F41d Checkpoint

Import libraries¶

In [ ]:

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:      F4.1 Exploring Image Collections
#  Checkpoint:   F41d
#  Author:       Gennadii Donchyts
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Define a region of interest as a point in Lisbon, Portugal.
lisbonPoint = ee.Geometry.Point(-9.179473, 38.763948)

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

# filter the large ImageCollection to be just images from 2020
# around Lisbon. From each image, select True-color bands to draw
filteredIC = (
    ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
    .filterDate("2020-01-01", "2021-01-01")
    .filterBounds(lisbonPoint)
    .select(["B6", "B5", "B4"])
)

# Add the filtered ImageCollection so that we can inspect values
# via the Inspector tool
Map.addLayer(filteredIC, {}, "TOA image collection")

# Construct a chart using values queried from image collection.
chart = ui.Chart.image.series(
    {
        "imageCollection": filteredIC,
        "region": lisbonPoint,
        "reducer": ee.Reducer.first(),
        "scale": 10,
    }
)

# Show the chart in the Console.
print(chart)

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

# compute and show the number of observations in an image collection
count = (
    ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
    .filterDate("2020-01-01", "2021-01-01")
    .select(["B6"])
    .count()
)

# add white background and switch to HYBRID basemap
Map.addLayer(ee.Image(1), {"palette": ["white"]}, "white", True, 0.5)
Map.setOptions("HYBRID")

# show image count
Map.addLayer(
    count,
    {
        "min": 0,
        "max": 50,
        "palette": ["d7191c", "fdae61", "ffffbf", "a6d96a", "1a9641"],
    },
    "landsat 8 image count (2020)",
)

# Center the map at that point.
Map.centerObject(lisbonPoint, 5)

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

# Zoom to an informative scale for the code that follows.
Map.centerObject(lisbonPoint, 10)

# Add a mean composite image.
meanFilteredIC = filteredIC.reduce(ee.Reducer.mean())
Map.addLayer(meanFilteredIC, {}, "Mean values within image collection")

# Add a median composite image.
medianFilteredIC = filteredIC.reduce(ee.Reducer.median())
Map.addLayer(medianFilteredIC, {}, "Median values within image collection")

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

# compute a single 30% percentile
p30 = filteredIC.reduce(ee.Reducer.percentile([30]))

Map.addLayer(p30, {"min": 0.05, "max": 0.35}, "30%")

percentiles = [0, 10, 20, 30, 40, 50, 60, 70, 80]

# let's compute percentile images and add them as separate layers


def func_sax(p):
    image = filteredIC.reduce(ee.Reducer.percentile([p]))
    Map.addLayer(image, {"min": 0.05, "max": 0.35}, p + "%")


percentiles.map(func_sax)


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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F4.1 Exploring Image Collections # Checkpoint: F41d # Author: Gennadii Donchyts # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Define a region of interest as a point in Lisbon, Portugal. lisbonPoint = ee.Geometry.Point(-9.179473, 38.763948) # Center the map at that point. Map.centerObject(lisbonPoint, 16) # filter the large ImageCollection to be just images from 2020 # around Lisbon. From each image, select True-color bands to draw filteredIC = ( ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") .filterDate("2020-01-01", "2021-01-01") .filterBounds(lisbonPoint) .select(["B6", "B5", "B4"]) ) # Add the filtered ImageCollection so that we can inspect values # via the Inspector tool Map.addLayer(filteredIC, {}, "TOA image collection") # Construct a chart using values queried from image collection. chart = ui.Chart.image.series( { "imageCollection": filteredIC, "region": lisbonPoint, "reducer": ee.Reducer.first(), "scale": 10, } ) # Show the chart in the Console. print(chart) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # compute and show the number of observations in an image collection count = ( ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA") .filterDate("2020-01-01", "2021-01-01") .select(["B6"]) .count() ) # add white background and switch to HYBRID basemap Map.addLayer(ee.Image(1), {"palette": ["white"]}, "white", True, 0.5) Map.setOptions("HYBRID") # show image count Map.addLayer( count, { "min": 0, "max": 50, "palette": ["d7191c", "fdae61", "ffffbf", "a6d96a", "1a9641"], }, "landsat 8 image count (2020)", ) # Center the map at that point. Map.centerObject(lisbonPoint, 5) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Zoom to an informative scale for the code that follows. Map.centerObject(lisbonPoint, 10) # Add a mean composite image. meanFilteredIC = filteredIC.reduce(ee.Reducer.mean()) Map.addLayer(meanFilteredIC, {}, "Mean values within image collection") # Add a median composite image. medianFilteredIC = filteredIC.reduce(ee.Reducer.median()) Map.addLayer(medianFilteredIC, {}, "Median values within image collection") # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # compute a single 30% percentile p30 = filteredIC.reduce(ee.Reducer.percentile([30])) Map.addLayer(p30, {"min": 0.05, "max": 0.35}, "30%") percentiles = [0, 10, 20, 30, 40, 50, 60, 70, 80] # let's compute percentile images and add them as separate layers def func_sax(p): image = filteredIC.reduce(ee.Reducer.percentile([p])) Map.addLayer(image, {"min": 0.05, "max": 0.35}, p + "%") percentiles.map(func_sax) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

In [ ]:

Map

Map