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Merged
MariaLuize merged 1 commit into from
Oct 17, 2022
Merged

feat:mosaic generation #2

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206 changes: 206 additions & 0 deletions mosaic_generation.py
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Original file line number Diff line number Diff line change
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import ee
try:
ee.Initialize(opt_url='https://earthengine-highvolume.googleapis.com')
except:
ee.Authenticate()
ee.Initialize(opt_url='https://earthengine-highvolume.googleapis.com')

def getImageCollection(studyArea,startDate,endDate):
sensorBandDictLandsatTOA = ee.Dictionary({
L8 : ee.List([1,2,3,4,5,11,6,'BQA']),
L7 : ee.List([0,1,2,3,4,5,7,'BQA']),
L5 : ee.List([0,1,2,3,4,5,6,'BQA']),
L4 : ee.List([0,1,2,3,4,5,6,'BQA'])
})
bandNamesLandsatTOA = ee.List(['blue','green','red','nir','swir1','temp','swir2','BQA'])

l4TOAs = ee.ImageCollection('LANDSAT/LT04/C01/T1_TOA')\
.filterDate(startDate,endDate)\
.filterBounds(studyArea)\
.select(sensorBandDictLandsatTOA.get('L4'),bandNamesLandsatTOA)

l5TOAs = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')\
.filterDate(startDate,endDate)\
.filterBounds(studyArea)\
.select(sensorBandDictLandsatTOA.get('L5'),bandNamesLandsatTOA)

l8TOAs = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')\
.filterDate(startDate,endDate)\
.filterBounds(studyArea)\
.select(sensorBandDictLandsatTOA.get('L8'),bandNamesLandsatTOA)


l7TOAs = ee.ImageCollection('LANDSAT/LE07/C01/T1_TOA')\
.filterDate(startDate,endDate)\
.filterBounds(studyArea)\
.select(sensorBandDictLandsatTOA.get('L7'),bandNamesLandsatTOA)

ls = ee.ImageCollection(l5TOAs.merge(l7TOAs).merge(l8TOAs))
out = ls
return out


def bqaFunction (image):
bqa = image.select('BQA');
if(year > 2013):
image = image.mask(bqa.eq(2720))
else:
image = image.mask(bqa.eq(672))
return image



def createIndexs(image) :
MNDWI = None
NDVI = None
MNDWI_calc = None
NDVI_calc = None
EVI = None
EVI_2 = None
NDWI = None
NDWI_GAO_calc = None
CAI = None
PRI = None
SAVI = None
WVI = None
WVI_GAO = None
MMDI_v1 = None
MMDI_v2 = None
NDSI = None

# Gerando NDVI
NDVI = image.expression(
'(((banda4 - banda3)/(banda4 + banda3)))', {
'banda4': image.select('nir'),
'banda3': image.select('red')
})
NDSI = image.expression(
'(((banda5 - banda4)/(banda4 + banda5)))', {
'banda5': image.select('swir1'),
'banda4': image.select('nir')
})

EVI = image.expression(
'(2.5 * ( ((banda4 - banda3)) / ((banda4 + (6 * banda3)) - (7.5 * banda1) + 1 ) )) ', {
'banda1' : image.select('blue'),
'banda3' : image.select('red'),
'banda4' : image.select('nir')
})

NDWI = image.expression(
'((banda2 - banda4)/ (banda4 + banda2))', {
'banda2' : image.select('green'),
'banda4' : image.select('nir')
})

NDWI_GAO_calc = image.expression(
'((banda4 - banda5)**2 / (banda4 + banda5)**2)**(1/2)', {
'banda4' : image.select('nir'),
'banda5' : image.select('swir1')
})

MNDWI = image.expression(
'((( banda2 - banda5) / (banda2 + banda5)))', {
'banda2': image.select('green'),
'banda5': image.select('swir1'),
})

MNDWI_calc = image.expression(
'(( banda2 - banda5)**2 / (banda2 + banda5)**2)**(1/2)', {
'banda2': image.select('green'),
'banda5': image.select('swir1'),
})

NDVI_calc = image.expression(
'((banda4 - banda3)**2/ (banda4 + banda3)**2)**(1/2)', {
'banda4': image.select('nir'),
'banda3': image.select('red'),
'banda2': image.select('green'),
})

#(Simplified Using Modules - Iury Angelo - "(abs(a-b) * abs(c+d) - abs(c-d) * abs(a+b))/(abs(a-b) * abs(c+d) + abs(c-d) * abs(a+b))"
MMDI_v1 = image.expression(
'(abs(banda2 - banda5) * abs(banda4 + banda3) - abs(banda4 - banda3) * (banda2 + banda5)) / (abs(banda2 - banda5) * abs(banda4 + banda3) + abs(banda4 - banda3) * abs(banda2 + banda5))', {
'banda2': image.select('green'),
'banda3': image.select('red'),
'banda4': image.select('nir'),
'banda5': image.select('swir1'),
})


maskedImage = image\
.addBands(image.metadata('system:time_start'))\
.addBands(NDVI.rename('NDVI'))\
.addBands(NDSI.rename('NDSI'))\
.addBands(MNDWI.rename('MNDWI'))\
.addBands(EVI.rename('EVI'))\
.addBands(NDWI.rename('NDWI'))\
.addBands(MMDI_v1.rename('MMRI'))\

return maskedImage


# Main CODE
geometry = ee.Geometry.Polygon(
[[[-51.85546875, 5.266007882805497],
[-52.294921875, 3.337953961416485],
[-51.317138671875, 0.39550467153201946],
[-49.52124573640583, -0.6920858950288072],
[-48.49365234375, -1.4610232806227417],
[-42.890625, -3.8642546157213955],
[-39.55078125, -4.740675384778361],
[-35.9912109375, -6.358975327235661],
[-36.38671875, -9.44906182688142],
[-38.49609375, -11.695272733029402],
[-39.7265625, -13.752724664396975],
[-39.375, -15.961329081596647],
[-40.25390625, -18.145851771694467],
[-40.78125, -19.642587534013032],
[-42.36328125, -22.105998799750555],
[-45.52734375, -22.593726063929296],
[-48.69140625, -24.686952411999144],
[-49.39453125, -26.431228064506428],
[-49.74609375, -28.613459424004418],
[-52.3828125, -31.503629305773018],
[-54.31640625, -32.99023555965106],
[-52.91015625, -33.72433966174759],
[-49.39453125, -30.14512718337612],
[-48.33984375, -28.76765910569124],
[-47.28515625, -25.482951175355307],
[-44.47265625, -23.885837699861995],
[-42.1875, -23.725011735951796],
[-40.25390625, -22.593726063929296],
[-37.6171875, -16.130262012034756],
[-37.96875, -13.752724664396975],
[-36.2109375, -12.039320557540572],
[-34.1015625, -9.102096738726443],
[-33.92578125, -6.315298538330034],
[-34.63334381058809, -4.370164795779153],
[-38.14453125, -3.5134210456400323],
[-42.01171875, -1.9332268264771106],
[-45.17578125, -0.8788717828324148],
[-47.63671875, 0.17578097424708533],
[-49.921875, 1.9332268264771233],
[-50.625, 3.8642546157214084]]])

studyArea = geometry
year = 2021
startDate = year+'-01-01'
endDate = (year)+'-12-30'

mosaicMerge = getImageCollection(studyArea,startDate,endDate).map(createIndexs).map(bqaFunction)
paleta = ['008100', '389D2E', '76B956', 'A7D17A','D0E899', 'F8F4BD', 'F0D9CA', 'E2BFD1', 'D2A5D8', 'AC93E2', '7C85EF', '347AF9', '006CFD', '0057E0', '4238BA', '5614BC']

mosaicMerge_tradicional = mosaicMerge.median()
mosaicMerge = mosaicMerge_tradicional.select(['green','red','nir','swir1','swir2']).multiply(255)
mosaicMerge = mosaicMerge.addBands(mosaicMerge_tradicional.select(['NDSI','NDVI','MNDWI','NDWI','MMRI']).add(1).multiply(127))

export = ee.batch.Export.image.toAsset(
image = mosaicMerge.select(['red','nir','swir1','swir2','NDVI','NDWI','NDSI']).set({'year':year,'mosaic':1,'version':5,'collection':3}).toByte(),
description = 'mosaico_zc_'+year,
assetId = '__PATH__'+year,
region = geometry,
scale = 30,
maxPixels = 1e13,
)
# export.start()