def to_ibi(image, high_plant_cover=False, L=0.5):
"""
Calculates the Index-based Built-up Index (IBI) for the provided image.
Required bands: ['green', 'red', 'nir', 'swir1']
Args:
image: Image to calculate IBI for
high_plant_cover: Set to True if plant cover is over 30%. It will then use NDVI instead of SAVI in
the calculation
L: (optional) Soil brightness correction factor. Will have no effect when high plant cover.
Returns:
If image contains required bands, single band image named 'savi', otherwise an image without bands.
"""
ndbi = to_ndbi(image)
mndwi = to_mndwi(image)
if high_plant_cover:
ndvi = to_ndvi(image)
return evaluate(
ndbi.addBands(ndvi).addBands(mndwi), ['ndbi', 'ndvi', 'mndwi'],
'(ndbi - (ndvi + mndwi) / 2) / (ndbi + (ndvi + mndwi) / 2)', 'ibi')
else:
savi = to_savi(image, L)
return evaluate(
ndbi.addBands(savi).addBands(mndwi), ['ndbi', 'savi', 'mndwi'],
'(ndbi - (savi + mndwi) / 2) / (ndbi + (savi + mndwi) / 2)', 'ibi')
def to_evi(image, L=1, C1=6, C2=7.5, G=2.5):
"""
Calculates the Enhanced Vegetation Index (EVI) for the provided image.
Required bands: ['blue', 'red', 'nir']
Args:
image: Image to calculate EVI for
L: (optional) Canopy background adjustment
C1: (optional) Aerosol resistance term 1
C2: (optional) Aerosol resistance term 2
G: (optional) Gain factor
Returns:
If image contains required bands, single band image named 'evi', otherwise an image without bands.
"""
return evaluate(
image=image,
required_bands=['blue', 'red', 'nir'],
expression=
'{G} * ((nir - red) / (nir + {C1} * red - {C2} * blue + {L}))'.format(
L=L, C1=C1, C2=C2, G=G),
name='evi')
def to_mndwi(image):
"""
Calculates the Modified Normalized Difference Water Index (MNDWI) for the provided image.
Required bands: ['green', 'swir1']
Args:
image: Image to calculate MNDWI for
Returns:
If image contains required bands, single band image named 'mndwi', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['green', 'swir1'],
expression='(green - swir1) / (green + swir1)',
name='mndwi')
def to_ndmi(image):
"""
Calculates the Normalized Difference Moisture Index (NDMI) for the provided image.
Required bands: ['nir', 'swir1']
Args:
image: Image to calculate NDMI for
Returns:
If image contains required bands, single band image named 'ndmi', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['nir', 'swir1'],
expression='(nir - swir1) / (nir + swir1)',
name='ndmi')
def to_ebbi(image):
"""
Calculates the Enhanced Built-up and Bareness Index (EBBI) for the provided image.
Required bands: ['nir', 'swir1', 'thermal']
Args:
image: Image to calculate EBBI for
Returns:
If image contains required bands, single band image named 'ebbi', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['nir', 'swir1', 'swir2'],
expression='(swir1 - nir) / 10 * sqrt(swir1 + thermal)',
name='ebbi')
def to_nbi(image):
"""
Calculates the New Built-up Index (NBI) for the provided image.
Required bands: ['red', 'nir', 'swir1']
Args:
image: Image to calculate NBI for
Returns:
If image contains required bands, single band image named 'nbi', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['red', 'nir', 'swir1'],
expression='red * swir1 / nir',
name='nbi')
def to_ndbi(image):
"""
Calculates the Normalized Difference Built-up Index (NDBI) for the provided image.
Required bands: ['nir', 'swir1']
Args:
image: Image to calculate NDBI for
Returns:
If image contains required bands, single band image named 'ndbi', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['nir', 'swir1'],
expression='(swir1 - nir) / (swir1 + nir)',
name='ndbi')
def to_ndvi(image):
"""
Calculates the Normalized Difference Vegetation Index (NDVI) for the provided image.
Required bands: ['red', 'nir']
Args:
image: Image to calculate NDVI for
Returns:
If image contains required bands, single band image named 'ndvi', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['red', 'nir'],
expression='(nir - red) / (nir + red)',
name='ndvi')
def to_ui(image):
"""
Calculates the Urban Index (UI) for the provided image.
Required bands: ['nir', 'swir2']
Args:
image: Image to calculate UI for
Returns:
If image contains required bands, single band image named 'ui', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['nir', 'swir2'],
expression='(swir2 - nir) / (swir2 + nir)',
name='ui')
def to_nbr(image):
"""
Calculates the Normalized Burn Ratio (NBR) for the provided image.
Required bands: ['nir', 'swir2']
Args:
image: Image to calculate NBR for
Returns:
If image contains required bands, single band image named 'NBR', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['nir', 'swir2'],
expression='(nir - swir2) / (nir + swir2)',
name='nbr')
def to_evi2(image):
"""
Calculates the Enhanced Vegetation Index 2 (EVI2) for the provided image.
Required bands: ['red', 'nir']
Args:
image: Image to calculate EVI2 for
Returns:
If image contains required bands, single band image named 'evi2', otherwise an image without bands.
"""
return evaluate(image=image,
required_bands=['blue', 'red', 'nir'],
expression='2.5 * (nir - red) / (nir + 2.4 * red + 1)',
name='evi2')
def to_bui(image):
"""
Calculates the Built-up Index (BUI) for the provided image.
Required bands: ['red', 'swir1', 'swir2']
Args:
image: Image to calculate BUI for
Returns:
If image contains required bands, single band image named 'bui', otherwise an image without bands.
"""
return evaluate(
image=image,
required_bands=['red', 'swir1', 'swir2'],
expression=
'(red - swir1) / (red + swir) + (swir2 - swir1) / (swir2 + swir1)',
name='bui')
def to_savi(image, L=0.5):
"""
Calculates the Soil-adjusted Vegetation Index (SAVI) for the provided image.
Required bands: ['red', 'nir']
Args:
image: Image to calculate SAVI for
L: (optional) Soil brightness correction factor
Returns:
If image contains required bands, single band image named 'savi', otherwise an image without bands.
"""
return evaluate(
image=image,
required_bands=['red', 'nir'],
expression='(nir - red) * (1 + {L})/(nir + red + {L})'.format(L=L),
name='savi')
