A38b 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:      A3.8 Detecting Land Cover Change in Rangelands
#  Checkpoint:   A38b
#  Authors:      Ginger Allington, Natalie Kreitzer
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Load the shapefile asset for the AOI as a Feature Collection
aoi = ee.FeatureCollection(
    'projects/gee-book/assets/A3-8/GEE_Ch_AOI')
Map.centerObject(aoi, 11)
Map.addLayer(aoi, {}, 'Subset of Naiman Banner')

# Filter the MODIS Collection
MODIS_LC = ee.ImageCollection('MODIS/006/MCD12Q1').select(
    'LC_Type1')

# Function to clip an image from the collection and set the year
def clipCol(img):
    date = ee.String(img.get('system:index'))
    date = date.slice(0, 4)
    return img.select('LC_Type1').clip(aoi) # .clip(aoi) \
        .set('year', date)


# Generate images for diff years you want to compare
modis01 = MODIS_LC.filterDate('2001-01-01', '2002-01-01').map(
    clipCol)
modis09 = MODIS_LC.filterDate('2009-01-01', '2010-01-01').map(
    clipCol)
modis16 = MODIS_LC.filterDate('2016-01-01', '2017-01-01').map(
    clipCol)
# Create an Image for each of the years
modis01 = modis01.first()
modis09 = modis09.first()
modis16 = modis16.first()

Map.addLayer(modis01.randomVisualizer(), {}, 'modis 2001', False)
Map.addLayer(modis09.randomVisualizer(), {}, 'modis 2009', False)
Map.addLayer(modis16.randomVisualizer(), {}, 'modis 2016', False)

# Add and clip the WorldCover data
wCov = ee.ImageCollection('ESA/WorldCover/v100').first()
landcover20 = wCov.clip(aoi)
Map.addLayer(landcover20, {}, 'Landcover 2020')

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

greennessColl = ee.ImageCollection(
    'projects/gee-book/assets/A3-8/GreennessCollection_aoi')
precipColl = ee.ImageCollection(
    'projects/gee-book/assets/A3-8/PrecipCollection')
print(greennessColl, 'Greenness Image Collection')
print(precipColl, 'Precip Image Collection')

greennessParams = {
    'bands': ['greenness'],
    'max': 0.5,
    'min': 0.06,
    'opacity': 1,
    'palette': ['e70808', 'ffffff', '1de22c']
}

greenness1985 = greennessColl.filterDate('1985-01-01',
    '1986-01-01').select('greenness')
greenness1999 = greennessColl.filterDate('1999-01-01',
    '2000-01-01').select('greenness')

print(greenness1999)
greenness2019 = greennessColl.filterDate('2019-01-01',
    '2020-01-01').select('greenness')

Map.addLayer(greenness1985, greennessParams, 'Greenness 1985', False)
Map.addLayer(greenness1999, greennessParams, 'Greenness 1999', False)
Map.addLayer(greenness2019, greennessParams, 'Greenness 2019', False)



# Load a function that will combine the Precipitation and Greenness collections,
# run a regression, then predict NDVI and calculate the residuals.

# Load the module
residFunctions = require(
    'projects/gee-edu/book:Part A - Applications/A3 - Terrestrial Applications/A3.8 Detecting Land Cover Change in Rangelands/modules/calcResid'
)

# Call the function we want that is in that module
# It requires three input parameters:
# the greenness collection, the precipitation collection and the aoi
residualColl = (residFunctions.createResidColl(greennessColl,
    precipColl, aoi))

# Now inspect what you have generated:
print('Module output of residuals', residualColl)

resids = residualColl.first()
res1 = resids.select(['residual'])
print(res1.getInfo(), 'residual image')
Map.addLayer(res1, {
    'min': -0.2,
    'max': 0.2,
    'palette': ['red', 'white', 'green']
}, 'residuals 1985', False)

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A3.8 Detecting Land Cover Change in Rangelands # Checkpoint: A38b # Authors: Ginger Allington, Natalie Kreitzer # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Load the shapefile asset for the AOI as a Feature Collection aoi = ee.FeatureCollection( 'projects/gee-book/assets/A3-8/GEE_Ch_AOI') Map.centerObject(aoi, 11) Map.addLayer(aoi, {}, 'Subset of Naiman Banner') # Filter the MODIS Collection MODIS_LC = ee.ImageCollection('MODIS/006/MCD12Q1').select( 'LC_Type1') # Function to clip an image from the collection and set the year def clipCol(img): date = ee.String(img.get('system:index')) date = date.slice(0, 4) return img.select('LC_Type1').clip(aoi) # .clip(aoi) \ .set('year', date) # Generate images for diff years you want to compare modis01 = MODIS_LC.filterDate('2001-01-01', '2002-01-01').map( clipCol) modis09 = MODIS_LC.filterDate('2009-01-01', '2010-01-01').map( clipCol) modis16 = MODIS_LC.filterDate('2016-01-01', '2017-01-01').map( clipCol) # Create an Image for each of the years modis01 = modis01.first() modis09 = modis09.first() modis16 = modis16.first() Map.addLayer(modis01.randomVisualizer(), {}, 'modis 2001', False) Map.addLayer(modis09.randomVisualizer(), {}, 'modis 2009', False) Map.addLayer(modis16.randomVisualizer(), {}, 'modis 2016', False) # Add and clip the WorldCover data wCov = ee.ImageCollection('ESA/WorldCover/v100').first() landcover20 = wCov.clip(aoi) Map.addLayer(landcover20, {}, 'Landcover 2020') # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- greennessColl = ee.ImageCollection( 'projects/gee-book/assets/A3-8/GreennessCollection_aoi') precipColl = ee.ImageCollection( 'projects/gee-book/assets/A3-8/PrecipCollection') print(greennessColl, 'Greenness Image Collection') print(precipColl, 'Precip Image Collection') greennessParams = { 'bands': ['greenness'], 'max': 0.5, 'min': 0.06, 'opacity': 1, 'palette': ['e70808', 'ffffff', '1de22c'] } greenness1985 = greennessColl.filterDate('1985-01-01', '1986-01-01').select('greenness') greenness1999 = greennessColl.filterDate('1999-01-01', '2000-01-01').select('greenness') print(greenness1999) greenness2019 = greennessColl.filterDate('2019-01-01', '2020-01-01').select('greenness') Map.addLayer(greenness1985, greennessParams, 'Greenness 1985', False) Map.addLayer(greenness1999, greennessParams, 'Greenness 1999', False) Map.addLayer(greenness2019, greennessParams, 'Greenness 2019', False) # Load a function that will combine the Precipitation and Greenness collections, # run a regression, then predict NDVI and calculate the residuals. # Load the module residFunctions = require( 'projects/gee-edu/book:Part A - Applications/A3 - Terrestrial Applications/A3.8 Detecting Land Cover Change in Rangelands/modules/calcResid' ) # Call the function we want that is in that module # It requires three input parameters: # the greenness collection, the precipitation collection and the aoi residualColl = (residFunctions.createResidColl(greennessColl, precipColl, aoi)) # Now inspect what you have generated: print('Module output of residuals', residualColl) resids = residualColl.first() res1 = resids.select(['residual']) print(res1.getInfo(), 'residual image') Map.addLayer(res1, { 'min': -0.2, 'max': 0.2, 'palette': ['red', 'white', 'green'] }, 'residuals 1985', False) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

In [ ]:

Map

Map