Delete burn_mapper

burn_mapper/.gitattributes

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burn_mapper/.github/workflows/sync-hf.yml

@@ -1,20 +0,0 @@
-name: Sync to Hugging Face hub
-on:
-  push:
-    branches: [main]
-
-  # to run this workflow manually from the Actions tab
-  workflow_dispatch:
-
-jobs:
-  sync-to-hub:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Push to hub
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        run: git push --force https://giswqs:[email protected]/spaces/giswqs/solara-geemap main

burn_mapper/.gitignore

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-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
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-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-private/
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
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-.pdm.toml
-
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-__pypackages__/
-
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-
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-*.sage.py
-
-# Environments
-.env
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-
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-.spyderproject
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-
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-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
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-
-# Pyre type checker
-.pyre/
-
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-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/

burn_mapper/Dockerfile

@@ -1,30 +0,0 @@
-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"]

burn_mapper/LICENSE

@@ -1,21 +0,0 @@
-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.

burn_mapper/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.

burn_mapper/pages/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)

burn_mapper/pages/current_fires.py

@@ -1,399 +0,0 @@
-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
-        )

burn_mapper/pages/historical_fires.py

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

burn_mapper/requirements.txt

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

burn_mapper/utils/NBR_calculations.py

@@ -1,130 +0,0 @@
-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)