A36b 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")

#*** Start of imports. If edited, may not auto-convert in the playground. ***#
geometry =

    # displayProperties: [
      {
        "type": "rectangle"
      }
    ] #
    ee.Geometry.Polygon(
        [[[-112.1088347655006, 38.522463862329126],
          [-112.1088347655006, 38.22315763773188],
          [-111.91520073229748, 38.22315763773188],
          [-111.91520073229748, 38.522463862329126]]], None, False)
#**** End of imports. If edited, may not auto-convert in the playground. ****#
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      A3.6 Working With GPS and Weather Data
#  Checkpoint:   A36b
#  Authors:      Peder Engelstad, Daniel Carver, Nicholas E. Young
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Import the data and add it to the map and print.
cougarF53 = ee.FeatureCollection(
    'projects/gee-book/assets/A3-6/cougarF53')

Map.centerObject(cougarF53, 10)

Map.addLayer(cougarF53, {}, 'cougar presence data')

print(cougarF53, 'cougar data')

# Call in image collection and filter.
Daymet = ee.ImageCollection('NASA/ORNL/DAYMET_V4') \
    .filterDate('2014-02-11', '2014-11-02') \
    .filterBounds(geometry)

def func_gqy(image):
        return image.clip(geometry) \
    .map(func_gqy)




print(Daymet, 'Daymet')

# Convert to a multiband image.
DaymetImage = Daymet.toBands()

print(DaymetImage, 'DaymetImage')

# Call the sample regions function.
samples = DaymetImage.sampleRegions({
    'collection': cougarF53,
    'properties': ['id'],
    'scale': 1000
})

print(samples, 'samples')

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

# Export value added data to your Google Drive.
Export.table.toDrive({
    'collection': samples,
    'description': 'cougarDaymetToDriveExample',
    'fileFormat': 'csv'
})

# Apply a median reducer to the dataset.
daymet1 = Daymet \
    .median() \
    .clip(geometry)

print(daymet1)

# Export the image to drive.
Export.image.toDrive({
    'image': daymet1,
    'description': 'MedianValueForStudyArea',
    'scale': 1000,
    'region': geometry,
    'maxPixels': 1e9
})

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") #*** Start of imports. If edited, may not auto-convert in the playground. ***# geometry = # displayProperties: [ { "type": "rectangle" } ] # ee.Geometry.Polygon( [[[-112.1088347655006, 38.522463862329126], [-112.1088347655006, 38.22315763773188], [-111.91520073229748, 38.22315763773188], [-111.91520073229748, 38.522463862329126]]], None, False) #**** End of imports. If edited, may not auto-convert in the playground. ****# # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A3.6 Working With GPS and Weather Data # Checkpoint: A36b # Authors: Peder Engelstad, Daniel Carver, Nicholas E. Young # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Import the data and add it to the map and print. cougarF53 = ee.FeatureCollection( 'projects/gee-book/assets/A3-6/cougarF53') Map.centerObject(cougarF53, 10) Map.addLayer(cougarF53, {}, 'cougar presence data') print(cougarF53, 'cougar data') # Call in image collection and filter. Daymet = ee.ImageCollection('NASA/ORNL/DAYMET_V4') \ .filterDate('2014-02-11', '2014-11-02') \ .filterBounds(geometry) def func_gqy(image): return image.clip(geometry) \ .map(func_gqy) print(Daymet, 'Daymet') # Convert to a multiband image. DaymetImage = Daymet.toBands() print(DaymetImage, 'DaymetImage') # Call the sample regions function. samples = DaymetImage.sampleRegions({ 'collection': cougarF53, 'properties': ['id'], 'scale': 1000 }) print(samples, 'samples') # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Export value added data to your Google Drive. Export.table.toDrive({ 'collection': samples, 'description': 'cougarDaymetToDriveExample', 'fileFormat': 'csv' }) # Apply a median reducer to the dataset. daymet1 = Daymet \ .median() \ .clip(geometry) print(daymet1) # Export the image to drive. Export.image.toDrive({ 'image': daymet1, 'description': 'MedianValueForStudyArea', 'scale': 1000, 'region': geometry, 'maxPixels': 1e9 }) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map