<reponame>c11/earthengine-py-notebooks
import ee 
from ee_plugin import Map 

# Map an expression over a collection.
#
# Computes the mean NDVI and SAVI by mapping an expression over a collection
# and taking the mean.  This intentionally exercises both variants of
# Image.expression.

# Filter the L7 collection to a single month.
collection = ee.ImageCollection('LANDSAT/LE07/C01/T1_TOA') \
    .filterDate('2002-11-01', '2002-12-01')

# A function to compute NDVI.
def NDVI(image):
  return image.expression('float(b("B4") - b("B3")) / (b("B4") + b("B3"))')


# A function to compute Soil Adjusted Vegetation Index.
def SAVI(image):
  return image.expression(
      '(1 + L) * float(nir - red)/ (nir + red + L)',
      {
        'nir': image.select('B4'),
        'red': image.select('B3'),
        'L': 0.2
      })


# Shared visualization parameters.
vis = {
  'min': 0,
  'max': 1,
  'palette': [
      'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
      '74A901', '66A000', '529400', '3E8601', '207401', '056201',
      '004C00', '023B01', '012E01', '011D01', '011301'
  ]
}

Map.setCenter(-93.7848, 30.3252, 11)

# Map the functions over the collection, reduce to mean and display.
Map.addLayer(collection.map(NDVI).mean(), vis, 'Mean NDVI')
Map.addLayer(collection.map(SAVI).mean(), vis, 'Mean SAVI')

import ee 
from ee_plugin import Map 

# Map an expression over a collection.
#
# Computes the mean NDVI and SAVI by mapping an expression over a collection
# and taking the mean.  This intentionally exercises both variants of
# Image.expression.

# Filter the L7 collection to a single month.
collection = ee.ImageCollection('LANDSAT/LE07/C01/T1_TOA') \
    .filterDate('2002-11-01', '2002-12-01')

# A function to compute NDVI.
def NDVI(image):
  return image.expression('float(b("B4") - b("B3")) / (b("B4") + b("B3"))')


# A function to compute Soil Adjusted Vegetation Index.
def SAVI(image):
  return image.expression(
      '(1 + L) * float(nir - red)/ (nir + red + L)',
      {
        'nir': image.select('B4'),
        'red': image.select('B3'),
        'L': 0.2
      })


# Shared visualization parameters.
vis = {
  'min': 0,
  'max': 1,
  'palette': [
      'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
      '74A901', '66A000', '529400', '3E8601', '207401', '056201',
      '004C00', '023B01', '012E01', '011D01', '011301'
  ]
}

Map.setCenter(-93.7848, 30.3252, 11)

# Map the functions over the collection, reduce to mean and display.
Map.addLayer(collection.map(NDVI).mean(), vis, 'Mean NDVI')
Map.addLayer(collection.map(SAVI).mean(), vis, 'Mean SAVI')
# Map an expression over a collection. # # Computes the mean NDVI and SAVI by mapping an expression over a collection # and taking the mean.  This intentionally exercises both variants of # Image.expression. # Filter the L7 collection to a single month. # A function to compute NDVI. # A function to compute Soil Adjusted Vegetation Index. # Shared visualization parameters. # Map the functions over the collection, reduce to mean and display.