F13e 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")

#*** Start of imports. If edited, may not auto-convert in the playground. ***#
msi = ee.ImageCollection("COPERNICUS/S2"),
    naip = ee.ImageCollection("USDA/NAIP/DOQQ"),
    eo1 = ee.ImageCollection("EO1/HYPERION"),
    tm = ee.ImageCollection("LANDSAT/LT05/C02/T1"),
    mod09 = ee.ImageCollection("MODIS/061/MOD09A1")
#**** End of imports. If edited, may not auto-convert in the playground. ****#
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      F1.3 The Remote Sensing Vocabulary
#  Checkpoint:   F13e
#  Authors:      K. Dyson, A. P. Nicolau, D. Saah, and N. Clinton
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
###
# Explore spatial resolution
###

# Define a region of interest as a point at San Francisco airport.
sfoPoint = ee.Geometry.Point(-122.3774, 37.6194)

# Center the map at that point.
Map.centerObject(sfoPoint, 16)

# MODIS
# Get an image from your imported MODIS MYD09GA collection.
modisImage = mod09.filterDate('2020-02-01', '2020-03-01').first()

# Use these MODIS bands for near infrared, red, and green, respectively.
modisBands = ['sur_refl_b02', 'sur_refl_b01', 'sur_refl_b04']

# Define visualization parameters for MODIS.
modisVis = {
    'bands': modisBands,
    'min': 0,
    'max': 3000
}

# Add the MODIS image to the map.
Map.addLayer(modisImage, modisVis, 'MODIS')

# Get the scale of the data from the NIR band's projection:
modisScale = modisImage.select('sur_refl_b02') \
    .projection().nominalScale()

print('MODIS NIR scale:', modisScale)

# TM
# Filter TM imagery by location and date.

tmImage = tm \
    .filterBounds(Map.getCenter()) \
    .filterDate('1987-03-01', '1987-08-01') \
    .first()

# Display the TM image as a False color composite.
Map.addLayer(tmImage, {
    'bands': ['B4', 'B3', 'B2'],
    'min': 0,
    'max': 100
}, 'TM')

# Get the scale of the TM data from its projection:
tmScale = tmImage.select('B4') \
    .projection().nominalScale()

print('TM NIR scale:', tmScale)

# MSI
# Filter MSI imagery by location and date.
msiImage = msi \
    .filterBounds(Map.getCenter()) \
    .filterDate('2020-02-01', '2020-04-01') \
    .first()

# Display the MSI image as a False color composite.
Map.addLayer(msiImage, {
    'bands': ['B8', 'B4', 'B3'],
    'min': 0,
    'max': 2000
}, 'MSI')

# Get the scale of the MSI data from its projection:
msiScale = msiImage.select('B8') \
    .projection().nominalScale()
print('MSI scale:', msiScale)

# NAIP
# Get NAIP images for the study period and region of interest.
naipImage = naip \
    .filterBounds(Map.getCenter()) \
    .filterDate('2018-01-01', '2018-12-31') \
    .first()

# Display the NAIP mosaic as a color-IR composite.
Map.addLayer(naipImage, {
    'bands': ['N', 'R', 'G']
}, 'NAIP')

# Get the NAIP resolution from the first image in the mosaic.
naipScale = naipImage.select('N') \
    .projection().nominalScale()

print('NAIP NIR scale:', naipScale)

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

##/
# Explore Temporal Resolution
##/

# Use Print to see Landsat revisit time
print('Landsat-5 series:', tm \
    .filterBounds(Map.getCenter()) \
    .filterDate('1987-06-01', '1987-09-01'))

# Create a chart to see Landsat 5's 16 day revisit time.
tmChart = ui.Chart.image.series({
    'imageCollection': tm.select('B4').filterDate('1987-06-01',
        '1987-09-01'),
    'region': sfoPoint
}).setSeriesNames(['NIR'])

# Define a chart style that will let us see the individual dates.
chartStyle = {
    'hAxis': {
        'title': 'Date'
    },
    'vAxis': {
        'title': 'NIR Mean'
    },
    'series': {
        '0': {
            'lineWidth': 3,
            'pointSize': 6
        }
    },
}

# Apply custom style properties to the chart.
tmChart.setOptions(chartStyle)

# Print the chart.
print('TM Chart', tmChart)

# Sentinel-2 has a 5 day revisit time.
msiChart = ui.Chart.image.series({
    'imageCollection': msi.select('B8').filterDate('2020-06-01',
        '2020-09-01'),
    'region': sfoPoint
}).setSeriesNames(['NIR'])

# Apply the previously defined custom style properties to the chart.
msiChart.setOptions(chartStyle)

# Print the chart.
print('MSI Chart', msiChart)

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

##/
# Explore spectral resolution
##/

# Get the MODIS band names as an ee.List
modisBands = modisImage.bandNames()

# Print the list.
print('MODIS bands:', modisBands)

# Print the length of the list.
print('Length of the bands list:', modisBands.length())

# Graph the MODIS spectral bands (bands 11-17).

# Select only the reflectance bands of interest.
reflectanceImage = modisImage.select(
    'sur_refl_b01',
    'sur_refl_b02',
    'sur_refl_b03',
    'sur_refl_b04',
    'sur_refl_b05',
    'sur_refl_b06',
    'sur_refl_b07'
)

# Define an object of customization parameters for the chart.
options = {
    'title': 'MODIS spectrum at SFO',
    'hAxis': {
        'title': 'Band'
    },
    'vAxis': {
        'title': 'Reflectance'
    },
    'legend': {
        'position': 'none'
    },
    'pointSize': 3
}

# Make the chart.
modisReflectanceChart = ui.Chart.image.regions({
    'image': reflectanceImage,
    'regions': sfoPoint
}).setOptions(options)

# Display the chart.
print(modisReflectanceChart)

# Get the EO-1 band names as a ee.List
eo1Image = eo1 \
    .filterDate('2015-01-01', '2016-01-01') \
    .first()

# Extract the EO-1 band names.
eo1Bands = eo1Image.bandNames()

# Print the list of band names.
print('EO-1 bands:', eo1Bands)

# Create an options object for our chart.
optionsEO1 = {
    'title': 'EO1 spectrum',
    'hAxis': {
        'title': 'Band'
    },
    'vAxis': {
        'title': 'Reflectance'
    },
    'legend': {
        'position': 'none'
    },
    'pointSize': 3
}

# Make the chart and set the options.
eo1Chart = ui.Chart.image.regions({
    'image': eo1Image,
    'regions': ee.Geometry.Point([6.10, 81.12])
}).setOptions(optionsEO1)

# Display the chart.
print(eo1Chart)

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

##/
# Examine pixel quality
##/

# Sentinel Quality Visualization.
msiCloud = msi \
    .filterBounds(Map.getCenter()) \
    .filterDate('2019-12-31', '2020-02-01') \
    .first()

# Display the MSI image as a False color composite.
Map.addLayer(msiCloud,
    {
        'bands': ['B8', 'B4', 'B3'],
        'min': 0,
        'max': 2000
    },
    'MSI Quality Image')

Map.addLayer(msiCloud,
    {
        'bands': ['QA60'],
        'min': 0,
        'max': 2000
    },
    'Sentinel Quality Visualization')

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

##/
# Metadata
##/
print('MSI Image Metadata', msiImage)

# Image-level Cloud info
msiCloudiness = msiImage.get('CLOUDY_PIXEL_PERCENTAGE')

print('MSI CLOUDY_PIXEL_PERCENTAGE:', msiCloudiness)

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") #*** Start of imports. If edited, may not auto-convert in the playground. ***# msi = ee.ImageCollection("COPERNICUS/S2"), naip = ee.ImageCollection("USDA/NAIP/DOQQ"), eo1 = ee.ImageCollection("EO1/HYPERION"), tm = ee.ImageCollection("LANDSAT/LT05/C02/T1"), mod09 = ee.ImageCollection("MODIS/061/MOD09A1") #**** End of imports. If edited, may not auto-convert in the playground. ****# # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F1.3 The Remote Sensing Vocabulary # Checkpoint: F13e # Authors: K. Dyson, A. P. Nicolau, D. Saah, and N. Clinton # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ### # Explore spatial resolution ### # Define a region of interest as a point at San Francisco airport. sfoPoint = ee.Geometry.Point(-122.3774, 37.6194) # Center the map at that point. Map.centerObject(sfoPoint, 16) # MODIS # Get an image from your imported MODIS MYD09GA collection. modisImage = mod09.filterDate('2020-02-01', '2020-03-01').first() # Use these MODIS bands for near infrared, red, and green, respectively. modisBands = ['sur_refl_b02', 'sur_refl_b01', 'sur_refl_b04'] # Define visualization parameters for MODIS. modisVis = { 'bands': modisBands, 'min': 0, 'max': 3000 } # Add the MODIS image to the map. Map.addLayer(modisImage, modisVis, 'MODIS') # Get the scale of the data from the NIR band's projection: modisScale = modisImage.select('sur_refl_b02') \ .projection().nominalScale() print('MODIS NIR scale:', modisScale) # TM # Filter TM imagery by location and date. tmImage = tm \ .filterBounds(Map.getCenter()) \ .filterDate('1987-03-01', '1987-08-01') \ .first() # Display the TM image as a False color composite. Map.addLayer(tmImage, { 'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 100 }, 'TM') # Get the scale of the TM data from its projection: tmScale = tmImage.select('B4') \ .projection().nominalScale() print('TM NIR scale:', tmScale) # MSI # Filter MSI imagery by location and date. msiImage = msi \ .filterBounds(Map.getCenter()) \ .filterDate('2020-02-01', '2020-04-01') \ .first() # Display the MSI image as a False color composite. Map.addLayer(msiImage, { 'bands': ['B8', 'B4', 'B3'], 'min': 0, 'max': 2000 }, 'MSI') # Get the scale of the MSI data from its projection: msiScale = msiImage.select('B8') \ .projection().nominalScale() print('MSI scale:', msiScale) # NAIP # Get NAIP images for the study period and region of interest. naipImage = naip \ .filterBounds(Map.getCenter()) \ .filterDate('2018-01-01', '2018-12-31') \ .first() # Display the NAIP mosaic as a color-IR composite. Map.addLayer(naipImage, { 'bands': ['N', 'R', 'G'] }, 'NAIP') # Get the NAIP resolution from the first image in the mosaic. naipScale = naipImage.select('N') \ .projection().nominalScale() print('NAIP NIR scale:', naipScale) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- ##/ # Explore Temporal Resolution ##/ # Use Print to see Landsat revisit time print('Landsat-5 series:', tm \ .filterBounds(Map.getCenter()) \ .filterDate('1987-06-01', '1987-09-01')) # Create a chart to see Landsat 5's 16 day revisit time. tmChart = ui.Chart.image.series({ 'imageCollection': tm.select('B4').filterDate('1987-06-01', '1987-09-01'), 'region': sfoPoint }).setSeriesNames(['NIR']) # Define a chart style that will let us see the individual dates. chartStyle = { 'hAxis': { 'title': 'Date' }, 'vAxis': { 'title': 'NIR Mean' }, 'series': { '0': { 'lineWidth': 3, 'pointSize': 6 } }, } # Apply custom style properties to the chart. tmChart.setOptions(chartStyle) # Print the chart. print('TM Chart', tmChart) # Sentinel-2 has a 5 day revisit time. msiChart = ui.Chart.image.series({ 'imageCollection': msi.select('B8').filterDate('2020-06-01', '2020-09-01'), 'region': sfoPoint }).setSeriesNames(['NIR']) # Apply the previously defined custom style properties to the chart. msiChart.setOptions(chartStyle) # Print the chart. print('MSI Chart', msiChart) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- ##/ # Explore spectral resolution ##/ # Get the MODIS band names as an ee.List modisBands = modisImage.bandNames() # Print the list. print('MODIS bands:', modisBands) # Print the length of the list. print('Length of the bands list:', modisBands.length()) # Graph the MODIS spectral bands (bands 11-17). # Select only the reflectance bands of interest. reflectanceImage = modisImage.select( 'sur_refl_b01', 'sur_refl_b02', 'sur_refl_b03', 'sur_refl_b04', 'sur_refl_b05', 'sur_refl_b06', 'sur_refl_b07' ) # Define an object of customization parameters for the chart. options = { 'title': 'MODIS spectrum at SFO', 'hAxis': { 'title': 'Band' }, 'vAxis': { 'title': 'Reflectance' }, 'legend': { 'position': 'none' }, 'pointSize': 3 } # Make the chart. modisReflectanceChart = ui.Chart.image.regions({ 'image': reflectanceImage, 'regions': sfoPoint }).setOptions(options) # Display the chart. print(modisReflectanceChart) # Get the EO-1 band names as a ee.List eo1Image = eo1 \ .filterDate('2015-01-01', '2016-01-01') \ .first() # Extract the EO-1 band names. eo1Bands = eo1Image.bandNames() # Print the list of band names. print('EO-1 bands:', eo1Bands) # Create an options object for our chart. optionsEO1 = { 'title': 'EO1 spectrum', 'hAxis': { 'title': 'Band' }, 'vAxis': { 'title': 'Reflectance' }, 'legend': { 'position': 'none' }, 'pointSize': 3 } # Make the chart and set the options. eo1Chart = ui.Chart.image.regions({ 'image': eo1Image, 'regions': ee.Geometry.Point([6.10, 81.12]) }).setOptions(optionsEO1) # Display the chart. print(eo1Chart) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- ##/ # Examine pixel quality ##/ # Sentinel Quality Visualization. msiCloud = msi \ .filterBounds(Map.getCenter()) \ .filterDate('2019-12-31', '2020-02-01') \ .first() # Display the MSI image as a False color composite. Map.addLayer(msiCloud, { 'bands': ['B8', 'B4', 'B3'], 'min': 0, 'max': 2000 }, 'MSI Quality Image') Map.addLayer(msiCloud, { 'bands': ['QA60'], 'min': 0, 'max': 2000 }, 'Sentinel Quality Visualization') # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- ##/ # Metadata ##/ print('MSI Image Metadata', msiImage) # Image-level Cloud info msiCloudiness = msiImage.get('CLOUDY_PIXEL_PERCENTAGE') print('MSI CLOUDY_PIXEL_PERCENTAGE:', msiCloudiness) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

In [ ]:

Map

Map