F43a 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:      F4.3 Clouds and Image Compositing
#  Checkpoint:   F43a
#  Authors:      Txomin Hermosilla, Saverio Francini, Andréa P. Nicolau,
#                Michael A. Wulder, Joanne C. White, Nicholas C. Coops,
#                Gherardo Chirici
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# ---------- Section 1 -----------------

# Define the AOI.
country = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017").filter(
    ee.Filter.equals("country_na", "Colombia")
)

# Center the Map. The second parameter is zoom level.
Map.centerObject(country, 5)

# Define time variables.
startDate = "2019-01-01"
endDate = "2019-12-31"

# Load and filter the Landsat 8 collection.
landsat8 = (
    ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
    .filterBounds(country)
    .filterDate(startDate, endDate)
)


# Apply scaling factors.
def applyScaleFactors(image):
    opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2)
    thermalBands = image.select("ST_B.*").multiply(0.00341802).add(149.0)
    return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)


landsat8 = landsat8.map(applyScaleFactors)

# Create composite.
composite = landsat8.median().clip(country)

visParams = {"bands": ["SR_B4", "SR_B3", "SR_B2"], "min": 0, "max": 0.2}
Map.addLayer(composite, visParams, "L8 Composite")

# Filter by the CLOUD_COVER property.
landsat8FiltClouds = (
    landsat8.filterBounds(country)
    .filterDate(startDate, endDate)
    .filter(ee.Filter.lessThan("CLOUD_COVER", 50))
)

# Create a composite from the filtered imagery.
compositeFiltClouds = landsat8FiltClouds.median().clip(country)

Map.addLayer(compositeFiltClouds, visParams, "L8 Composite cloud filter")

# Print size of collections, for comparison.
print("Size landsat8 collection", landsat8.size())
print("Size landsat8FiltClouds collection", landsat8FiltClouds.size())


# Define the cloud mask function.
def maskSrClouds(image):
    # Bit 0 - Fill
    # Bit 1 - Dilated Cloud
    # Bit 2 - Cirrus
    # Bit 3 - Cloud
    # Bit 4 - Cloud Shadow
    qaMask = image.select("QA_PIXEL").bitwiseAnd(parseInt("11111", 2)).eq(0)
    saturationMask = image.select("QA_RADSAT").eq(0)

    return image.updateMask(qaMask).updateMask(saturationMask)


# Apply the cloud mask to the collection.
landsat8FiltMasked = landsat8FiltClouds.map(maskSrClouds)

# Create a composite.
landsat8compositeMasked = landsat8FiltMasked.median().clip(country)

Map.addLayer(landsat8compositeMasked, visParams, "L8 composite masked")

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F4.3 Clouds and Image Compositing # Checkpoint: F43a # Authors: Txomin Hermosilla, Saverio Francini, Andréa P. Nicolau, # Michael A. Wulder, Joanne C. White, Nicholas C. Coops, # Gherardo Chirici # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ---------- Section 1 ----------------- # Define the AOI. country = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017").filter( ee.Filter.equals("country_na", "Colombia") ) # Center the Map. The second parameter is zoom level. Map.centerObject(country, 5) # Define time variables. startDate = "2019-01-01" endDate = "2019-12-31" # Load and filter the Landsat 8 collection. landsat8 = ( ee.ImageCollection("LANDSAT/LC08/C02/T1_L2") .filterBounds(country) .filterDate(startDate, endDate) ) # Apply scaling factors. def applyScaleFactors(image): opticalBands = image.select("SR_B.").multiply(0.0000275).add(-0.2) thermalBands = image.select("ST_B.*").multiply(0.00341802).add(149.0) return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True) landsat8 = landsat8.map(applyScaleFactors) # Create composite. composite = landsat8.median().clip(country) visParams = {"bands": ["SR_B4", "SR_B3", "SR_B2"], "min": 0, "max": 0.2} Map.addLayer(composite, visParams, "L8 Composite") # Filter by the CLOUD_COVER property. landsat8FiltClouds = ( landsat8.filterBounds(country) .filterDate(startDate, endDate) .filter(ee.Filter.lessThan("CLOUD_COVER", 50)) ) # Create a composite from the filtered imagery. compositeFiltClouds = landsat8FiltClouds.median().clip(country) Map.addLayer(compositeFiltClouds, visParams, "L8 Composite cloud filter") # Print size of collections, for comparison. print("Size landsat8 collection", landsat8.size()) print("Size landsat8FiltClouds collection", landsat8FiltClouds.size()) # Define the cloud mask function. def maskSrClouds(image): # Bit 0 - Fill # Bit 1 - Dilated Cloud # Bit 2 - Cirrus # Bit 3 - Cloud # Bit 4 - Cloud Shadow qaMask = image.select("QA_PIXEL").bitwiseAnd(parseInt("11111", 2)).eq(0) saturationMask = image.select("QA_RADSAT").eq(0) return image.updateMask(qaMask).updateMask(saturationMask) # Apply the cloud mask to the collection. landsat8FiltMasked = landsat8FiltClouds.map(maskSrClouds) # Create a composite. landsat8compositeMasked = landsat8FiltMasked.median().clip(country) Map.addLayer(landsat8compositeMasked, visParams, "L8 composite masked") # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map