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 )