F47e 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:   F47e
#  Authors:      Paulo Arévalo, Pontus Olofsson
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Load the required libraries.
palettes = require('users/gena/packages:palettes')
utils = require(
    'users/parevalo_bu/gee-ccdc-tools:ccdcUtilities/api')

# Load the results.
resultsPath =
    'projects/gee-book/assets/F4-7/Rondonia_example_small'
ccdResults = ee.Image(resultsPath)
Map.centerObject(ccdResults, 10)

# Convert a date into fractional years.
inputDate = '2005-09-25'
dateParams = {
    'inputFormat': 3,
    'inputDate': inputDate,
    'outputFormat': 1
}
formattedDate = utils.Dates.convertDate(dateParams)

# Band names originally used as inputs to the CCD algorithm.
BANDS = ['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2']

# Names for the time segments to retrieve.
SEGS = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9',
    'S10'
]

# Transform CCD results into a multiband image.
ccdImage = utils.CCDC.buildCcdImage(ccdResults, SEGS.length,
    BANDS)
print(ccdImage)

# Define bands to select.
SELECT_BANDS = ['RED', 'GREEN', 'BLUE', 'NIR']

# Define coefficients to select.
# This list contains all possible coefficients, and the RMSE
SELECT_COEFS = ['INTP', 'SLP', 'RMSE']

# Obtain coefficients.
coefs = utils.CCDC.getMultiCoefs(
    ccdImage, formattedDate, SELECT_BANDS, SELECT_COEFS, True,
    SEGS, 'after')
print(coefs)

# Show a single coefficient.
slpVisParams = {
    'palette': palettes.matplotlib.viridis[7],
    'min': -0.0005,
    'max': 0.005
}
Map.addLayer(coefs.select('RED_SLP'), slpVisParams,
    'RED SLOPE 2005-09-25')

rmseVisParams = {
    'palette': palettes.matplotlib.viridis[7],
    'min': 0,
    'max': 0.1
}
Map.addLayer(coefs.select('NIR_RMSE'), rmseVisParams,
    'NIR RMSE 2005-09-25')

# Show an RGB with three coefficients.
rgbVisParams = {
    'bands': ['RED_INTP', 'GREEN_INTP', 'BLUE_INTP'],
    'min': 0,
    'max': 0.1
}
Map.addLayer(coefs, rgbVisParams, 'RGB 2005-09-25')

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

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F4.7 Interpreting Time Series with CCDC # Checkpoint: F47e # Authors: Paulo Arévalo, Pontus Olofsson # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Load the required libraries. palettes = require('users/gena/packages:palettes') utils = require( 'users/parevalo_bu/gee-ccdc-tools:ccdcUtilities/api') # Load the results. resultsPath = 'projects/gee-book/assets/F4-7/Rondonia_example_small' ccdResults = ee.Image(resultsPath) Map.centerObject(ccdResults, 10) # Convert a date into fractional years. inputDate = '2005-09-25' dateParams = { 'inputFormat': 3, 'inputDate': inputDate, 'outputFormat': 1 } formattedDate = utils.Dates.convertDate(dateParams) # Band names originally used as inputs to the CCD algorithm. BANDS = ['BLUE', 'GREEN', 'RED', 'NIR', 'SWIR1', 'SWIR2'] # Names for the time segments to retrieve. SEGS = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9', 'S10' ] # Transform CCD results into a multiband image. ccdImage = utils.CCDC.buildCcdImage(ccdResults, SEGS.length, BANDS) print(ccdImage) # Define bands to select. SELECT_BANDS = ['RED', 'GREEN', 'BLUE', 'NIR'] # Define coefficients to select. # This list contains all possible coefficients, and the RMSE SELECT_COEFS = ['INTP', 'SLP', 'RMSE'] # Obtain coefficients. coefs = utils.CCDC.getMultiCoefs( ccdImage, formattedDate, SELECT_BANDS, SELECT_COEFS, True, SEGS, 'after') print(coefs) # Show a single coefficient. slpVisParams = { 'palette': palettes.matplotlib.viridis[7], 'min': -0.0005, 'max': 0.005 } Map.addLayer(coefs.select('RED_SLP'), slpVisParams, 'RED SLOPE 2005-09-25') rmseVisParams = { 'palette': palettes.matplotlib.viridis[7], 'min': 0, 'max': 0.1 } Map.addLayer(coefs.select('NIR_RMSE'), rmseVisParams, 'NIR RMSE 2005-09-25') # Show an RGB with three coefficients. rgbVisParams = { 'bands': ['RED_INTP', 'GREEN_INTP', 'BLUE_INTP'], 'min': 0, 'max': 0.1 } Map.addLayer(coefs, rgbVisParams, 'RGB 2005-09-25') # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map