burn_mapper / historical_fires.py
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import ee
import geemap
import solara
import ipywidgets as widgets
#from NBR_calculations import GcalcNBR, VcalcNBR, LcalcNBR, ScalcNBR, GcalcCCsingle
import requests
# Bit-masking
BitMask_0 = 1 << 0
BitMask_1 = 1 << 1
BitMask_2 = 1 << 2
BitMask_3 = 1 << 3
BitMask_4 = 1 << 4
BitMask_5 = 1 << 5
BitMask_6 = 1 << 6
BitMask_7 = 1 << 7
BitMask_8 = 1 << 8
BitMask_9 = 1 << 9
def GcalcCCsingle (goesImg):
fireDQF = goesImg.select('DQF').int()
CMI_QF3 = goesImg.select('DQF_C03').int()
CMI_QF6 = goesImg.select('DQF_C06').int()
#Right now, cloud mask is excluding clouds and water; active fire, bad data and fire free are unmasked. NBR mask exlcudes fire
F_Mask = fireDQF.eq(0)
C_Mask = (fireDQF.lt(2).Or(fireDQF.gt(2))).rename('C_Mask')
#.And(CMI_QF3.lt(2)).And(CMI_QF6.lt(2)).rename('C_Mask')
QF_Mask = (fireDQF.eq(1).Or(fireDQF.gt(3)))\
.And(CMI_QF3.lt(2)).And(CMI_QF6.lt(2)).rename('QFmask')
GOESmasked = goesImg.select(['CMI_C03','CMI_C06']).updateMask(QF_Mask)
NBRmasked = GOESmasked.normalizedDifference(['CMI_C03', 'CMI_C06']).toFloat().rename('NBR')
cloudMasked = goesImg.select('CMI_C03').updateMask(C_Mask).toFloat().rename('CC')
fireMasked = goesImg.select('CMI_C03').updateMask(F_Mask).toFloat().rename('FC')
return goesImg.addBands([NBRmasked,cloudMasked, fireMasked,QF_Mask,C_Mask])
'''Parameter Array Name Value Bit(s) = Value
Sun Glint QF1 Surface Reflectance None 6-7 = 00
Low Sun Mask QF1 Surface Reflectance High 5 = 0
Day/Night QF1 Surface Reflectance Day 4 =0
Cloud Detection QF1 Surface Reflectance Confident Clear 2-3 = 00 or Problably Clear 2-3 = 01
Cloud Mask Quality QF1 Surface Reflectance High or Medium 0-1 = 10 or 11
Snow/Ice QF2 Surface Reflectance No Snow or Ice 5 = 0
Cloud Shadow QF2 Surface Reflectance No Cloud Shadow 3 = 0
LandWater QF2 Surface Reflectance Land, Snow, Arctic, Antarctic or Greenland, Desert 0-2 = 011, 100, 101, 110, 111
Thin Cirrus Flag QF7 Surface Reflectance No Thin Cirrus 4 = 0
Aerosol Quantity QF7 Surface Reflectance Climatology, Low or Medium 2-3 = 00, 01 or 10
Adjacent to Cloud QF7 Surface Reflectance Not Adjacent to Cloud 1 = 0'''
def VcalcNBR (VIIRSimg):
QF1 = VIIRSimg.select('QF1').int()
QF2 = VIIRSimg.select('QF2').int()
QF7 = VIIRSimg.select('QF7').int()
QF_Mask = (QF1.bitwiseAnd(BitMask_3).eq(0)).And\
((QF2.bitwiseAnd(BitMask_2).eq(4)).Or((QF2.bitwiseAnd(BitMask_1).eq(0)))).And\
(QF2.bitwiseAnd(BitMask_5).eq(0)).rename('QFmask');
VIIRSm = VIIRSimg.select(['I2','M11']).updateMask(QF_Mask);
NBR = VIIRSm.normalizedDifference(['I2','M11']).toFloat().rename('NBR')
return VIIRSimg.addBands(NBR).addBands(QF_Mask)#.set('avgNBR', avgNBR)
''' Bit 1: Dilated Cloud
Bit 2: Cirrus (high confidence)
Bit 3: Cloud
Bit 4: Cloud Shadow
Bit 5: Snow
Bit 6: Clear (0: Cloud or Dilated Cloud bits are set, 1: Cloud and Dilated Cloud bits are not set)
Bit 7: Water
Bits 8-9: Cloud Confidence (0: None, 1: Low, 2: Medium, 3: High)
Bits 10-11: Cloud Shadow Confidence (0: None, 1: Low, 2: Medium, 3: High)
Bits 12-13: Snow/Ice Confidence (0: None, 1: Low, 2: Medium, 3: High)
Bits 14-15: Cirrus Confidence (0: None, 1: Low, 2: Medium, 3: High)'''
def LcalcNBR (LSimg):
QApixel = LSimg.select('QA_PIXEL').int()
QF_Mask =(QApixel.bitwiseAnd(BitMask_3).eq(0)).And\
(QApixel.bitwiseAnd(BitMask_5).eq(0)).And\
(QApixel.bitwiseAnd(BitMask_7).eq(0)).rename('QFmask');
LSmasked = LSimg.select(['SR_B5','SR_B7']).updateMask(QF_Mask);
NBR = LSmasked.normalizedDifference(['SR_B5','SR_B7']).toFloat().rename('NBR')
return LSimg.addBands(NBR).addBands(QF_Mask)#.set('avgNBR', avgNBR)
''' 1 Saturated or defective
2 Dark Area Pixels
3 Cloud Shadows
4 Vegetation
5 Bare Soils
6 Water
7 Clouds Low Probability / Unclassified
8 Clouds Medium Probability
9 Clouds High Probability
10 Cirrus
11 Snow / Ice'''
def ScalcNBR (sentImg):
SCL = sentImg.select('SCL');
QF_Mask =(SCL.neq(6)).And\
(SCL.neq(8)).And\
(SCL.neq(9)).And\
(SCL.neq(11))\
.rename('QFmask');
sentMasked = sentImg.select(['B8A','B12']).updateMask(QF_Mask); #B8 is another option- broadband NIR
NBR = sentMasked.normalizedDifference(['B8A','B12']).toFloat().rename('NBR')
return sentImg.addBands(NBR).addBands(QF_Mask).addBands(SCL)#.set('avgNBR', avgNBR)
fireList = ["North Complex", "Dixie", "Cameron Peak", "August Complex", "South Fork"]
selected_fire = solara.reactive(fireList[4])
selected_days = solara.reactive(25) #30
dNBRvisParams = {'min': 0.0,'max': 0.8, 'palette': ['green', 'yellow','orange','red']}
class Map(geemap.Map):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.add_basemap('OpenStreetMap')
self.customize_ee_data(selected_fire.value, selected_days.value)
self.add_selector()
self.add_intSlider()
self.add_dwnldButton()
self.add("layer_manager")
self.remove("draw_control")
def customize_ee_data(self, fire, elapDays):
elapDayNum = ee.Number(elapDays)
elapDay_plusOne = elapDayNum.add(ee.Number(1))
north_startDate = ee.Date('2020-08-16')
dixie_startDate = ee.Date('2021-07-13')
cam_startDate = ee.Date('2020-08-13')
aug_startDate = ee.Date('2020-08-15')
sfork_startDate = ee.Date('2024-05-25')
north_complex_bb = ee.Geometry.BBox(-121.616097, 39.426723, -120.668526, 40.030845)
dixie_bb = ee.Geometry.BBox(-121.680467, 39.759303, -120.065477, 40.873387)
cam_peak_bb = ee.Geometry.BBox(-106.014784, 40.377907, -105.116651, 40.822094)
aug_complex_bb = ee.Geometry.BBox(-123.668726, 39.337654, -122.355860, 40.498304)
sfork_bb = ee.Geometry.BBox(-106.192, 33.1, -105.065, 33.782)
def calc_nbr(pre_start, pre_stop, post_start, post_stop, bbox, goes):
def MergeBands (eachImage):
oneImage = ee.Image.cat(eachImage.get('CMI'), eachImage.get('FDC'))
return oneImage
displacementImg18 = ee.Image.load('projects/ee-losos/assets/G18-F-meter-offset_GEE')
y_dif = displacementImg18.select([1])
x_dif = displacementImg18.select([0]).multiply(-1)
displacement18 = ee.Image([x_dif, y_dif])
displacementImg16 = ee.Image.load('projects/ee-losos/assets/G16-F-meter-offset_GEE')
y_dif = displacementImg16.select([1])
x_dif = displacementImg16.select([0]).multiply(-1)
displacement16 = ee.Image([x_dif, y_dif]);
preCMIcol = ee.ImageCollection(f"NOAA/GOES/{goes}/MCMIPF").filter(ee.Filter.date(pre_start, pre_stop))\
.filter(ee.Filter.calendarRange(17, 21, 'hour'))#10-2pm PCT, 11am-3pm MST
preFDCcol = ee.ImageCollection(f"NOAA/GOES/{goes}/FDCF").filter(ee.Filter.date(pre_start, pre_stop))\
.filter(ee.Filter.calendarRange(17, 21, 'hour')) #10-2pm PCT, 11am-3pm MST
postCMIcol = ee.ImageCollection(f"NOAA/GOES/{goes}/MCMIPF").filter(ee.Filter.date(post_start, post_stop))\
.filter(ee.Filter.calendarRange(17, 21, 'hour'))#10-2pm PCT, 11am-3pm MST
postFDCcol = ee.ImageCollection(f"NOAA/GOES/{goes}/FDCF").filter(ee.Filter.date(post_start, post_stop))\
.filter(ee.Filter.calendarRange(17, 21, 'hour')) #10-2pm PCT, 11am-3pm MST
prejoinedGOES = ee.Join.inner('CMI','FDC').apply(
primary = preCMIcol,
secondary = preFDCcol,
condition = ee.Filter.maxDifference(
difference = 10, #milliseconds
leftField = 'system:time_start',
rightField = 'system:time_start',))
preMiddayGOEScol = ee.ImageCollection(prejoinedGOES.map(lambda object: MergeBands(object)))
preMiddayGOEScol = preMiddayGOEScol.map(GcalcCCsingle)
pre_meanNBR = preMiddayGOEScol.select(['NBR']).mean()
pre_meanNBR = pre_meanNBR.multiply(1.18).subtract(0.12)
postjoinedGOES = ee.Join.inner('CMI','FDC').apply(
primary = postCMIcol,
secondary = postFDCcol,
condition = ee.Filter.maxDifference(
difference = 10, #milliseconds
leftField = 'system:time_start',
rightField = 'system:time_start',))
postMiddayGOEScol = ee.ImageCollection(postjoinedGOES.map(lambda object: MergeBands(object)))
postMiddayGOEScol = postMiddayGOEScol.map(GcalcCCsingle)
post_meanNBR = postMiddayGOEScol.select(['NBR']).mean()
post_meanNBR = post_meanNBR.multiply(1.18).subtract(0.12)
dNBR_goes17 = pre_meanNBR.subtract(post_meanNBR).select('NBR')
#GOES-16
preCMIcol = ee.ImageCollection("NOAA/GOES/16/MCMIPF").filter(ee.Filter.date(pre_start, pre_stop))\
.filter(ee.Filter.calendarRange(17, 21, 'hour'))#10-2pm PCT, 11am-3pm MST
preFDCcol = ee.ImageCollection("NOAA/GOES/16/FDCF").filter(ee.Filter.date(pre_start, pre_stop))\
.filter(ee.Filter.calendarRange(17, 21, 'hour')) #10-2pm PCT, 11am-3pm MST
prejoinedGOES = ee.Join.inner('CMI','FDC').apply(
primary = preCMIcol,
secondary = preFDCcol,
condition = ee.Filter.maxDifference(
difference = 10, #milliseconds
leftField = 'system:time_start',
rightField = 'system:time_start',))
preMiddayGOEScol = ee.ImageCollection(prejoinedGOES.map(lambda object: MergeBands(object)))
preMiddayGOEScol = preMiddayGOEScol.map(GcalcCCsingle)
pre_meanNBR = preMiddayGOEScol.select(['NBR']).mean()
pre_meanNBR = pre_meanNBR.multiply(1.18).subtract(0.12)
postCMIcol = ee.ImageCollection("NOAA/GOES/16/MCMIPF").filter(ee.Filter.date(post_start, post_stop))\
.filter(ee.Filter.calendarRange(17, 21, 'hour'))#10-2pm PCT, 11am-3pm MST
postFDCcol = ee.ImageCollection("NOAA/GOES/16/FDCF").filter(ee.Filter.date(post_start, post_stop))\
.filter(ee.Filter.calendarRange(17, 21, 'hour')) #10-2pm PCT, 11am-3pm MST
postjoinedGOES = ee.Join.inner('CMI','FDC').apply(
primary = postCMIcol,
secondary = postFDCcol,
condition = ee.Filter.maxDifference(
difference = 10, #milliseconds
leftField = 'system:time_start',
rightField = 'system:time_start',))
postMiddayGOEScol = ee.ImageCollection(postjoinedGOES.map(lambda object: MergeBands(object)))
postMiddayGOEScol = postMiddayGOEScol.map(GcalcCCsingle)
post_meanNBR = postMiddayGOEScol.select(['NBR']).mean()
post_meanNBR = post_meanNBR.multiply(1.18).subtract(0.12)
dNBR_goes16 = pre_meanNBR.subtract(post_meanNBR).select('NBR')
dNBRclip_goes17= dNBR_goes17.clip(bbox)
dNBRclip_goes16= dNBR_goes16.clip(bbox)
dNBRdisp_goes17 = dNBRclip_goes17.displace(displacement18, 'bicubic')
dNBRdisp_goes16 = dNBRclip_goes16.displace(displacement16, 'bicubic')
dNBRgoes_compos = ee.ImageCollection([dNBRdisp_goes17,dNBRdisp_goes16]).mean()
#ACTIVE fire
activeFire18 = ee.ImageCollection(f"NOAA/GOES/{goes}/FDCF").filter(ee.Filter.date(pre_stop, post_stop))
activeFire16 = ee.ImageCollection(f"NOAA/GOES/16/FDCF").filter(ee.Filter.date(pre_stop, post_stop))
sumFRP18 = activeFire18.select('Power').sum().rename('sumFRP')
sumFRP16 = activeFire16.select('Power').sum().rename('sumFRP')
maskNoFire18 = sumFRP18.gt(200).displace(displacement18, 'bicubic')
maskNoFire16 = sumFRP16.gt(200).displace(displacement16, 'bicubic')
maskNoFire = ee.ImageCollection([maskNoFire18,maskNoFire16]).sum().gt(0)
'''
activeSNPP = ee.ImageCollection("NASA/LANCE/SNPP_VIIRS/C2").filter(ee.Filter.date(pre_stop, post_stop))
activeNOAA20 = ee.ImageCollection("NASA/LANCE/NOAA20_VIIRS/C2").filter(ee.Filter.date(pre_stop, post_stop))
sumFRP_SNPP = activeSNPP.select('confidence').max().rename('sumFRP')
sumFRP_NOAA20 = activeNOAA20.select('confidence').max().rename('sumFRP')
#maskNoFire = ee.ImageCollection([sumFRP_SNPP,sumFRP_NOAA20]).sum().gt(0)
maskNoFire = sumFRP_SNPP.gt(0)
'''
#VIIRS
preVIIRSimg = ee.ImageCollection("NASA/VIIRS/002/VNP09GA").filter(ee.Filter.date(pre_start, pre_stop)).mean()
#postVIIRSimgCol = ee.ImageCollection("NASA/VIIRS/002/VNP09GA").filter(ee.Filter.date(post_start, post_stop))
postVIIRSimgCol = ee.ImageCollection("NASA/VIIRS/002/VNP09GA").filter(ee.Filter.date(post_start, post_stop)) #TO FIX ON JUNE 18 sfork_startDate.advance(24, 'day'), sfork_startDate.advance(25,'day')
#Landsat
prelandsat8col = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filterDate(pre_start.advance(-10, 'day'), pre_stop).filterBounds(bbox)
postlandsat8col = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filterDate(post_start, post_stop).filterBounds(bbox)
prelandsat9col = ee.ImageCollection("LANDSAT/LC09/C02/T1_L2").filterDate(pre_start.advance(-10, 'day'), pre_stop).filterBounds(bbox)
postlandsat9col = ee.ImageCollection("LANDSAT/LC09/C02/T1_L2").filterDate(post_start, post_stop).filterBounds(bbox)
prelandsatcol = prelandsat8col.merge(prelandsat9col)
postlandsatcol = postlandsat8col.merge(postlandsat9col)
#Sentinel
presentCol = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filterDate(pre_start.advance(-10, 'day'), pre_stop).filterBounds(bbox)
postsentCol = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filterDate(post_start, post_stop).filterBounds(bbox) #TO FIX on JULY 5: sfork_startDate.advance(32, 'day'), sfork_startDate.advance(33,'day')
olderPostSentCol = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filterDate(sfork_startDate.advance(37, 'day'), sfork_startDate.advance(38,'day')).filterBounds(bbox)
#SAR
#SARimg = ee.Image('projects/ovcrge-ssec-burn-scar-map-c116/assets/burned_20200907_20200919_test')
#SARmask = SARimg.eq(1)
if postVIIRSimgCol.size().getInfo() > 0:
postVIIRSimg = postVIIRSimgCol.mean()
preVIIRSimg = VcalcNBR(preVIIRSimg)
postVIIRSimg = VcalcNBR(postVIIRSimg)
dNBR_viirs = preVIIRSimg.subtract(postVIIRSimg).select('NBR')
dNBRclip_viirs = dNBR_viirs.clip(bbox)
else:
dNBR_composite = dNBRgoes_compos
if postsentCol.size().getInfo() > 0:
presentMean = presentCol.mean()
postsentMean = postsentCol.mean()
postsent2Mean = olderPostSentCol.mean()
presentImg = ScalcNBR(presentMean)
postsentImg = ScalcNBR(postsentMean)
postsentImg2 = ScalcNBR(postsent2Mean)
postSentCombo = ee.ImageCollection([postsentImg,postsentImg2]).mosaic()
dnbr_sent = presentImg.subtract(postSentCombo).multiply(1.3).add(0.05).select('NBR')
dNBRclip_sent = dnbr_sent.clip(bbox)
dNBR_composite = ee.ImageCollection([dNBRgoes_compos,dNBRclip_viirs,dNBRclip_sent]).mosaic()
elif postlandsatcol.size().getInfo() > 0:
print(postlandsatcol.size().getInfo())
prelandsat = prelandsatcol.mean()
prelandsatImg = LcalcNBR(prelandsat)
postlandsat = postlandsatcol.mean()
postlandsatImg = LcalcNBR(postlandsat)
dNBR_landsat = prelandsatImg.subtract(postlandsatImg).multiply(3.23).add(0.01).select('NBR')
dNBRclip_ls = dNBR_landsat.clip(bbox)
dNBR_composite = ee.ImageCollection([dNBRgoes_compos,dNBRclip_viirs,dNBRclip_ls]).mosaic()
else:
dNBR_composite = ee.ImageCollection([dNBRgoes_compos,dNBRclip_viirs]).mosaic()
masked_compos = dNBR_composite.updateMask(maskNoFire) #(SARmask)
#doubleMasked_compos = masked_compos.updateMask(maskNoFire)
doubleMasked_compos = masked_compos.mask(masked_compos.mask()).float()
downloadArgs = {'name': 'VIIRS_burnMap',
'crs': 'EPSG:4326',
'scale': 60,
'region': bbox}
url = doubleMasked_compos.getDownloadURL(downloadArgs)
print(url)
noDataVal = -9999
unmaskedImage = doubleMasked_compos.unmask(noDataVal, False)
task = ee.batch.Export.image.toDrive(**{
'image': unmaskedImage,
'description': "Composite_burnMap6",
'folder': "Earth Engine Outputs",
'fileNamePrefix': "Composite_burnMap_noData_VIIRS_June18_espg3857_60m",
'region': bbox,
'crs': 'EPSG:3857',
'scale': 60,})
#task.start()
return masked_compos
self.clear_specific_layers()
if fire == "North Complex":
north_complex = calc_nbr(north_startDate.advance(-7, 'day'), north_startDate, north_startDate.advance(elapDayNum, 'day'), north_startDate.advance(elapDay_plusOne,'day'), north_complex_bb, 17)
self.addLayer(north_complex, dNBRvisParams, 'North Complex GOES NBR', True)
self.centerObject(north_complex_bb, 9)
file = north_complex
elif fire == "Dixie":
dixie = calc_nbr(dixie_startDate.advance(-7, 'day'), dixie_startDate, dixie_startDate.advance(elapDayNum, 'day'), dixie_startDate.advance(elapDay_plusOne,'day'), dixie_bb, 17)
self.addLayer(dixie, dNBRvisParams, 'Dixie Complex GOES NBR', True)
self.centerObject(dixie_bb, 9)
file = dixie
elif fire == "Cameron Peak":
cam_peak = calc_nbr(cam_startDate.advance(-7, 'day'), cam_startDate, cam_startDate.advance(elapDayNum, 'day'), cam_startDate.advance(elapDay_plusOne,'day'), cam_peak_bb, 17)
self.addLayer(cam_peak, dNBRvisParams, 'Cameron Peak GOES NBR', True)
self.centerObject(cam_peak_bb, 9)
file = cam_peak
elif fire == "August Complex":
aug_complex = calc_nbr(aug_startDate.advance(-7, 'day'), aug_startDate, aug_startDate.advance(elapDayNum, 'day'), aug_startDate.advance(elapDay_plusOne,'day'), aug_complex_bb, 17)
self.addLayer(aug_complex, dNBRvisParams, 'August Complex GOES NBR', True)
self.centerObject(aug_complex_bb, 9)
file = aug_complex
elif fire == "South Fork":
sfork = calc_nbr(sfork_startDate.advance(-7, 'day'), sfork_startDate, sfork_startDate.advance(elapDayNum, 'day'), sfork_startDate.advance(elapDay_plusOne,'day'), sfork_bb, 18)
self.addLayer(sfork, dNBRvisParams, 'South Fork GOES NBR', True)
self.centerObject(sfork_bb, 9)
file = sfork
def clear_specific_layers(self):
layers_to_keep = ['OpenStreetMap']
layers = list(self.layers)
for layer in layers:
if layer.name not in layers_to_keep:
self.remove_layer(layer)
def add_selector(self):
selector = widgets.Dropdown(options=fireList, value="South Fork", description='Wildfire Case Study:', style={'description_width': '125px'}, layout=widgets.Layout(width='400px'))
def on_selector_change(change):
if change['name'] == 'value':
selected_fire.value = change['new']
self.customize_ee_data(selected_fire.value, selected_days.value)
selector.observe(on_selector_change, names='value')
self.add_widget(selector, position="topleft")
def add_intSlider(self):
slider = widgets.IntSlider(value=selected_days.value,min=1,max=40,step=1,description='Elapsed days:',style={'description_width': '125px'}, layout=widgets.Layout(width='400px'))
def on_slider_change(change):
if change['name'] == 'value':
selected_days.value = change['new']
self.customize_ee_data(selected_fire.value, selected_days.value)
slider.observe(on_slider_change, names='value')
self.add_widget(slider, position="topleft")
def add_dwnldButton(self):
button = widgets.Button(description='Download',icon='cloud-arrow-down')
#def on_button_click(change, file):
# if change['name'] == 'value':
# selected_days.value = change['new']
# self.download_ee_image(file, "trial_file.tif", scale=30)
def on_button_click(b):
# Get the currently selected fire and elapsed days
fire = selected_fire.value
elapDays = selected_days.value
# Customize the EE data and download the image
file = self.customize_ee_data(fire, elapDays)
#self.download_ee_image(file, f"{fire}_NBR_{elapDays}days.tif", scale=30)
button.observe(on_button_click)
self.add_widget(button, position="topleft")
@solara.component
def Page():
with solara.Column(align="center"):
markdown = """
## Historical Western US wildfires from 2020-2021 """
solara.Markdown(markdown)
# Isolation is required to prevent the map from overlapping navigation (when screen width < 960px)
with solara.Column(style={"isolation": "isolate"}):
map_widget = Map.element(
center=[39, -120.5],
zoom=8,
height="600px",
toolbar_ctrl=False
)