compute NDVI

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")

#
'Author': Sofia Ermida (sofia.ermida@ipma.pt; @ermida_sofia)

this function computes NDVI values for Landsat


'to call this function use':

NDVIfun = require('users/sofiaermida/landsat_smw_lst:modules/compute_NDVI.js')
ImagewithNDVI = NDVIfun.addBand(landsat)(image)
or
collectionwithNDVI = ImageCollection.map(NDVIfun.addBand(landsat))

'INPUTS':
        '- landsat': <string>
                  the Landsat satellite id
                  'valid inputs': 'L4', 'L5', 'L7' and 'L8'
        '- image': <ee.Image>
                image for which to calculate the NDVI
'OUTPUTS':
        - <ee.Image>
          'the input image with 1 new band':
          'NDVI': normalized difference vegetation index

  '11-07-2022': update to use Collection 2 Level 2 Surface Reflectance data
#

def exports.addBand(landsat):
  def wrap(image):

    # choose bands
    nir = ee.String(ee.Algorithms.If(landsat==='L8','SR_B5','SR_B4'))
    red = ee.String(ee.Algorithms.If(landsat==='L8','SR_B4','SR_B3'))

    # compute NDVI
    return image.addBands(image.expression('(nir-red)/(nir+red)',{
      'nir':image.select(nir).multiply(0.0000275).add(-0.2),
      'red':image.select(red).multiply(0.0000275).add(-0.2)
    }).rename('NDVI'))

  return wrap


# COLLECTION 1
def exports.addBand(landsat):
  def wrap(image):

    # choose bands
    nir = ee.String(ee.Algorithms.If(landsat==='L8','B5','B4'))
    red = ee.String(ee.Algorithms.If(landsat==='L8','B4','B3'))

    # compute NDVI
    return image.addBands(image.expression('(nir-red)/(nir+red)',{
      'nir':image.select(nir).multiply(0.0001),
      'red':image.select(red).multiply(0.0001)
    }).rename('NDVI'))

  return wrap

#

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # 'Author': Sofia Ermida (sofia.ermida@ipma.pt; @ermida_sofia) this function computes NDVI values for Landsat 'to call this function use': NDVIfun = require('users/sofiaermida/landsat_smw_lst:modules/compute_NDVI.js') ImagewithNDVI = NDVIfun.addBand(landsat)(image) or collectionwithNDVI = ImageCollection.map(NDVIfun.addBand(landsat)) 'INPUTS': '- landsat': the Landsat satellite id 'valid inputs': 'L4', 'L5', 'L7' and 'L8' '- image': image for which to calculate the NDVI 'OUTPUTS': - 'the input image with 1 new band': 'NDVI': normalized difference vegetation index '11-07-2022': update to use Collection 2 Level 2 Surface Reflectance data # def exports.addBand(landsat): def wrap(image): # choose bands nir = ee.String(ee.Algorithms.If(landsat==='L8','SR_B5','SR_B4')) red = ee.String(ee.Algorithms.If(landsat==='L8','SR_B4','SR_B3')) # compute NDVI return image.addBands(image.expression('(nir-red)/(nir+red)',{ 'nir':image.select(nir).multiply(0.0000275).add(-0.2), 'red':image.select(red).multiply(0.0000275).add(-0.2) }).rename('NDVI')) return wrap # COLLECTION 1 def exports.addBand(landsat): def wrap(image): # choose bands nir = ee.String(ee.Algorithms.If(landsat==='L8','B5','B4')) red = ee.String(ee.Algorithms.If(landsat==='L8','B4','B3')) # compute NDVI return image.addBands(image.expression('(nir-red)/(nir+red)',{ 'nir':image.select(nir).multiply(0.0001), 'red':image.select(red).multiply(0.0001) }).rename('NDVI')) return wrap #

Display the interactive map¶

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Map

Map