to support text annotation. text = require('users/gena/packages:text') rgbVisParam = { 'bands': ['red', 'green', 'blue'], 'gamma': 1, 'max': 12011, 'min': 8114, 'opacity': 1 } # Define arguments for animation function parameters. videoArgs = { 'region': bufferedBounds, 'framesPerSecond': 3, 'scale': 10 } annotations = [{ 'position': 'left', 'offset': '5%', 'margin': '5%', 'property': 'label', 'scale': 30 }] def addText(image): date = ee.String(ee.Date(image.get('system:time_start')) \ .format(' YYYY-MM-dd')) # Set a property called label for each image. image = image.clip(bufferedBounds).visualize(rgbVisParam) \ .set({ 'label': date }) # Create a new image with the label overlaid using gena's package. annotated = text.annotateImage(image, {}, bufferedBounds, annotations) return annotated # Add timestamp annotation to all images in video. tempCol = ic.map(addText) # Click the URL to watch the time series video. print('L8 Time Series Video', tempCol.getVideoThumbURL(videoArgs)) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map