Error in code #269

pardhasreechaitnya · 2021-01-21T05:34:44Z

pardhasreechaitnya
Jan 21, 2021

Hello Community !

Greetings,

I converted my JavaScript code to python while I am running the python code it is showing error. How can i overcome that error?

Answered by ErikSeras

Jan 23, 2021

This code I tested in Google Colab.

import ee
import geemap

Map = geemap.Map()
ee.Initialize()

Map.add_basemap(basemap='HYBRID')

L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")

roi =ee.Geometry.Polygon(
    [[
        [78.36456276631178, 13.52224244688901],
        [79.99053932881178, 13.564965726101205],
        [79.81475807881178, 16.703679438038012],
        [77.63946511006178, 16.324487178056735]
    ]]
)


def maskL8sr(image):
    cloudShadowBitMask = ee.Number(2).pow(3).int()
    cloudsBitMask = ee.Number(2).pow(5).int()
    qa = image.select('pixel_qa')
    mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0).And(qa.bitwiseAnd(cloudsBitMask).eq(0))
    return image.updateMask(mask).d…

View full answer

giswqs · 2021-01-21T13:32:36Z

giswqs
Jan 21, 2021
Maintainer

Hard to tell from the screenshot. Please share your complete source code.

2 replies

pardhasreechaitnya Jan 23, 2021
Author

import ee
import geemap

Map = geemap.Map()
ee.Initialize()

L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR"),
roi =ee.Geometry.Polygon(
        [[[78.36456276631178, 13.52224244688901],
          [79.99053932881178, 13.564965726101205],
          [79.81475807881178, 16.703679438038012],
          [77.63946511006178, 16.324487178056735]]])
def maskL8sr(image):
  # Bits 3 and 5 are cloud shadow and cloud, respectively.
  cloudShadowBitMask = (1 << 3)
  cloudsBitMask = (1 << 5)
  # Get the pixel QA band.
  qa = image.select('pixel_qa')
  # Both flags should be set to zero, indicating clear conditions.
  mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0) \
                 .And(qa.bitwiseAnd(cloudsBitMask).eq(0))
  return image.updateMask(mask)

rgb_vis = {'min':0, 'max':3000, 'gamma': 1.4, 'bands':['B5','B4','B3']}
filtered = L8.filterDate('2016-03-01', '2016-05-31') \
                 .filterBounds(roi)

median = filtered.median()
clipped = median.clip(roi)
#image = ee.Image(filtered.sum())
Pre_ndvi = clipped.expression(
        "(NIR - RED) / (NIR + RED)",
        {
          'RED': clipped.select("B4"),    #  RED
          'NIR': clipped.select("B5"),    # NIR
          #BLUE: image.select("B2")    # BLUE
        })
Pre_NBR = clipped.expression(
                "(NIR - SWIR2)/(NIR + SWIR2)",
                {
                  'NIR' : clipped.select("B5"), # NIR
                  'SWIR2' : clipped.select("B7"), #SWIR2
                })
Map.addLayer(clipped, rgb_vis, 'RGB')
Map.addLayer(Pre_ndvi, {'min':-0.2, 'max':0.8}, 'Pre_NDVI')
Map.addLayer(Pre_NBR, {'min':-0.5, 'max':1.3}, 'Pre_NBR')
rgb_vis1 = {'min':0, 'max':3000, 'gamma': 1.4, 'bands':['B5','B4','B3']}
# Post Monsoon dates
filtered_1 = L8.filterDate('2016-10-01', '2016-11-30') \
                 .filterBounds(roi)
median = filtered_1.median()
clipped = median.clip(roi)
#image = ee.Image(filtered.sum())
# Calculating Post_NDVI
Post_ndvi = clipped.expression(
        "(NIR - RED) / (NIR + RED)",
        {
          'RED': clipped.select("B4"),    #  RED
          'NIR': clipped.select("B5"),    # NIR
          #BLUE: image.select("B2")    # BLUE
        })
# Calculating Post_NBR
Post_NBR = clipped.expression(
                "(NIR - SWIR2)/(NIR + SWIR2)",
                {
                  'NIR' : clipped.select("B5"), # NIR
                  'SWIR2' : clipped.select("B7"), #SWIR2
                })
Map.addLayer(clipped, rgb_vis, 'RGB')
Map.addLayer(Post_ndvi, {'min':-0.2, 'max':0.8}, 'Post_NDVI')
Map.addLayer(Post_NBR, {'min':-0.5, 'max':1.3}, 'Post_NBR')

# Difference in NDVI
Dndvi = Pre_ndvi.subtract(Post_ndvi)
# Difference in NBR
dNBR = Pre_NBR.subtract(Post_NBR)

Map.addLayer(Dndvi,{'min':0, 'max':1},'DNDVI')
Map.addLayer(dNBR, {'min':0, 'max':1}, 'dNBR')
RBR = (dNBR/Pre_NBR) +1.001
RBR = clipped.expression(
                "(a)/(b) + 1.001",
                {
                  'a' : dNBR,
                  'b' : Pre_NBR,
                })
Map.addLayer(RBR,{'min':0, 'max':1},'RBR')
Map

pardhasreechaitnya Jan 23, 2021
Author

while I am running the above code it is showing error that tuple object has no attribute filterDate.

ErikSeras · 2021-01-23T10:59:33Z

ErikSeras
Jan 23, 2021

This code I tested in Google Colab.

import ee
import geemap

Map = geemap.Map()
ee.Initialize()

Map.add_basemap(basemap='HYBRID')

L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")

roi =ee.Geometry.Polygon(
    [[
        [78.36456276631178, 13.52224244688901],
        [79.99053932881178, 13.564965726101205],
        [79.81475807881178, 16.703679438038012],
        [77.63946511006178, 16.324487178056735]
    ]]
)


def maskL8sr(image):
    cloudShadowBitMask = ee.Number(2).pow(3).int()
    cloudsBitMask = ee.Number(2).pow(5).int()
    qa = image.select('pixel_qa')
    mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0).And(qa.bitwiseAnd(cloudsBitMask).eq(0))
    return image.updateMask(mask).divide(10000).copyProperties(image, ["system:time_start"])

rgb_vis = {'min':0, 'max':0.3, 'gamma': 1.4, 'bands':['B5','B4','B3']}

filtered = L8.filterDate("2016-03-01", "2016-05-31").filterBounds(roi).map(maskL8sr)

median = filtered.median()
clipped = median.clip(roi)

#image = ee.Image(filtered.sum())
Pre_ndvi = clipped.expression(
    "(NIR - RED) / (NIR + RED)",
    {
        'RED': clipped.select("B4"), # RED
        'NIR': clipped.select("B5") # NIR
    })

Pre_NBR = clipped.expression(
    "(NIR - SWIR2)/(NIR + SWIR2)",
    {
        'NIR' : clipped.select("B5"), # NIR
        'SWIR2' : clipped.select("B7") #SWIR2
    })

Map.addLayer(clipped, rgb_vis, 'RGB', 0)
Map.addLayer(Pre_ndvi, {'min':-0.2, 'max':0.8}, 'Pre_NDVI', 0)
Map.addLayer(Pre_NBR, {'min':-0.5, 'max':1.3}, 'Pre_NBR', 0)


rgb_vis1 = {'min':0, 'max':0.3, 'gamma': 1.4, 'bands':['B5','B4','B3']}

# Post Monsoon dates

filtered_1 = L8.filterDate('2016-10-01', '2016-11-30').filterBounds(roi).map(maskL8sr)
median_1 = filtered_1.median()
post_clipped = median_1.clip(roi)
#image = ee.Image(filtered.sum())

# Calculating Post_NDVI
Post_ndvi = post_clipped.expression(
    "(NIR - RED) / (NIR + RED)",
    {
        'RED': post_clipped.select("B4"), # RED
        'NIR': post_clipped.select("B5"), # NIR
        #BLUE: image.select("B2") # BLUE
    })

# Calculating Post_NBR
Post_NBR = post_clipped.expression(
    "(NIR - SWIR2)/(NIR + SWIR2)",
    {
        'NIR' : post_clipped.select("B5"), # NIR
        'SWIR2' : post_clipped.select("B7"), #SWIR2
    })


Map.addLayer(post_clipped, rgb_vis1, 'post_RGB', 0)
Map.addLayer(Post_ndvi, {'min':-0.2, 'max':0.8}, 'Post_NDVI', 0)
Map.addLayer(Post_NBR, {'min':-0.5, 'max':1.3}, 'Post_NBR', 0)

# Difference in NDVI
Dndvi = Pre_ndvi.subtract(Post_ndvi)

# Difference in NBR
dNBR = Pre_NBR.subtract(Post_NBR)

Map.addLayer(Dndvi,{'min':0, 'max':1},'DNDVI', 0)
Map.addLayer(dNBR, {'min':0, 'max':1}, 'dNBR', 0)


Map.centerObject(roi, zoom=10)
Map.add_layer_control()
Map

2 replies

pardhasreechaitnya Jan 23, 2021
Author

ThankYou, it worked. Can I use LinearRegression directly in this code for the variables Dndvi and dNBR?

ErikSeras Jan 23, 2021

Look at this example.

pardhasreechaitnya · 2021-01-25T05:09:44Z

pardhasreechaitnya
Jan 25, 2021
Author

I Tried ee.Reducer.LinearFit() model but it is showing an error that Dndvi is not found and I also tried to print the chart but it is showing earth engine limit exceeded. Please help me with this. Here is the code link <https://code.earthengine.google.com/9644dd482b297a67d263eb57d9e7908b>.

…

On Sun, 24 Jan 2021 at 01:03, ErikSeras ***@***.***> wrote: Look at this example <https://developers.google.com/earth-engine/guides/reducers_regression>. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#269 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AQC7HG6EGH55CUSICFYMNSLS3MQAHANCNFSM4WMHMFLQ> .

1 reply

ErikSeras Jan 25, 2021

Try changing the scale from 30 to a larger number (400 or more) so that Google Earth Engine can process it:

var chart = ui.Chart.image.regions(clipped, feature, ee.Reducer.mean(), 400, 'label')

pardhasreechaitnya · 2021-01-25T06:45:44Z

pardhasreechaitnya
Jan 25, 2021
Author

Thanks for the tip! Also, please help me with linear regression in the code.

…

On Mon, 25 Jan 2021 at 10:59, ErikSeras ***@***.***> wrote: Try changing the scale from 30 to a larger number (400 or more) so that Google Earth Engine can process it: var chart = ui.Chart.image.regions(clipped, feature, ee.Reducer.mean(), 400, 'label') — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#269 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AQC7HG5UE3GMKZ3ZDT4UNFTS3T6TRANCNFSM4WMHMFLQ> .

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Error in code #269

pardhasreechaitnya Jan 21, 2021

Replies: 4 comments · 5 replies

giswqs Jan 21, 2021 Maintainer

pardhasreechaitnya Jan 23, 2021 Author

pardhasreechaitnya Jan 23, 2021 Author

ErikSeras Jan 23, 2021

pardhasreechaitnya Jan 23, 2021 Author

ErikSeras Jan 23, 2021

pardhasreechaitnya Jan 25, 2021 Author

ErikSeras Jan 25, 2021

pardhasreechaitnya Jan 25, 2021 Author

pardhasreechaitnya
Jan 21, 2021

Replies: 4 comments 5 replies

giswqs
Jan 21, 2021
Maintainer

pardhasreechaitnya Jan 23, 2021
Author

pardhasreechaitnya Jan 23, 2021
Author

ErikSeras
Jan 23, 2021

pardhasreechaitnya Jan 23, 2021
Author

pardhasreechaitnya
Jan 25, 2021
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pardhasreechaitnya
Jan 25, 2021
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