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Nested loop in time series change detection in Google Earth Engine?


How to calculate forest loss in google earth engineCreating a area time series chart on Google Earth EngineGoogle Earth Engine, how to distinguish between rivers/streams and ponds/lakes in a water maskTemporal segmentation algorithms using Google Earth EngineCreate time series for multi-polygon Google Earth EngineRegarding Time Series analysis in earth engineExtracting pixel time series from Google Earth EngineEarth Engine loopObject detection in Google Earth EngineDoing change detection of land cover between 2 landsat images in google earth engine?






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;








0















I want to execute Continuous Change Detection and Classification(CCDC, Zhu&Woodcock, 2014) to all pixels in a given region in GEE.



The first step is to build an image collection containing all landsat images according to the path and row number assigned with. Here is my code:



# Define the path and row
path = 120
row = 38

# Define the start and finish time
start = ee.Date.fromYMD(1983, 1, 1)
finish = ee.Date.fromYMD(2019, 1, 1)

# Select Landsat bands respectively as their different band configurations
l8_bandlist = ee.List(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'pixel_qa'])
l7_bandlist = ee.List(['B1', 'B2', 'B3', 'B4', 'B5', 'B7', 'B6', 'pixel_qa'])

# Unified the band names in the collection
rename_list = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb', 'qa'])

# Function to rename the band names of images in the collection
def __renamelandsatbands(img):
return img.select(
old_bandname,
new_bandname
)


# Function to calculate the evi
def __calculateEVI(img):
index = img.expression(
'2.5 * ((NIR-RED) / (NIR +6 * RED -7.5* BLUE))',
'NIR': img.select('nir'),
'RED': img.select('red'),
'BLUE': img.select('blue')
).set('system:time_start', img.get('system:time_start'))
return img.addBands(index)

# Function to calculate the NBRT
def __calculateNBRT(img):
index = img.expression(
'(NIR - 0.1 * SWIR * Temp) / (NIR + 0.1 * SWIR * Temp)',
'NIR': img.select('nir'),
'SWIR': img.select('swir1'),
'Temp': img.select('tbb')
).set('system:time_start', img.get('system:time_start'))
return img.addBands(index)

#Landsat Collection
l8_sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filter(
ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
start, finish).select(l8_bandlist, rename_list).sort('system:time_start')

l7_sr = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').filter(
ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
start, finish).select(l7_bandlist, rename_list).sort('system:time_start')

l5_sr = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR').filter(
ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
start, finish).select(l7_bandlist, rename_list).sort('system:time_start')

# Add EVI and NBRT into Landsat SR dataset
#OLI
l8_srevi = l8_sr.map(__calculateEVI)
l8_dataset = l8_srevi.map(__calculateNBRT)

#ETM+
l7_srevi = l7_sr.map(__calculateEVI)
l7_dataset = l7_srevi.map(__calculateNBRT)

# TM
l5_srevi = l5_sr.map(__calculateEVI)
l5_dataset = l5_srevi.map(__calculateNBRT)

# Rename images again
old_bandname = rename_list.add('constant').add('nir_1')
new_bandname = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb', 'qa', 'evi', 'nbrt'])
l8_dataset = l8_dataset.map(__renamelandsatbands)
l7_dataset = l7_dataset.map(__renamelandsatbands)
l5_dataset = l5_dataset.map(__renamelandsatbands)

# Stack landsat series image collection
lcdataset = l8_dataset.merge(l7_dataset).merge(l5_dataset).sort('system:time_start')


According to these code above, i made a Landsat series image collection which contains all TM/ETM+/OLI images in path 120 and row 38. Each of the image contains ten bands, including seven surface reflectance bands('blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb'), two derived feature bands('EVI', ‘NBRT') and one quality control band('qa').



After the data get prepared, What i want to do is to get the every pixel clear observation time series of all seven SR bands and the two derived feature bands which filtered by the qa band. Then, for every pixel, the first 24 observations are used to initialize a regression time series model, the break points detection start from the 25th clear observation based on a specific threshold determined by 3 folds of the RMSE of the regression model . The 25th observation will be identified as a break pointexceed if it exceeds the threshold, and the next 24 clear observations will be used to initialize a new model again. If the 25th observation is in the threshold, it will be joined into the first 24 observations to update the initial model, and the 26th observation will be assessed until all clear observations of the time series been checked.



AS i introduced above, the algorithm is based on pixel scale and use circulative iteration of nine time series trajectorys to find the break point in each pixel. In other words, using CCDC to an image collection facing two iterations, the time series break points iterative evaluation and loop this algorithm pixel by pixel to the whole image coverage. Is there a best way to do this in GEE?










share|improve this question






























    0















    I want to execute Continuous Change Detection and Classification(CCDC, Zhu&Woodcock, 2014) to all pixels in a given region in GEE.



    The first step is to build an image collection containing all landsat images according to the path and row number assigned with. Here is my code:



    # Define the path and row
    path = 120
    row = 38

    # Define the start and finish time
    start = ee.Date.fromYMD(1983, 1, 1)
    finish = ee.Date.fromYMD(2019, 1, 1)

    # Select Landsat bands respectively as their different band configurations
    l8_bandlist = ee.List(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'pixel_qa'])
    l7_bandlist = ee.List(['B1', 'B2', 'B3', 'B4', 'B5', 'B7', 'B6', 'pixel_qa'])

    # Unified the band names in the collection
    rename_list = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb', 'qa'])

    # Function to rename the band names of images in the collection
    def __renamelandsatbands(img):
    return img.select(
    old_bandname,
    new_bandname
    )


    # Function to calculate the evi
    def __calculateEVI(img):
    index = img.expression(
    '2.5 * ((NIR-RED) / (NIR +6 * RED -7.5* BLUE))',
    'NIR': img.select('nir'),
    'RED': img.select('red'),
    'BLUE': img.select('blue')
    ).set('system:time_start', img.get('system:time_start'))
    return img.addBands(index)

    # Function to calculate the NBRT
    def __calculateNBRT(img):
    index = img.expression(
    '(NIR - 0.1 * SWIR * Temp) / (NIR + 0.1 * SWIR * Temp)',
    'NIR': img.select('nir'),
    'SWIR': img.select('swir1'),
    'Temp': img.select('tbb')
    ).set('system:time_start', img.get('system:time_start'))
    return img.addBands(index)

    #Landsat Collection
    l8_sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filter(
    ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
    start, finish).select(l8_bandlist, rename_list).sort('system:time_start')

    l7_sr = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').filter(
    ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
    start, finish).select(l7_bandlist, rename_list).sort('system:time_start')

    l5_sr = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR').filter(
    ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
    start, finish).select(l7_bandlist, rename_list).sort('system:time_start')

    # Add EVI and NBRT into Landsat SR dataset
    #OLI
    l8_srevi = l8_sr.map(__calculateEVI)
    l8_dataset = l8_srevi.map(__calculateNBRT)

    #ETM+
    l7_srevi = l7_sr.map(__calculateEVI)
    l7_dataset = l7_srevi.map(__calculateNBRT)

    # TM
    l5_srevi = l5_sr.map(__calculateEVI)
    l5_dataset = l5_srevi.map(__calculateNBRT)

    # Rename images again
    old_bandname = rename_list.add('constant').add('nir_1')
    new_bandname = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb', 'qa', 'evi', 'nbrt'])
    l8_dataset = l8_dataset.map(__renamelandsatbands)
    l7_dataset = l7_dataset.map(__renamelandsatbands)
    l5_dataset = l5_dataset.map(__renamelandsatbands)

    # Stack landsat series image collection
    lcdataset = l8_dataset.merge(l7_dataset).merge(l5_dataset).sort('system:time_start')


    According to these code above, i made a Landsat series image collection which contains all TM/ETM+/OLI images in path 120 and row 38. Each of the image contains ten bands, including seven surface reflectance bands('blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb'), two derived feature bands('EVI', ‘NBRT') and one quality control band('qa').



    After the data get prepared, What i want to do is to get the every pixel clear observation time series of all seven SR bands and the two derived feature bands which filtered by the qa band. Then, for every pixel, the first 24 observations are used to initialize a regression time series model, the break points detection start from the 25th clear observation based on a specific threshold determined by 3 folds of the RMSE of the regression model . The 25th observation will be identified as a break pointexceed if it exceeds the threshold, and the next 24 clear observations will be used to initialize a new model again. If the 25th observation is in the threshold, it will be joined into the first 24 observations to update the initial model, and the 26th observation will be assessed until all clear observations of the time series been checked.



    AS i introduced above, the algorithm is based on pixel scale and use circulative iteration of nine time series trajectorys to find the break point in each pixel. In other words, using CCDC to an image collection facing two iterations, the time series break points iterative evaluation and loop this algorithm pixel by pixel to the whole image coverage. Is there a best way to do this in GEE?










    share|improve this question


























      0












      0








      0








      I want to execute Continuous Change Detection and Classification(CCDC, Zhu&Woodcock, 2014) to all pixels in a given region in GEE.



      The first step is to build an image collection containing all landsat images according to the path and row number assigned with. Here is my code:



      # Define the path and row
      path = 120
      row = 38

      # Define the start and finish time
      start = ee.Date.fromYMD(1983, 1, 1)
      finish = ee.Date.fromYMD(2019, 1, 1)

      # Select Landsat bands respectively as their different band configurations
      l8_bandlist = ee.List(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'pixel_qa'])
      l7_bandlist = ee.List(['B1', 'B2', 'B3', 'B4', 'B5', 'B7', 'B6', 'pixel_qa'])

      # Unified the band names in the collection
      rename_list = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb', 'qa'])

      # Function to rename the band names of images in the collection
      def __renamelandsatbands(img):
      return img.select(
      old_bandname,
      new_bandname
      )


      # Function to calculate the evi
      def __calculateEVI(img):
      index = img.expression(
      '2.5 * ((NIR-RED) / (NIR +6 * RED -7.5* BLUE))',
      'NIR': img.select('nir'),
      'RED': img.select('red'),
      'BLUE': img.select('blue')
      ).set('system:time_start', img.get('system:time_start'))
      return img.addBands(index)

      # Function to calculate the NBRT
      def __calculateNBRT(img):
      index = img.expression(
      '(NIR - 0.1 * SWIR * Temp) / (NIR + 0.1 * SWIR * Temp)',
      'NIR': img.select('nir'),
      'SWIR': img.select('swir1'),
      'Temp': img.select('tbb')
      ).set('system:time_start', img.get('system:time_start'))
      return img.addBands(index)

      #Landsat Collection
      l8_sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filter(
      ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
      start, finish).select(l8_bandlist, rename_list).sort('system:time_start')

      l7_sr = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').filter(
      ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
      start, finish).select(l7_bandlist, rename_list).sort('system:time_start')

      l5_sr = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR').filter(
      ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
      start, finish).select(l7_bandlist, rename_list).sort('system:time_start')

      # Add EVI and NBRT into Landsat SR dataset
      #OLI
      l8_srevi = l8_sr.map(__calculateEVI)
      l8_dataset = l8_srevi.map(__calculateNBRT)

      #ETM+
      l7_srevi = l7_sr.map(__calculateEVI)
      l7_dataset = l7_srevi.map(__calculateNBRT)

      # TM
      l5_srevi = l5_sr.map(__calculateEVI)
      l5_dataset = l5_srevi.map(__calculateNBRT)

      # Rename images again
      old_bandname = rename_list.add('constant').add('nir_1')
      new_bandname = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb', 'qa', 'evi', 'nbrt'])
      l8_dataset = l8_dataset.map(__renamelandsatbands)
      l7_dataset = l7_dataset.map(__renamelandsatbands)
      l5_dataset = l5_dataset.map(__renamelandsatbands)

      # Stack landsat series image collection
      lcdataset = l8_dataset.merge(l7_dataset).merge(l5_dataset).sort('system:time_start')


      According to these code above, i made a Landsat series image collection which contains all TM/ETM+/OLI images in path 120 and row 38. Each of the image contains ten bands, including seven surface reflectance bands('blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb'), two derived feature bands('EVI', ‘NBRT') and one quality control band('qa').



      After the data get prepared, What i want to do is to get the every pixel clear observation time series of all seven SR bands and the two derived feature bands which filtered by the qa band. Then, for every pixel, the first 24 observations are used to initialize a regression time series model, the break points detection start from the 25th clear observation based on a specific threshold determined by 3 folds of the RMSE of the regression model . The 25th observation will be identified as a break pointexceed if it exceeds the threshold, and the next 24 clear observations will be used to initialize a new model again. If the 25th observation is in the threshold, it will be joined into the first 24 observations to update the initial model, and the 26th observation will be assessed until all clear observations of the time series been checked.



      AS i introduced above, the algorithm is based on pixel scale and use circulative iteration of nine time series trajectorys to find the break point in each pixel. In other words, using CCDC to an image collection facing two iterations, the time series break points iterative evaluation and loop this algorithm pixel by pixel to the whole image coverage. Is there a best way to do this in GEE?










      share|improve this question
















      I want to execute Continuous Change Detection and Classification(CCDC, Zhu&Woodcock, 2014) to all pixels in a given region in GEE.



      The first step is to build an image collection containing all landsat images according to the path and row number assigned with. Here is my code:



      # Define the path and row
      path = 120
      row = 38

      # Define the start and finish time
      start = ee.Date.fromYMD(1983, 1, 1)
      finish = ee.Date.fromYMD(2019, 1, 1)

      # Select Landsat bands respectively as their different band configurations
      l8_bandlist = ee.List(['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'pixel_qa'])
      l7_bandlist = ee.List(['B1', 'B2', 'B3', 'B4', 'B5', 'B7', 'B6', 'pixel_qa'])

      # Unified the band names in the collection
      rename_list = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb', 'qa'])

      # Function to rename the band names of images in the collection
      def __renamelandsatbands(img):
      return img.select(
      old_bandname,
      new_bandname
      )


      # Function to calculate the evi
      def __calculateEVI(img):
      index = img.expression(
      '2.5 * ((NIR-RED) / (NIR +6 * RED -7.5* BLUE))',
      'NIR': img.select('nir'),
      'RED': img.select('red'),
      'BLUE': img.select('blue')
      ).set('system:time_start', img.get('system:time_start'))
      return img.addBands(index)

      # Function to calculate the NBRT
      def __calculateNBRT(img):
      index = img.expression(
      '(NIR - 0.1 * SWIR * Temp) / (NIR + 0.1 * SWIR * Temp)',
      'NIR': img.select('nir'),
      'SWIR': img.select('swir1'),
      'Temp': img.select('tbb')
      ).set('system:time_start', img.get('system:time_start'))
      return img.addBands(index)

      #Landsat Collection
      l8_sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filter(
      ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
      start, finish).select(l8_bandlist, rename_list).sort('system:time_start')

      l7_sr = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').filter(
      ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
      start, finish).select(l7_bandlist, rename_list).sort('system:time_start')

      l5_sr = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR').filter(
      ee.Filter.eq('WRS_PATH', path)).filter(ee.Filter.eq('WRS_ROW', row)).filterDate(
      start, finish).select(l7_bandlist, rename_list).sort('system:time_start')

      # Add EVI and NBRT into Landsat SR dataset
      #OLI
      l8_srevi = l8_sr.map(__calculateEVI)
      l8_dataset = l8_srevi.map(__calculateNBRT)

      #ETM+
      l7_srevi = l7_sr.map(__calculateEVI)
      l7_dataset = l7_srevi.map(__calculateNBRT)

      # TM
      l5_srevi = l5_sr.map(__calculateEVI)
      l5_dataset = l5_srevi.map(__calculateNBRT)

      # Rename images again
      old_bandname = rename_list.add('constant').add('nir_1')
      new_bandname = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb', 'qa', 'evi', 'nbrt'])
      l8_dataset = l8_dataset.map(__renamelandsatbands)
      l7_dataset = l7_dataset.map(__renamelandsatbands)
      l5_dataset = l5_dataset.map(__renamelandsatbands)

      # Stack landsat series image collection
      lcdataset = l8_dataset.merge(l7_dataset).merge(l5_dataset).sort('system:time_start')


      According to these code above, i made a Landsat series image collection which contains all TM/ETM+/OLI images in path 120 and row 38. Each of the image contains ten bands, including seven surface reflectance bands('blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'tbb'), two derived feature bands('EVI', ‘NBRT') and one quality control band('qa').



      After the data get prepared, What i want to do is to get the every pixel clear observation time series of all seven SR bands and the two derived feature bands which filtered by the qa band. Then, for every pixel, the first 24 observations are used to initialize a regression time series model, the break points detection start from the 25th clear observation based on a specific threshold determined by 3 folds of the RMSE of the regression model . The 25th observation will be identified as a break pointexceed if it exceeds the threshold, and the next 24 clear observations will be used to initialize a new model again. If the 25th observation is in the threshold, it will be joined into the first 24 observations to update the initial model, and the 26th observation will be assessed until all clear observations of the time series been checked.



      AS i introduced above, the algorithm is based on pixel scale and use circulative iteration of nine time series trajectorys to find the break point in each pixel. In other words, using CCDC to an image collection facing two iterations, the time series break points iterative evaluation and loop this algorithm pixel by pixel to the whole image coverage. Is there a best way to do this in GEE?







      python google-earth-engine change-detection






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Apr 6 at 4:16







      myzhenghr

















      asked Apr 4 at 9:21









      myzhenghrmyzhenghr

      32




      32




















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