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landsat.py
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landsat.py
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# Landsat package
import ee
import math
from utils import *
class env(object):
def __init__(self):
"""Initialize the environment."""
# Initialize the Earth Engine object, using the authentication credentials.
self.startDate = ""
self.endDate = ""
self.location = ee.Geometry.Polygon([[103.876,18.552],[105.806,18.552],[105.806,19.999],[103.876,19.999],[103.876,18.552]])
self.metadataCloudCoverMax = 40
self.cloudThreshold = 10
self.hazeThresh = 200
self.maskSR = True
self.cloudMask = True
self.hazeMask = True
self.shadowMask = True
self.brdfCorrect = True
self.terrainCorrection = True
self.SLC = False
self.percentiles = [20,80]
self.medoidBands = ee.List(['blue','green','red','nir','swir1','swir2'])
self.divideBands = ee.List(['blue','green','red','nir','swir1','swir2'])
self.bandNamesLandsat = ee.List(['blue','green','red','nir','swir1','thermal','swir2','sr_atmos_opacity','pixel_qa','radsat_qa'])
self.sensorBandDictLandsatSR = ee.Dictionary({'L8' : ee.List([1,2,3,4,5,7,6,9,10,11]),\
'L7' : ee.List([0,1,2,3,4,5,6,7,9,10]),\
'L5' : ee.List([0,1,2,3,4,5,6,7,9,10]),\
'L4' : ee.List([0,1,2,3,4,5,6,7,9,10])})
class functions():
def __init__(self):
"""Initialize the Surfrace Reflectance app."""
# get the environment
self.env = env()
def getLandsat(self,aoi,year):
self.env.location = aoi
print type(year)
self.env.startDate = ee.Date.fromYMD(int(year),1,1) #.advance(90,"day")
self.env.endDate = ee.Date.fromYMD(int(year),12,31) #.advance(335,"day")
#self.env.endDate = ee.Date.fromYMD(year+1,1,1).advance(366+90,"day")
#self.env.endDate = ee.Date.fromYMD(year,1,1).advance(335,"day")
print self.env.startDate.getInfo(), self.env.endDate.getInfo()
landsat5 = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR').filterDate(self.env.startDate,self.env.endDate).filterBounds(self.env.location)
landsat5 = landsat5.filterMetadata('CLOUD_COVER','less_than',self.env.metadataCloudCoverMax)
landsat5 = landsat5.select(self.env.sensorBandDictLandsatSR.get('L5'),self.env.bandNamesLandsat).map(self.defringe)
landsat7 = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').filterDate(self.env.startDate,self.env.endDate).filterBounds(self.env.location)
landsat7 = landsat7.filterMetadata('CLOUD_COVER','less_than',self.env.metadataCloudCoverMax)
landsat7 = landsat7.select(self.env.sensorBandDictLandsatSR.get('L7'),self.env.bandNamesLandsat)
landsat8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filterDate(self.env.startDate,self.env.endDate).filterBounds(self.env.location)
landsat8 = landsat8.filterMetadata('CLOUD_COVER','less_than',self.env.metadataCloudCoverMax)
landsat8 = landsat8.select(self.env.sensorBandDictLandsatSR.get('L8'),self.env.bandNamesLandsat)
if (self.env.SLC or year < 2004):
landsat = landsat5.merge(landsat7).merge(landsat8)
else:
landsat = landsat5.merge(landsat8)
print landsat.size().getInfo()
if landsat.size().getInfo() > 0:
# mask clouds using the QA band
if self.env.maskSR == True:
#print "removing clouds"
landsat = landsat.map(self.CloudMaskSRL8)
# mask clouds using cloud mask function
if self.env.hazeMask == True:
#print "removing haze"
landsat = landsat.map(self.maskHaze)
# mask clouds using cloud mask function
if self.env.shadowMask == True:
#print "shadow masking"
self.fullCollection = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').filterBounds(self.env.location).select(self.env.sensorBandDictLandsatSR.get('L8'),self.env.bandNamesLandsat)
landsat = self.maskShadows(landsat)
landsat = landsat.map(self.scaleLandsat)
# mask clouds using cloud mask function
if self.env.cloudMask == True:
#print "removing some more clouds"
landsat = landsat.map(self.maskClouds)
if self.env.brdfCorrect == True:
landsat = landsat.map(self.brdf)
if self.env.terrainCorrection == True:
landsat = ee.ImageCollection(landsat.map(self.terrain))
medoid = self.medoidMosaic(landsat)
medoidDown = ee.Image(self.medoidMosaicPercentiles(landsat,self.env.percentiles[0]))
medoidUp = self.medoidMosaicPercentiles(landsat,self.env.percentiles[1])
stdevBands = self.addSTDdev(landsat)
mosaic = medoid.addBands(medoidDown).addBands(medoidUp).addBands(stdevBands)
return mosaic
def CloudMaskSRL8(self,img):
"""apply cf-mask Landsat"""
QA = img.select("pixel_qa")
shadow = QA.bitwiseAnd(8).neq(0);
cloud = QA.bitwiseAnd(32).neq(0);
return img.updateMask(shadow.Not()).updateMask(cloud.Not()).copyProperties(img)
def scaleLandsat(self,img):
"""Landast is scaled by factor 0.0001 """
thermal = img.select(ee.List(['thermal'])).multiply(0.1)
scaled = ee.Image(img).select(self.env.divideBands).multiply(ee.Number(0.0001))
return img.select([]).addBands(scaled).addBands(thermal)
def reScaleLandsat(self,img):
"""Landast is scaled by factor 0.0001 """
thermalBand = ee.List(['thermal'])
thermal = ee.Image(img).select(thermalBand).multiply(10)
otherBands = ee.Image(img).bandNames().removeAll(thermalBand)
scaled = ee.Image(img).select(otherBands).divide(0.0001)
image = ee.Image(scaled.addBands(thermal)).int16()
return image.copyProperties(img)
def maskHaze(self,img):
""" mask haze """
opa = ee.Image(img.select(['sr_atmos_opacity']).multiply(0.001))
haze = opa.gt(self.env.hazeThresh)
return img.updateMask(haze.Not())
def maskClouds(self,img):
"""
Computes spectral indices of cloudyness and take the minimum of them.
Each spectral index is fairly lenient because the group minimum
is a somewhat stringent comparison policy. side note -> this seems like a job for machine learning :)
originally written by Matt Hancher for Landsat imageryadapted to Sentinel by Chris Hewig and Ian Housman
"""
score = ee.Image(1.0);
# Clouds are reasonably bright in the blue band.
blue_rescale = img.select('blue').subtract(ee.Number(0.1)).divide(ee.Number(0.3).subtract(ee.Number(0.1)))
score = score.min(blue_rescale);
# Clouds are reasonably bright in all visible bands.
visible = img.select('red').add(img.select('green')).add(img.select('blue'))
visible_rescale = visible.subtract(ee.Number(0.2)).divide(ee.Number(0.8).subtract(ee.Number(0.2)))
score = score.min(visible_rescale);
# Clouds are reasonably bright in all infrared bands.
infrared = img.select('nir').add(img.select('swir1')).add(img.select('swir2'))
infrared_rescale = infrared.subtract(ee.Number(0.3)).divide(ee.Number(0.8).subtract(ee.Number(0.3)))
score = score.min(infrared_rescale);
# Clouds are reasonably cool in temperature.
temp_rescale = img.select('thermal').subtract(ee.Number(300)).divide(ee.Number(290).subtract(ee.Number(300)))
score = score.min(temp_rescale);
# However, clouds are not snow.
ndsi = img.normalizedDifference(['green', 'swir1']);
ndsi_rescale = ndsi.subtract(ee.Number(0.8)).divide(ee.Number(0.6).subtract(ee.Number(0.8)))
score = score.min(ndsi_rescale).multiply(100).byte();
mask = score.lt(self.env.cloudThreshold).rename(['cloudMask']);
img = img.updateMask(mask);
return img;
def maskShadows(self,collection,zScoreThresh=-0.8,shadowSumThresh=0.35,dilatePixels=2):
def TDOM(image):
zScore = image.select(shadowSumBands).subtract(irMean).divide(irStdDev)
irSum = image.select(shadowSumBands).reduce(ee.Reducer.sum())
TDOMMask = zScore.lt(zScoreThresh).reduce(ee.Reducer.sum()).eq(2)\
.And(irSum.lt(shadowSumThresh)).Not()
TDOMMask = TDOMMask.focal_min(dilatePixels)
return image.updateMask(TDOMMask)
shadowSumBands = ['nir','swir1']
# Get some pixel-wise stats for the time series
irStdDev = self.fullCollection.select(shadowSumBands).reduce(ee.Reducer.stdDev())
irMean = self.fullCollection.select(shadowSumBands).reduce(ee.Reducer.mean())
# Mask out dark dark outliers
collection_tdom = collection.map(TDOM)
return collection_tdom
def addSTDdev(self,collection):
def addSTDdevIndices(img):
""" Function to add common (and less common) spectral indices to an image.
Includes the Normalized Difference Spectral Vector from (Angiuli and Trianni, 2014) """
img = img.addBands(img.normalizedDifference(['green','swir1']).rename(['ND_green_swir1'])); # NDSI, MNDWI
img = img.addBands(img.normalizedDifference(['nir','red']).rename(['ND_nir_red'])); # NDVI
img = img.addBands(img.normalizedDifference(['nir','swir2']).rename(['ND_nir_swir2'])); # NBR, MNDVI
return img;
blue_stdDev = collection.select(["blue"]).reduce(ee.Reducer.stdDev()).rename(['blue_stdDev'])
red_stdDev = collection.select(["red"]).reduce(ee.Reducer.stdDev()).rename(['red_stdDev'])
green_stdDev = collection.select(["green"]).reduce(ee.Reducer.stdDev()).rename(['green_stdDev'])
nir_stdDev = collection.select(["nir"]).reduce(ee.Reducer.stdDev()).rename(['nir_stdDev'])
swir1_stdDev = collection.select(["swir1"]).reduce(ee.Reducer.stdDev()).rename(['swir1_stdDev'])
swir2_stdDev = collection.select(["swir2"]).reduce(ee.Reducer.stdDev()).rename(['swir2_stdDev'])
col = collection.map(addSTDdevIndices)
ND_green_swir1 = col.select(['ND_green_swir1']).reduce(ee.Reducer.stdDev()).rename(['ND_green_swir1_stdDev']);
ND_nir_red = col.select(['ND_nir_red']).reduce(ee.Reducer.stdDev()).rename(['ND_nir_red_stdDev']);
ND_nir_swir2 = col.select(['ND_nir_swir2']).reduce(ee.Reducer.stdDev()).rename(['ND_nir_swir2_stdDev']);
stdevBands = ee.Image(blue_stdDev.addBands(red_stdDev).addBands(green_stdDev).addBands(nir_stdDev).addBands(swir1_stdDev).addBands(swir2_stdDev)\
.addBands(ND_green_swir1).addBands(ND_nir_red).addBands(ND_nir_swir2))
return stdevBands
def defringe(self,img):
# threshold for defringing landsat5 and 7
fringeCountThreshold = 279
k = ee.Kernel.fixed(41, 41,
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]);
m = ee.Image(img).mask().reduce(ee.Reducer.min())
sum = m.reduceNeighborhood(ee.Reducer.sum(), k, 'kernel')
mask = sum.gte(fringeCountThreshold)
return img.updateMask(mask)
def terrain(self,img):
degree2radian = 0.01745;
thermalBand = img.select(['thermal'])
def topoCorr_IC(img):
dem = ee.Image("JAXA/ALOS/AW3D30_V1_1").select("MED")
# Extract image metadata about solar position
SZ_rad = ee.Image.constant(ee.Number(img.get('SOLAR_ZENITH_ANGLE'))).multiply(degree2radian).clip(img.geometry().buffer(10000));
SA_rad = ee.Image.constant(ee.Number(img.get('SOLAR_AZIMUTH_ANGLE'))).multiply(degree2radian).clip(img.geometry().buffer(10000));
# Creat terrain layers
slp = ee.Terrain.slope(dem).clip(img.geometry().buffer(10000));
slp_rad = ee.Terrain.slope(dem).multiply(degree2radian).clip(img.geometry().buffer(10000));
asp_rad = ee.Terrain.aspect(dem).multiply(degree2radian).clip(img.geometry().buffer(10000));
# Calculate the Illumination Condition (IC)
# slope part of the illumination condition
cosZ = SZ_rad.cos();
cosS = slp_rad.cos();
slope_illumination = cosS.expression("cosZ * cosS", \
{'cosZ': cosZ, 'cosS': cosS.select('slope')});
# aspect part of the illumination condition
sinZ = SZ_rad.sin();
sinS = slp_rad.sin();
cosAziDiff = (SA_rad.subtract(asp_rad)).cos();
aspect_illumination = sinZ.expression("sinZ * sinS * cosAziDiff", \
{'sinZ': sinZ, \
'sinS': sinS, \
'cosAziDiff': cosAziDiff});
# full illumination condition (IC)
ic = slope_illumination.add(aspect_illumination);
# Add IC to original image
img_plus_ic = ee.Image(img.addBands(ic.rename(['IC'])).addBands(cosZ.rename(['cosZ'])).addBands(cosS.rename(['cosS'])).addBands(slp.rename(['slope'])));
return ee.Image(img_plus_ic);
def topoCorr_SCSc(img):
img_plus_ic = img;
mask1 = img_plus_ic.select('nir').gt(-0.1);
mask2 = img_plus_ic.select('slope').gte(5) \
.And(img_plus_ic.select('IC').gte(0)) \
.And(img_plus_ic.select('nir').gt(-0.1));
img_plus_ic_mask2 = ee.Image(img_plus_ic.updateMask(mask2));
bandList = ['blue', 'green', 'red', 'nir', 'swir1', 'swir2']; # Specify Bands to topographically correct
def applyBands(image):
blue = apply_SCSccorr('blue').select(['blue'])
green = apply_SCSccorr('green').select(['green'])
red = apply_SCSccorr('red').select(['red'])
nir = apply_SCSccorr('nir').select(['nir'])
swir1 = apply_SCSccorr('swir1').select(['swir1'])
swir2 = apply_SCSccorr('swir2').select(['swir2'])
return replace_bands(image, [blue, green, red, nir, swir1, swir2])
def apply_SCSccorr(band):
method = 'SCSc';
out = ee.Image(1).addBands(img_plus_ic_mask2.select('IC', band)).reduceRegion(reducer= ee.Reducer.linearRegression(2,1), \
geometry= ee.Geometry(img.geometry().buffer(-5000)), \
scale= 300, \
bestEffort =True,
maxPixels=1e10)
#out_a = ee.Number(out.get('scale'));
#out_b = ee.Number(out.get('offset'));
#out_c = ee.Number(out.get('offset')).divide(ee.Number(out.get('scale')));
fit = out.combine({"coefficients": ee.Array([[1],[1]])}, False);
#Get the coefficients as a nested list,
#cast it to an array, and get just the selected column
out_a = (ee.Array(fit.get('coefficients')).get([0,0]));
out_b = (ee.Array(fit.get('coefficients')).get([1,0]));
out_c = out_a.divide(out_b)
# apply the SCSc correction
SCSc_output = img_plus_ic_mask2.expression("((image * (cosB * cosZ + cvalue)) / (ic + cvalue))", {
'image': img_plus_ic_mask2.select([band]),
'ic': img_plus_ic_mask2.select('IC'),
'cosB': img_plus_ic_mask2.select('cosS'),
'cosZ': img_plus_ic_mask2.select('cosZ'),
'cvalue': out_c });
return ee.Image(SCSc_output);
#img_SCSccorr = ee.Image([apply_SCSccorr(band) for band in bandList]).addBands(img_plus_ic.select('IC'));
img_SCSccorr = applyBands(img).select(bandList).addBands(img_plus_ic.select('IC'))
bandList_IC = ee.List([bandList, 'IC']).flatten();
img_SCSccorr = img_SCSccorr.unmask(img_plus_ic.select(bandList_IC)).select(bandList);
return img_SCSccorr.unmask(img_plus_ic.select(bandList))
img = topoCorr_IC(img)
img = topoCorr_SCSc(img)
return img.addBands(thermalBand)
def brdf(self,img):
import sun_angles
import view_angles
def _apply(image, kvol, kvol0):
blue = _correct_band(image, 'blue', kvol, kvol0, f_iso=0.0774, f_geo=0.0079, f_vol=0.0372)
green = _correct_band(image, 'green', kvol, kvol0, f_iso=0.1306, f_geo=0.0178, f_vol=0.0580)
red = _correct_band(image, 'red', kvol, kvol0, f_iso=0.1690, f_geo=0.0227, f_vol=0.0574)
nir = _correct_band(image, 'nir', kvol, kvol0, f_iso=0.3093, f_geo=0.0330, f_vol=0.1535)
swir1 = _correct_band(image, 'swir1', kvol, kvol0, f_iso=0.3430, f_geo=0.0453, f_vol=0.1154)
swir2 = _correct_band(image, 'swir2', kvol, kvol0, f_iso=0.2658, f_geo=0.0387, f_vol=0.0639)
return replace_bands(image, [blue, green, red, nir, swir1, swir2])
def _correct_band(image, band_name, kvol, kvol0, f_iso, f_geo, f_vol):
"""fiso + fvol * kvol + fgeo * kgeo"""
iso = ee.Image(f_iso)
geo = ee.Image(f_geo)
vol = ee.Image(f_vol)
pred = vol.multiply(kvol).add(geo.multiply(kvol)).add(iso).rename(['pred'])
pred0 = vol.multiply(kvol0).add(geo.multiply(kvol0)).add(iso).rename(['pred0'])
cfac = pred0.divide(pred).rename(['cfac'])
corr = image.select(band_name).multiply(cfac).rename([band_name])
return corr
def _kvol(sunAz, sunZen, viewAz, viewZen):
"""Calculate kvol kernel.
From Lucht et al. 2000
Phase angle = cos(solar zenith) cos(view zenith) + sin(solar zenith) sin(view zenith) cos(relative azimuth)"""
relative_azimuth = sunAz.subtract(viewAz).rename(['relAz'])
pa1 = viewZen.cos() \
.multiply(sunZen.cos())
pa2 = viewZen.sin() \
.multiply(sunZen.sin()) \
.multiply(relative_azimuth.cos())
phase_angle1 = pa1.add(pa2)
phase_angle = phase_angle1.acos()
p1 = ee.Image(PI().divide(2)).subtract(phase_angle)
p2 = p1.multiply(phase_angle1)
p3 = p2.add(phase_angle.sin())
p4 = sunZen.cos().add(viewZen.cos())
p5 = ee.Image(PI().divide(4))
kvol = p3.divide(p4).subtract(p5).rename(['kvol'])
viewZen0 = ee.Image(0)
pa10 = viewZen0.cos() \
.multiply(sunZen.cos())
pa20 = viewZen0.sin() \
.multiply(sunZen.sin()) \
.multiply(relative_azimuth.cos())
phase_angle10 = pa10.add(pa20)
phase_angle0 = phase_angle10.acos()
p10 = ee.Image(PI().divide(2)).subtract(phase_angle0)
p20 = p10.multiply(phase_angle10)
p30 = p20.add(phase_angle0.sin())
p40 = sunZen.cos().add(viewZen0.cos())
p50 = ee.Image(PI().divide(4))
kvol0 = p30.divide(p40).subtract(p50).rename(['kvol0'])
return (kvol, kvol0)
date = img.date()
footprint = determine_footprint(img)
(sunAz, sunZen) = sun_angles.create(date, footprint)
(viewAz, viewZen) = view_angles.create(footprint)
(kvol, kvol0) = _kvol(sunAz, sunZen, viewAz, viewZen)
return _apply(img, kvol.multiply(PI()), kvol0.multiply(PI()))
def medoidMosaic(self,collection):
""" medoid composite with equal weight among indices """
# calculate the median of temp band
thermal = ee.ImageCollection(collection.select(['thermal'])).median()
collection = collection.select(self.env.divideBands)
bandNames = self.env.divideBands;
bandNumbers = ee.List.sequence(1,bandNames.length());
# calculate medion
median = ee.ImageCollection(collection).median()
def subtractmedian(img):
diff = ee.Image(img).subtract(median).pow(ee.Image.constant(2));
return diff.reduce('sum').addBands(img);
medoid = collection.map(subtractmedian)
medoid = ee.ImageCollection(medoid).reduce(ee.Reducer.min(bandNames.length().add(1))).select(bandNumbers,bandNames);
return medoid.addBands(thermal);
def medoidMosaicPercentiles(self,inCollection,p):
' calculate the medoid of a percentile'
inCollection = inCollection.select(self.env.medoidBands)
p1 = p
p2 = 100 -p
med1 = self.medoidPercentiles(inCollection,p1).select(["green","nir"])
med2 = self.medoidPercentiles(inCollection,p2).select(["blue","red","swir1","swir2"])
medoidP = self.renameBands(ee.Image(med1).addBands(med2),str("p")+str(p))
return medoidP
def medoidPercentiles(self,inCollection,p):
# Find band names in first image
bandNumbers = ee.List.sequence(1,self.env.medoidBands.length());
# Find the median
percentile = inCollection.select(self.env.medoidBands).reduce(ee.Reducer.percentile([p]));
def subtractPercentile(img):
diff = ee.Image(img).subtract(percentile).pow(ee.Image.constant(2));
return diff.reduce('sum').addBands(img);
percentile = inCollection.map(subtractPercentile)
percentile = ee.ImageCollection(percentile).reduce(ee.Reducer.min(self.env.medoidBands.length().add(1))).select(bandNumbers,self.env.medoidBands);
return percentile;
def renameBands(self,image,prefix):
'rename bands with prefix'
bandnames = image.bandNames();
def mapBands(band):
band = ee.String(prefix).cat('_').cat(band);
return band;
bandnames = bandnames.map(mapBands)
image = image.rename(bandnames);
return image;
if __name__ == "__main__":
#def composite(aoi,year):
ee.Initialize()
year = 2017
aoi = ee.FeatureCollection("users/KarisTenneson/MyanmarRubberPlantations/StudyRegion").geometry().bounds()
img = ee.Image(functions().getLandsat(aoi,year))
img = ee.Image(img).multiply(10000).int16()
region = ee.Geometry.Polygon(aoi.getInfo()['coordinates'][0])
#outputName = "users/servirmekong/rubberCollection/Landsat2017_winter" + str(year)
task_ordered = ee.batch.Export.image.toAsset(image=ee.Image(img).clip(aoi), description="Export "+str(year), assetId=outputName,region=region['coordinates'], maxPixels=1e13,scale=30 )
# start task
task_ordered.start()