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Dockerfile

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+FROM jupyter/base-notebook:latest
+
+# Install required packages
+RUN mamba install -c conda-forge leafmap geopandas localtileserver -y && \
+    fix-permissions "${CONDA_DIR}" && \
+    fix-permissions "/home/${NB_USER}"
+
+# Copy the requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+# Copy the entire project directory into the container
+COPY . /home/${NB_USER}
+
+# Set the working directory
+WORKDIR /home/${NB_USER}
+
+# Set the PROJ_LIB environment variable
+ENV PROJ_LIB='/opt/conda/share/proj'
+
+# Ensure the notebook user owns the home directory
+USER root
+RUN chown -R ${NB_UID} ${HOME}
+USER ${NB_USER}
+
+# Expose the port for Solara
+EXPOSE 8765
+
+# Run the Solara app
+CMD ["solara", "run", "pages", "--host=0.0.0.0"]

Home.py

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+import solara
+
+@solara.component
+def Page():
+    with solara.Column(align="center"):
+        markdown = """
+        ## Real-time wildfire burn mapping 
+        
+        ### About the project
+
+        **A proof of concept illustrating wildfire burn severity maps with emerging clarity while the fires progress. 
+        Target users are forecasters and emergency managers responding to post-fire risks including debris flows and landslides.**
+
+        More project description, etc, etc. 
+
+        **Case Studies from 2020 and 2021 Western US wildfire seasons **
+
+        - August Complex, CA (2020)
+        - Cameron Peak, CO (2020)
+        - Dixie Fire, CA (2021)
+        - North Complex, CA (2020)
+
+        
+         **Current 2024 wildfires over 10,000 acres **
+        
+        ### How to use the app
+
+        1. Select the fire from the drop-down menu
+
+        2. Export image to Google Drive as a geotiff
+
+        3. 
+
+        ### Support
+        
+        Initial funding for wildland burn scar mapping came through the NOAA JPSS/RRPG Fire and Smoke Initiative. 
+        This supported the initial tests of BRIDGE maps using dNDVI. Subsequent funding supported the development of dNBR mapping and an effort 
+        to tie support the near real-time distribution of incident-based fire detection and related satellite imagery products through the Next Generation Fire System (NGFS). 
+        Current funding from the NOAA Weather Program Office (WPO) is supporting the refinement of our Google Earth Engine App (GEE) 
+        and integration of GEE burn scar output with AWIPS (see example above) for Weather Forecast Offices, Regional Offices, and the Weather Prediction Center.
+        
+        """
+
+        solara.Markdown(markdown)

LICENSE

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+MIT License
+
+Copyright (c) 2023 Open Geospatial Solutions
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+title: Solara Geemap
+emoji: 🏃
+colorFrom: blue
+colorTo: purple
+sdk: docker
+pinned: false
+license: mit
+app_port: 8765
+---
+
+## Earth Engine Web Apps
+
+### Introduction
+
+**A collection of Earth Engine web apps developed using [Solara](https://github.com/widgetti/solara) and geemap**
+
+- Web App: <https://giswqs-solara-geemap.hf.space>
+- GitHub: <https://github.com/opengeos/solara-geemap>
+- Hugging Face: <https://huggingface.co/spaces/giswqs/solara-geemap>
+
+### How to deploy this app on Hugging Face Spaces
+
+1. Go to <https://huggingface.co/spaces/giswqs/solara-geemap/tree/main> and duplicate the space to your own space.
+
+   ![](https://i.imgur.com/gTg4V2x.png)
+
+2. You need to set `EARTHENGINE_TOKEN` in order to use Earth Engine. The token value should be copied from the following file depending on your operating system:
+
+   ```text
+   Windows: C:\\Users\\USERNAME\\.config\\earthengine\\credentials
+   Linux: /home/USERNAME/.config/earthengine/credentials
+   MacOS: /Users/USERNAME/.config/earthengine/credentials
+   ```
+
+   Simply open the file and copy **ALL** the content to the `EARTHENGINE_TOKEN` environment variable.
+
+   ![](https://i.imgur.com/i04gzyH.png)
+
+   ![](https://i.imgur.com/Ex37Ut7.png)
+
+   Alternatively, you can run the following code to retrieve your Earth Engine token:
+
+   ```python
+   import geemap
+   geemap.get_ee_token()
+   ```
+
+   Copy all the content of the printed token and set it as the `EARTHENGINE_TOKEN` environment variable.
+
+3. After the space is built successfully, click the `Embed this Space` menu and find the `Direct URL` for the app, such as <https://giswqs-solara-geemap.hf.space>.
+
+   ![](https://i.imgur.com/DNM36sk.png)
+
+   ![](https://i.imgur.com/KX82lSf.png)
+
+4. Add your own apps (\*.py) to the `pages` folder.
+5. Commit and push your changes to the repository. Wait for the space to be built successfully.

current_fires.py

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+import ee
+import geemap
+import solara
+import ipywidgets as widgets
+import datetime
+
+#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)
+
+#createDates = NIFC_perims_716.aggregate_array('attr_Cre_1')
+#incidentIDs = NIFC_perims_716.aggregate_array('poly_Incid')
+#fireList = incidentIDs.getInfo()
+fireList = wildfire_names = [ "FRESNO JUNE LIGHTNING COMPLEX", "Larch Creek","Deadman","Cow Valley","0404 RV LONE ROCK",
+    "PIONEER","South Fork", "Deer Springs","Basin","Lake","Horse Gulch","Falls","Silver King","Indios"]
+selected_fire = solara.reactive(fireList[6])
+dNBRvisParams = {'min': 0.0,'max': 0.8, 'palette': ['green', 'yellow','orange','red']}
+today = datetime.datetime.today().strftime('%Y-%m-%d')
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)        
+        self.add_basemap('OpenStreetMap')
+
+        self.customize_ee_data(selected_fire.value, today)
+        self.add_selector()
+        self.add_dwnldButton()
+        self.add("layer_manager")
+        self.remove("draw_control")
+
+        
+    def customize_ee_data(self, fireID, elapDays):
+        NIFC_perims_716 = ee.FeatureCollection('projects/ovcrge-ssec-burn-scar-map-c116/assets/NIFC_perimeters_7-16')
+        fire = NIFC_perims_716.filter(ee.Filter.eq('poly_Incid',fireID)).first()
+        timestamp = fire.get('attr_Cre_1')
+        geom = fire.geometry()
+        
+        startDate = ee.Date(timestamp)#.format('YYYY-MM-dd')
+        endDate = ee.Date.parse('YYYY-MM-dd', str(today))
+        
+        boundingBox = ee.Geometry(geom.buffer(5000).bounds())
+
+        elapDayNum = ee.Number(10)
+        elapDay_plusOne = elapDayNum.add(ee.Number(1))
+
+        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)) #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() 
+                        presentImg = ScalcNBR(presentMean)
+                        postsentImg = ScalcNBR(postsentMean)
+                        dnbr_sent =  presentImg.subtract(postsentImg).multiply(1.3).add(0.05).select('NBR')
+                        dNBRclip_sent = dnbr_sent.clip(bbox)
+                        dNBR_composite = ee.ImageCollection([dNBRgoes_compos,dNBRclip_sent]).mosaic() #dNBRclip_viirs SHOULD GO IN IF UP TO DATE
+                elif postlandsatcol.size().getInfo() > 0:
+                        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_ls]).mosaic() #dNBRclip_viirs  SHOULD GO IN IF UP TO DATE
+                else:
+                        dNBR_composite = ee.ImageCollection([dNBRgoes_compos]).mosaic() #dNBRclip_viirs  SHOULD GO IN IF UP TO DATE
+                
+                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()
+        
+        fireImg = calc_nbr(startDate.advance(-7, 'day'), startDate, endDate.advance(-3, 'day'), endDate, boundingBox, 18) 
+        self.addLayer(fireImg, dNBRvisParams, fireID, True)
+        self.centerObject(boundingBox, 10)
+        file = fireImg
+
+    
+    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=fireList[6], description='Current wildfire :', 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, today)
+
+        selector.observe(on_selector_change, names='value')
+        self.add_widget(selector, position="topleft")
+
+
+    def add_dwnldButton(self):
+        button = widgets.Button(description='Export to Drive',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 = today
+            
+            # 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 = """
+        ## Current 2024 wildfires over 10,000 acres"""
+        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
+        )

historical_fires.py

@@ -0,0 +1,429 @@
+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
+        )

pages/Home.py

@@ -0,0 +1,44 @@
+import solara
+
+@solara.component
+def Page():
+    with solara.Column(align="center"):
+        markdown = """
+        ## Real-time wildfire burn mapping 
+        
+        ### About the project
+
+        **A proof of concept illustrating wildfire burn severity maps with emerging clarity while the fires progress. 
+        Target users are forecasters and emergency managers responding to post-fire risks including debris flows and landslides.**
+
+        More project description, etc, etc. 
+
+        **Case Studies from 2020 and 2021 Western US wildfire seasons **
+
+        - August Complex, CA (2020)
+        - Cameron Peak, CO (2020)
+        - Dixie Fire, CA (2021)
+        - North Complex, CA (2020)
+
+        
+         **Current 2024 wildfires over 10,000 acres **
+        
+        ### How to use the app
+
+        1. Select the fire from the drop-down menu
+
+        2. Export image to Google Drive as a geotiff
+
+        3. 
+
+        ### Support
+        
+        Initial funding for wildland burn scar mapping came through the NOAA JPSS/RRPG Fire and Smoke Initiative. 
+        This supported the initial tests of BRIDGE maps using dNDVI. Subsequent funding supported the development of dNBR mapping and an effort 
+        to tie support the near real-time distribution of incident-based fire detection and related satellite imagery products through the Next Generation Fire System (NGFS). 
+        Current funding from the NOAA Weather Program Office (WPO) is supporting the refinement of our Google Earth Engine App (GEE) 
+        and integration of GEE burn scar output with AWIPS (see example above) for Weather Forecast Offices, Regional Offices, and the Weather Prediction Center.
+        
+        """
+
+        solara.Markdown(markdown)

pages/current_fires.py

@@ -0,0 +1,399 @@
+import ee
+import geemap
+import solara
+import ipywidgets as widgets
+import datetime
+
+#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)
+
+#createDates = NIFC_perims_716.aggregate_array('attr_Cre_1')
+#incidentIDs = NIFC_perims_716.aggregate_array('poly_Incid')
+#fireList = incidentIDs.getInfo()
+fireList = wildfire_names = [ "FRESNO JUNE LIGHTNING COMPLEX", "Larch Creek","Deadman","Cow Valley","0404 RV LONE ROCK",
+    "PIONEER","South Fork", "Deer Springs","Basin","Lake","Horse Gulch","Falls","Silver King","Indios"]
+selected_fire = solara.reactive(fireList[6])
+dNBRvisParams = {'min': 0.0,'max': 0.8, 'palette': ['green', 'yellow','orange','red']}
+today = datetime.datetime.today().strftime('%Y-%m-%d')
+
+class Map(geemap.Map):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)        
+        self.add_basemap('OpenStreetMap')
+
+        self.customize_ee_data(selected_fire.value, today)
+        self.add_selector()
+        self.add_dwnldButton()
+        self.add("layer_manager")
+        self.remove("draw_control")
+
+        
+    def customize_ee_data(self, fireID, elapDays):
+        NIFC_perims_716 = ee.FeatureCollection('projects/ovcrge-ssec-burn-scar-map-c116/assets/NIFC_perimeters_7-16')
+        fire = NIFC_perims_716.filter(ee.Filter.eq('poly_Incid',fireID)).first()
+        timestamp = fire.get('attr_Cre_1')
+        geom = fire.geometry()
+        
+        startDate = ee.Date(timestamp)#.format('YYYY-MM-dd')
+        endDate = ee.Date.parse('YYYY-MM-dd', str(today))
+        
+        boundingBox = ee.Geometry(geom.buffer(5000).bounds())
+
+        elapDayNum = ee.Number(10)
+        elapDay_plusOne = elapDayNum.add(ee.Number(1))
+
+        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)) #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() 
+                        presentImg = ScalcNBR(presentMean)
+                        postsentImg = ScalcNBR(postsentMean)
+                        dnbr_sent =  presentImg.subtract(postsentImg).multiply(1.3).add(0.05).select('NBR')
+                        dNBRclip_sent = dnbr_sent.clip(bbox)
+                        dNBR_composite = ee.ImageCollection([dNBRgoes_compos,dNBRclip_sent]).mosaic() #dNBRclip_viirs SHOULD GO IN IF UP TO DATE
+                elif postlandsatcol.size().getInfo() > 0:
+                        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_ls]).mosaic() #dNBRclip_viirs  SHOULD GO IN IF UP TO DATE
+                else:
+                        dNBR_composite = ee.ImageCollection([dNBRgoes_compos]).mosaic() #dNBRclip_viirs  SHOULD GO IN IF UP TO DATE
+                
+                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()
+        
+        fireImg = calc_nbr(startDate.advance(-7, 'day'), startDate, endDate.advance(-3, 'day'), endDate, boundingBox, 18) 
+        self.addLayer(fireImg, dNBRvisParams, fireID, True)
+        self.centerObject(boundingBox, 10)
+        file = fireImg
+
+    
+    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=fireList[6], description='Current wildfire :', 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, today)
+
+        selector.observe(on_selector_change, names='value')
+        self.add_widget(selector, position="topleft")
+
+
+    def add_dwnldButton(self):
+        button = widgets.Button(description='Export to Drive',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 = today
+            
+            # 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 = """
+        ## Current 2024 wildfires over 10,000 acres"""
+        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
+        )

pages/historical_fires.py

@@ -0,0 +1,429 @@
+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
+        )

requirements.txt

@@ -0,0 +1,6 @@
+geemap
+solara== 1.33.0
+geopandas
+pydantic< 2.0 
+ipyevents
+ipywidgets

utils/NBR_calculations.py

@@ -0,0 +1,130 @@
+import ee
+
+''' 0   Good quality fire
+    1   Good quality fire-free land
+    2   Invalid due to opaque cloud
+    3   Invalid due to surface type or sunglint or LZA threshold exceeded or off earth or missing input data
+    4   Invalid due to bad input data
+    5   Invalid due to algorithm failure'''
+# 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 GcalcNBR (goesImg, aoi):
+  #day = ee.Date(eachImg.get('system:time_start')).get('day','America/Los_Angeles')
+  fireMode = goesImg.select('fireMode')
+  fireMin = goesImg.select('fireMin')
+
+  CMI_QF3 = goesImg.select('DQF_C03').int()
+  CMI_QF6 = goesImg.select('DQF_C06').int()
+
+  # To include active fire pixels - fireMin.lt(2)\ for next line
+  QF_Mask = (fireMin.eq(1)\
+    .Or(fireMin.gt(3)))\
+    .And(CMI_QF3.lt(2))\
+    .And(CMI_QF6.lt(2))\
+    .rename('QFmask');
+  GOESm = goesImg.select(['CMI_C03','CMI_C06']).updateMask(QF_Mask)
+  NBR = GOESm.normalizedDifference(['CMI_C03', 'CMI_C06']).toFloat().rename('NBR')
+
+  return goesImg.addBands([NBR,QF_Mask])
+
+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)