F47b 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:      F4.7 Interpreting Time Series with CCDC
#  Checkpoint:   F47b
#  Authors:      Paulo Arévalo, Pontus Olofsson
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

utils = require(
    'users/parevalo_bu/gee-ccdc-tools:ccdcUtilities/api')

studyRegion = ee.Geometry.Rectangle([
    [-63.9533, -10.1315],
    [-64.9118, -10.6813]
])

# Define start, end dates and Landsat bands to use.
startDate = '2000-01-01'
endDate = '2020-01-01'
bands = ['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2']

# Retrieve all clear, Landsat 4, 5, 7 and 8 observations (Collection 2, Tier 1).
filteredLandsat = utils.Inputs.getLandsat({
        'collection': 2
    }) \
    .filterBounds(studyRegion) \
    .filterDate(startDate, endDate) \
    .select(bands)

print(filteredLandsat.first())

# Set CCD params to use.
ccdParams = {
    'breakpointBands': ['GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'],
    'tmaskBands': ['GREEN', 'SWIR1'],
    'minObservations': 6,
    'chiSquareProbability': 0.99,
    'minNumOfYearsScaler': 1.33,
    'dateFormat': 1,
    'lambda': 0.002,
    'maxIterations': 10000,
    'collection': filteredLandsat
}

# Run CCD.
ccdResults = ee.Algorithms.TemporalSegmentation.Ccdc(ccdParams)
print(ccdResults)

exportResults = False
if (exportResults) {
    # Create a metadata dictionary with the parameters and arguments used.
    metadata = ccdParams
    metadata['breakpointBands'] = metadata['breakpointBands'].toString()
    metadata['tmaskBands'] = metadata['tmaskBands'].toString()
    metadata['startDate'] = startDate
    metadata['endDate'] = endDate
    metadata['bands'] = bands.toString()

    # Export results, assigning the metadata as image properties.
    #
    Export.image.toAsset({
        'image': ccdResults.set(metadata),
        'region': studyRegion,
        'pyramidingPolicy': {
            ".default": 'sample'
        },
        'scale': 30
    })
}

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F4.7 Interpreting Time Series with CCDC # Checkpoint: F47b # Authors: Paulo Arévalo, Pontus Olofsson # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ utils = require( 'users/parevalo_bu/gee-ccdc-tools:ccdcUtilities/api') studyRegion = ee.Geometry.Rectangle([ [-63.9533, -10.1315], [-64.9118, -10.6813] ]) # Define start, end dates and Landsat bands to use. startDate = '2000-01-01' endDate = '2020-01-01' bands = ['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'] # Retrieve all clear, Landsat 4, 5, 7 and 8 observations (Collection 2, Tier 1). filteredLandsat = utils.Inputs.getLandsat({ 'collection': 2 }) \ .filterBounds(studyRegion) \ .filterDate(startDate, endDate) \ .select(bands) print(filteredLandsat.first()) # Set CCD params to use. ccdParams = { 'breakpointBands': ['GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'], 'tmaskBands': ['GREEN', 'SWIR1'], 'minObservations': 6, 'chiSquareProbability': 0.99, 'minNumOfYearsScaler': 1.33, 'dateFormat': 1, 'lambda': 0.002, 'maxIterations': 10000, 'collection': filteredLandsat } # Run CCD. ccdResults = ee.Algorithms.TemporalSegmentation.Ccdc(ccdParams) print(ccdResults) exportResults = False if (exportResults) { # Create a metadata dictionary with the parameters and arguments used. metadata = ccdParams metadata['breakpointBands'] = metadata['breakpointBands'].toString() metadata['tmaskBands'] = metadata['tmaskBands'].toString() metadata['startDate'] = startDate metadata['endDate'] = endDate metadata['bands'] = bands.toString() # Export results, assigning the metadata as image properties. # Export.image.toAsset({ 'image': ccdResults.set(metadata), 'region': studyRegion, 'pyramidingPolicy': { ".default": 'sample' }, 'scale': 30 }) } # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map