F52c 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:      F5.2 Zonal Statistics
#  Checkpoint:   F52c
#  Authors:      Sara Winsemius and Justin Braaten
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Copy properties to computed images

# Define a Landsat image.
img = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').first()

# Print its properties.
print('All image properties', img.propertyNames())

# Subset the reflectance bands and unscale them.
computedImg = img.select('SR_B.').multiply(0.0000275).add(-0.2)

# Print the unscaled image's properties.
print('Lost original image properties', computedImg.propertyNames())

# Subset the reflectance bands and unscale them, keeping selected
# source properties.
computedImg = img.select('SR_B.').multiply(0.0000275).add(-0.2) \
    .copyProperties(img, ['system:time_start', 'LANDSAT_PRODUCT_ID'])

# Print the unscaled image's properties.
print('Selected image properties retained', computedImg \
.propertyNames())

# Understanding which pixels are included in polygon statistics

# Define polygon geometry.
geometry = ee.Geometry.Polygon(
    [
        [
            [-118.6019835717645, 37.079867782687884],
            [-118.6019835717645, 37.07838698844939],
            [-118.60036351751951, 37.07838698844939],
            [-118.60036351751951, 37.079867782687884]
        ]
    ], None, False)

# Import the MERIT global elevation dataset.
elev = ee.Image('MERIT/DEM/v1_0_3')

# Define desired scale and crs for region reduction (for image display too).
proj = {
    'scale': 90,
    'crs': 'EPSG:5070'
}

# A count reducer will return how many pixel centers are overlapped by the
# polygon region.
count = elev.select(0).reduceRegion({
    'reducer': ee.Reducer.count(),
    'geometry': geometry,
    'scale': proj.scale,
    'crs': proj.crs
})
print('n pixels in the reduction', count.get('dem'))

# Make a feature collection of pixel center points for those that are
# included in the reduction.
pixels = ee.Image.pixelLonLat().reduceRegion({
    'reducer': ee.Reducer.toCollection(['lon', 'lat']),
    'geometry': geometry,
    'scale': proj.scale,
    'crs': proj.crs
})
pixelsFc = ee.FeatureCollection(pixels.get('features')).map(
    def function(f):
        return f.setGeometry(ee.Geometry.Point([f.get('lon'), f \
            .get('lat')
        ]))
    )

# Display layers on the map.
Map.centerObject(geometry, 18)
Map.addLayer(
    elev.reproject({
        'crs': proj.crs,
        'scale': proj.scale
    }),
    {
        'min': 2500,
        'max': 3000,
        'palette': ['blue', 'white', 'red']
    }, 'Image')
Map.addLayer(geometry, {
    'color': 'white'
}, 'Geometry')
Map.addLayer(pixelsFc, {
    'color': 'purple'
}, 'Pixels in reduction')



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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F5.2 Zonal Statistics # Checkpoint: F52c # Authors: Sara Winsemius and Justin Braaten # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Copy properties to computed images # Define a Landsat image. img = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').first() # Print its properties. print('All image properties', img.propertyNames()) # Subset the reflectance bands and unscale them. computedImg = img.select('SR_B.').multiply(0.0000275).add(-0.2) # Print the unscaled image's properties. print('Lost original image properties', computedImg.propertyNames()) # Subset the reflectance bands and unscale them, keeping selected # source properties. computedImg = img.select('SR_B.').multiply(0.0000275).add(-0.2) \ .copyProperties(img, ['system:time_start', 'LANDSAT_PRODUCT_ID']) # Print the unscaled image's properties. print('Selected image properties retained', computedImg \ .propertyNames()) # Understanding which pixels are included in polygon statistics # Define polygon geometry. geometry = ee.Geometry.Polygon( [ [ [-118.6019835717645, 37.079867782687884], [-118.6019835717645, 37.07838698844939], [-118.60036351751951, 37.07838698844939], [-118.60036351751951, 37.079867782687884] ] ], None, False) # Import the MERIT global elevation dataset. elev = ee.Image('MERIT/DEM/v1_0_3') # Define desired scale and crs for region reduction (for image display too). proj = { 'scale': 90, 'crs': 'EPSG:5070' } # A count reducer will return how many pixel centers are overlapped by the # polygon region. count = elev.select(0).reduceRegion({ 'reducer': ee.Reducer.count(), 'geometry': geometry, 'scale': proj.scale, 'crs': proj.crs }) print('n pixels in the reduction', count.get('dem')) # Make a feature collection of pixel center points for those that are # included in the reduction. pixels = ee.Image.pixelLonLat().reduceRegion({ 'reducer': ee.Reducer.toCollection(['lon', 'lat']), 'geometry': geometry, 'scale': proj.scale, 'crs': proj.crs }) pixelsFc = ee.FeatureCollection(pixels.get('features')).map( def function(f): return f.setGeometry(ee.Geometry.Point([f.get('lon'), f \ .get('lat') ])) ) # Display layers on the map. Map.centerObject(geometry, 18) Map.addLayer( elev.reproject({ 'crs': proj.crs, 'scale': proj.scale }), { 'min': 2500, 'max': 3000, 'palette': ['blue', 'white', 'red'] }, 'Image') Map.addLayer(geometry, { 'color': 'white' }, 'Geometry') Map.addLayer(pixelsFc, { 'color': 'purple' }, 'Pixels in reduction') # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map