F40c 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:      F4.0 Filter, Map, Reduce
#  Checkpoint:   F40c
#  Author:       Jeff Cardille
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

imgCol = ee.ImageCollection("LANDSAT/LT05/C02/T1_L2")
# How many Tier 1 Landsat 5 images have ever been collected?
print("All images ever: ", imgCol.size())
# A very large number

# How many images were collected in the 2000s?
startDate = "2000-01-01"
endDate = "2010-01-01"

imgColfilteredByDate = imgCol.filterDate(startDate, endDate)
print("All images 2000-2010: ", imgColfilteredByDate.size())
# A smaller (but still large) number

ShanghaiImage = ee.Image("LANDSAT/LT05/C02/T1_L2/LT05_118038_20000606")
Map.centerObject(ShanghaiImage, 9)

imgColfilteredByDateHere = imgColfilteredByDate.filterBounds(Map.getCenter())
print("All images here, 2000-2010: ", imgColfilteredByDateHere.size())

L5FilteredLowCloudImages = imgColfilteredByDateHere.filterMetadata(
    "CLOUD_COVER", "less_than", 50
)
print("Less than 50% clouds in this area, 2000-2010", L5FilteredLowCloudImages.size())
# A smaller number

chainedFilteredSet = (
    imgCol.filterDate(startDate, endDate)
    .filterBounds(Map.getCenter())
    .filterMetadata("CLOUD_COVER", "less_than", 50)
)
print(
    "Chained: Less than 50% clouds in this area, 2000-2010", chainedFilteredSet.size()
)

efficientFilteredSet = (
    imgCol.filterBounds(Map.getCenter())
    .filterDate(startDate, endDate)
    .filterMetadata("CLOUD_COVER", "less_than", 50)
)
print(
    "Efficient filtering: Less than 50% clouds in this area, 2000-2010",
    efficientFilteredSet.size(),
)

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


def makeLandsat5EVI(oneL5Image):
    # compute the EVI for any Landsat 5 image. Note it's specific to
    # Landsat 5 images due to the band numbers. Don't run this exact
    # function for images from sensors other than Landsat 5.

    # Extract the bands and divide by 1e4 to account for scaling done.
    nirScaled = oneL5Image.select("SR_B4").divide(10000)
    redScaled = oneL5Image.select("SR_B3").divide(10000)
    blueScaled = oneL5Image.select("SR_B1").divide(10000)

    # Calculate the numerator, note that order goes from left to right.
    numeratorEVI = (nirScaled.subtract(redScaled)).multiply(2.5)

    # Calculate the denominator
    denomClause1 = redScaled.multiply(6)
    denomClause2 = blueScaled.multiply(7.5)
    denominatorEVI = nirScaled.add(denomClause1).subtract(denomClause2).add(1)

    # Calculate EVI and name it.
    landsat5EVI = numeratorEVI.divide(denominatorEVI).rename("EVI")
    return landsat5EVI


L5EVIimages = efficientFilteredSet.map(makeLandsat5EVI)
print(
    "Verifying that the .map gives back the same number of images: ", L5EVIimages.size()
)
print(L5EVIimages)

Map.addLayer(L5EVIimages, {}, "L5EVIimages", 1, 1)

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

L5EVImean = L5EVIimages.reduce(ee.Reducer.mean())
print(L5EVImean)
Map.addLayer(
    L5EVImean, {"min": -1, "max": 2, "palette": ["red", "white", "green"]}, "Mean EVI"
)

L5EVImedian = L5EVIimages.reduce(ee.Reducer.median())
print(L5EVImedian)
Map.addLayer(
    L5EVImedian,
    {"min": -1, "max": 2, "palette": ["red", "white", "green"]},
    "Median EVI",
)

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F4.0 Filter, Map, Reduce # Checkpoint: F40c # Author: Jeff Cardille # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ imgCol = ee.ImageCollection("LANDSAT/LT05/C02/T1_L2") # How many Tier 1 Landsat 5 images have ever been collected? print("All images ever: ", imgCol.size()) # A very large number # How many images were collected in the 2000s? startDate = "2000-01-01" endDate = "2010-01-01" imgColfilteredByDate = imgCol.filterDate(startDate, endDate) print("All images 2000-2010: ", imgColfilteredByDate.size()) # A smaller (but still large) number ShanghaiImage = ee.Image("LANDSAT/LT05/C02/T1_L2/LT05_118038_20000606") Map.centerObject(ShanghaiImage, 9) imgColfilteredByDateHere = imgColfilteredByDate.filterBounds(Map.getCenter()) print("All images here, 2000-2010: ", imgColfilteredByDateHere.size()) L5FilteredLowCloudImages = imgColfilteredByDateHere.filterMetadata( "CLOUD_COVER", "less_than", 50 ) print("Less than 50% clouds in this area, 2000-2010", L5FilteredLowCloudImages.size()) # A smaller number chainedFilteredSet = ( imgCol.filterDate(startDate, endDate) .filterBounds(Map.getCenter()) .filterMetadata("CLOUD_COVER", "less_than", 50) ) print( "Chained: Less than 50% clouds in this area, 2000-2010", chainedFilteredSet.size() ) efficientFilteredSet = ( imgCol.filterBounds(Map.getCenter()) .filterDate(startDate, endDate) .filterMetadata("CLOUD_COVER", "less_than", 50) ) print( "Efficient filtering: Less than 50% clouds in this area, 2000-2010", efficientFilteredSet.size(), ) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- def makeLandsat5EVI(oneL5Image): # compute the EVI for any Landsat 5 image. Note it's specific to # Landsat 5 images due to the band numbers. Don't run this exact # function for images from sensors other than Landsat 5. # Extract the bands and divide by 1e4 to account for scaling done. nirScaled = oneL5Image.select("SR_B4").divide(10000) redScaled = oneL5Image.select("SR_B3").divide(10000) blueScaled = oneL5Image.select("SR_B1").divide(10000) # Calculate the numerator, note that order goes from left to right. numeratorEVI = (nirScaled.subtract(redScaled)).multiply(2.5) # Calculate the denominator denomClause1 = redScaled.multiply(6) denomClause2 = blueScaled.multiply(7.5) denominatorEVI = nirScaled.add(denomClause1).subtract(denomClause2).add(1) # Calculate EVI and name it. landsat5EVI = numeratorEVI.divide(denominatorEVI).rename("EVI") return landsat5EVI L5EVIimages = efficientFilteredSet.map(makeLandsat5EVI) print( "Verifying that the .map gives back the same number of images: ", L5EVIimages.size() ) print(L5EVIimages) Map.addLayer(L5EVIimages, {}, "L5EVIimages", 1, 1) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- L5EVImean = L5EVIimages.reduce(ee.Reducer.mean()) print(L5EVImean) Map.addLayer( L5EVImean, {"min": -1, "max": 2, "palette": ["red", "white", "green"]}, "Mean EVI" ) L5EVImedian = L5EVIimages.reduce(ee.Reducer.median()) print(L5EVImedian) Map.addLayer( L5EVImedian, {"min": -1, "max": 2, "palette": ["red", "white", "green"]}, "Median EVI", ) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map