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  1. solara-carbonARR-geemap/.gitattributes +0 -35
  2. solara-carbonARR-geemap/.github/workflows/sync-hf.yml +0 -20
  3. solara-carbonARR-geemap/.gitignore +0 -161
  4. solara-carbonARR-geemap/.pre-commit-config.yaml +0 -33
  5. solara-carbonARR-geemap/CSVs/ARR_projects.csv +0 -0
  6. solara-carbonARR-geemap/Dockerfile +0 -21
  7. solara-carbonARR-geemap/LICENSE +0 -21
  8. solara-carbonARR-geemap/README.md +0 -58
  9. solara-carbonARR-geemap/notebooks/01_basic.ipynb +0 -88
  10. solara-carbonARR-geemap/notebooks/02_inspector.ipynb +0 -104
  11. solara-carbonARR-geemap/notebooks/03_plotting.ipynb +0 -97
  12. solara-carbonARR-geemap/notebooks/04_split_map.ipynb +0 -115
  13. solara-carbonARR-geemap/notebooks/05_timelapse.ipynb +0 -79
  14. solara-carbonARR-geemap/notebooks/06_timeseries.ipynb +0 -348
  15. solara-carbonARR-geemap/notebooks/07_jrc.ipynb +0 -205
  16. solara-carbonARR-geemap/notebooks/08_compare.ipynb +0 -335
  17. solara-carbonARR-geemap/notebooks/09_teste.ipynb +0 -352
  18. solara-carbonARR-geemap/notebooks/esa_stats_ucs_rj2.csv +0 -41
  19. solara-carbonARR-geemap/pages/00_home.py +0 -80
  20. solara-carbonARR-geemap/pages/01_ARR 1º.py +0 -102
  21. solara-carbonARR-geemap/pages/02_ARR 2º.py +0 -100
  22. solara-carbonARR-geemap/pages/03_ARR 3°.py +0 -100
  23. solara-carbonARR-geemap/pages/04_ARR 4º.py +0 -102
  24. solara-carbonARR-geemap/pages/05_ARR 5º.py +0 -103
  25. solara-carbonARR-geemap/pages/06_ARR 6º.py +0 -103
  26. solara-carbonARR-geemap/pages/07_ARR 7º.py +0 -103
  27. solara-carbonARR-geemap/pages/08_ARR 8º.py +0 -102
  28. solara-carbonARR-geemap/pages/09_ARR 9º.py +0 -103
  29. solara-carbonARR-geemap/requirements.txt +0 -5
solara-carbonARR-geemap/.gitattributes DELETED
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- *.7z filter=lfs diff=lfs merge=lfs -text
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- *.arrow filter=lfs diff=lfs merge=lfs -text
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- *.bin filter=lfs diff=lfs merge=lfs -text
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- *.bz2 filter=lfs diff=lfs merge=lfs -text
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- *.ckpt filter=lfs diff=lfs merge=lfs -text
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- *.h5 filter=lfs diff=lfs merge=lfs -text
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- *.joblib filter=lfs diff=lfs merge=lfs -text
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- *.lfs.* filter=lfs diff=lfs merge=lfs -text
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- *.mlmodel filter=lfs diff=lfs merge=lfs -text
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- *.model filter=lfs diff=lfs merge=lfs -text
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- *.msgpack filter=lfs diff=lfs merge=lfs -text
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- *.npy filter=lfs diff=lfs merge=lfs -text
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- *.parquet filter=lfs diff=lfs merge=lfs -text
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- *.pb filter=lfs diff=lfs merge=lfs -text
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- *.pickle filter=lfs diff=lfs merge=lfs -text
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- *.pkl filter=lfs diff=lfs merge=lfs -text
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- *.pt filter=lfs diff=lfs merge=lfs -text
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- *.pth filter=lfs diff=lfs merge=lfs -text
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- *.rar filter=lfs diff=lfs merge=lfs -text
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- *.safetensors filter=lfs diff=lfs merge=lfs -text
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- saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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- *.tar.* filter=lfs diff=lfs merge=lfs -text
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- *.tar filter=lfs diff=lfs merge=lfs -text
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- *.tflite filter=lfs diff=lfs merge=lfs -text
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- *.tgz filter=lfs diff=lfs merge=lfs -text
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- *.wasm filter=lfs diff=lfs merge=lfs -text
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- *.xz filter=lfs diff=lfs merge=lfs -text
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- *.zip filter=lfs diff=lfs merge=lfs -text
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- *.zst filter=lfs diff=lfs merge=lfs -text
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- *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/.github/workflows/sync-hf.yml DELETED
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- name: Sync to Hugging Face hub
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- on:
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- push:
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- branches: [main]
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-
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- # to run this workflow manually from the Actions tab
7
- workflow_dispatch:
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-
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- jobs:
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- sync-to-hub:
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- runs-on: ubuntu-latest
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- steps:
13
- - uses: actions/checkout@v3
14
- with:
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- fetch-depth: 0
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- lfs: true
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- - name: Push to hub
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- env:
19
- HF_TOKEN: ${{ secrets.HF_TOKEN }}
20
- run: git push --force https://giswqs:[email protected]/spaces/giswqs/solara-geemap main
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/.gitignore DELETED
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- # Byte-compiled / optimized / DLL files
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- __pycache__/
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- *.py[cod]
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- *$py.class
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-
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- # C extensions
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- *.so
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-
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- # Distribution / packaging
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- .Python
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- build/
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- develop-eggs/
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- dist/
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- downloads/
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- eggs/
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- .eggs/
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- lib/
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- lib64/
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- parts/
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- sdist/
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- var/
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- wheels/
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- share/python-wheels/
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- *.egg-info/
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- .installed.cfg
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- *.egg
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- MANIFEST
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- private/
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-
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- # PyInstaller
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- # Usually these files are written by a python script from a template
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- # before PyInstaller builds the exe, so as to inject date/other infos into it.
33
- *.manifest
34
- *.spec
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-
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- # Installer logs
37
- pip-log.txt
38
- pip-delete-this-directory.txt
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-
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- # Unit test / coverage reports
41
- htmlcov/
42
- .tox/
43
- .nox/
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- .coverage
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- .coverage.*
46
- .cache
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- nosetests.xml
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- coverage.xml
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- *.cover
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- *.py,cover
51
- .hypothesis/
52
- .pytest_cache/
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- cover/
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-
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- # Translations
56
- *.mo
57
- *.pot
58
-
59
- # Django stuff:
60
- *.log
61
- local_settings.py
62
- db.sqlite3
63
- db.sqlite3-journal
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-
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- # Flask stuff:
66
- instance/
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- .webassets-cache
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-
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- # Scrapy stuff:
70
- .scrapy
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-
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- # Sphinx documentation
73
- docs/_build/
74
-
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- # PyBuilder
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- .pybuilder/
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- target/
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-
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- # Jupyter Notebook
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- .ipynb_checkpoints
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-
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- # IPython
83
- profile_default/
84
- ipython_config.py
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-
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- # pyenv
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- # For a library or package, you might want to ignore these files since the code is
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- # intended to run in multiple environments; otherwise, check them in:
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- # .python-version
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-
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- # pipenv
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- # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
93
- # However, in case of collaboration, if having platform-specific dependencies or dependencies
94
- # having no cross-platform support, pipenv may install dependencies that don't work, or not
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- # install all needed dependencies.
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- #Pipfile.lock
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-
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- # poetry
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- # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
100
- # This is especially recommended for binary packages to ensure reproducibility, and is more
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- # commonly ignored for libraries.
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- # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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- #poetry.lock
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-
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- # pdm
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- # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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- #pdm.lock
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- # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
109
- # in version control.
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- # https://pdm.fming.dev/#use-with-ide
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- .pdm.toml
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-
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- # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
114
- __pypackages__/
115
-
116
- # Celery stuff
117
- celerybeat-schedule
118
- celerybeat.pid
119
-
120
- # SageMath parsed files
121
- *.sage.py
122
-
123
- # Environments
124
- .env
125
- .venv
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- env/
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- venv/
128
- ENV/
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- env.bak/
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- venv.bak/
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-
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- # Spyder project settings
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- .spyderproject
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- .spyproject
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-
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- # Rope project settings
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- .ropeproject
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-
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- # mkdocs documentation
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- /site
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-
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- # mypy
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- .mypy_cache/
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- .dmypy.json
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- dmypy.json
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-
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- # Pyre type checker
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- .pyre/
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-
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- # pytype static type analyzer
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- .pytype/
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-
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- # Cython debug symbols
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- cython_debug/
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-
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- # PyCharm
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- # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
158
- # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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- # and can be added to the global gitignore or merged into this file. For a more nuclear
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- # option (not recommended) you can uncomment the following to ignore the entire idea folder.
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- #.idea/
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/.pre-commit-config.yaml DELETED
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- repos:
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- - repo: https://github.com/pre-commit/pre-commit-hooks
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- rev: v5.0.0
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- hooks:
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- - id: check-toml
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- - id: check-yaml
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- - id: end-of-file-fixer
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- types: [python]
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- - id: trailing-whitespace
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- - id: requirements-txt-fixer
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- - id: check-added-large-files
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- args: ["--maxkb=500"]
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-
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- - repo: https://github.com/psf/black
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- rev: 24.10.0
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- hooks:
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- - id: black-jupyter
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- language_version: python3
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-
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- - repo: https://github.com/codespell-project/codespell
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- rev: v2.3.0
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- hooks:
23
- - id: codespell
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- args:
25
- [
26
- "--ignore-words-list=gis,timeseries,sav,slowy",
27
- "--skip=*.json,*.csv",
28
- ]
29
-
30
- - repo: https://github.com/kynan/nbstripout
31
- rev: 0.7.1
32
- hooks:
33
- - id: nbstripout
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/CSVs/ARR_projects.csv DELETED
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solara-carbonARR-geemap/Dockerfile DELETED
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- FROM jupyter/base-notebook:latest
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-
3
- RUN mamba install -c conda-forge leafmap geopandas localtileserver -y && \
4
- fix-permissions "${CONDA_DIR}" && \
5
- fix-permissions "/home/${NB_USER}"
6
-
7
- COPY requirements.txt .
8
- RUN pip install -r requirements.txt
9
-
10
- RUN mkdir ./pages
11
- COPY /pages ./pages
12
-
13
- ENV PROJ_LIB='/opt/conda/share/proj'
14
-
15
- USER root
16
- RUN chown -R ${NB_UID} ${HOME}
17
- USER ${NB_USER}
18
-
19
- EXPOSE 8765
20
-
21
- CMD ["solara", "run", "./pages", "--host=0.0.0.0"]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/LICENSE DELETED
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- MIT License
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-
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- Copyright (c) 2023 Open Geospatial Solutions
4
-
5
- Permission is hereby granted, free of charge, to any person obtaining a copy
6
- of this software and associated documentation files (the "Software"), to deal
7
- in the Software without restriction, including without limitation the rights
8
- to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
- copies of the Software, and to permit persons to whom the Software is
10
- furnished to do so, subject to the following conditions:
11
-
12
- The above copyright notice and this permission notice shall be included in all
13
- copies or substantial portions of the Software.
14
-
15
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
- OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
- SOFTWARE.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/README.md DELETED
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- ---
2
- title: Solara Geemap
3
- emoji: 🏃
4
- colorFrom: blue
5
- colorTo: purple
6
- sdk: docker
7
- pinned: false
8
- license: mit
9
- app_port: 8765
10
- ---
11
-
12
- ## Earth Engine Web Apps
13
-
14
- ### Introduction
15
-
16
- **A collection of Earth Engine web apps developed using [Solara](https://github.com/widgetti/solara) and geemap**
17
-
18
- - Web App: <https://giswqs-solara-geemap.hf.space>
19
- - GitHub: <https://github.com/opengeos/solara-geemap>
20
- - Hugging Face: <https://huggingface.co/spaces/giswqs/solara-geemap>
21
-
22
- ### How to deploy this app on Hugging Face Spaces
23
-
24
- 1. Go to <https://huggingface.co/spaces/giswqs/solara-geemap/tree/main> and duplicate the space to your own space.
25
-
26
- ![](https://i.imgur.com/gTg4V2x.png)
27
-
28
- 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:
29
-
30
- ```text
31
- Windows: C:\\Users\\USERNAME\\.config\\earthengine\\credentials
32
- Linux: /home/USERNAME/.config/earthengine/credentials
33
- MacOS: /Users/USERNAME/.config/earthengine/credentials
34
- ```
35
-
36
- Simply open the file and copy **ALL** the content to the `EARTHENGINE_TOKEN` environment variable.
37
-
38
- ![](https://i.imgur.com/i04gzyH.png)
39
-
40
- ![](https://i.imgur.com/Ex37Ut7.png)
41
-
42
- Alternatively, you can run the following code to retrieve your Earth Engine token:
43
-
44
- ```python
45
- import geemap
46
- geemap.get_ee_token()
47
- ```
48
-
49
- Copy all the content of the printed token and set it as the `EARTHENGINE_TOKEN` environment variable.
50
-
51
- 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>.
52
-
53
- ![](https://i.imgur.com/DNM36sk.png)
54
-
55
- ![](https://i.imgur.com/KX82lSf.png)
56
-
57
- 4. Add your own apps (\*.py) to the `pages` folder.
58
- 5. Commit and push your changes to the repository. Wait for the space to be built successfully.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/01_basic.ipynb DELETED
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- {
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- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/01_basic.ipynb)"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": null,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "# %pip install -U geemap solara"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
21
- "execution_count": null,
22
- "metadata": {},
23
- "outputs": [],
24
- "source": [
25
- "import geemap\n",
26
- "import solara\n",
27
- "\n",
28
- "zoom = solara.reactive(4)\n",
29
- "center = solara.reactive((40, -100))\n",
30
- "bounds = solara.reactive(None)\n",
31
- "\n",
32
- "\n",
33
- "@solara.component\n",
34
- "def Page():\n",
35
- " # Isolation is required to prevent the map from overlapping navigation (when screen width < 960px)\n",
36
- " with solara.Column(\n",
37
- " style={\"min-width\": \"500px\", \"height\": \"780px\", \"isolation\": \"isolate\"}\n",
38
- " ):\n",
39
- " # solara components support reactive variables\n",
40
- " solara.SliderInt(label=\"Zoom level\", value=zoom, min=1, max=20)\n",
41
- " # using 3rd party widget library require wiring up the events manually\n",
42
- " # using zoom.value and zoom.set\n",
43
- " geemap.Map.element( # type: ignore\n",
44
- " zoom=zoom.value,\n",
45
- " on_zoom=zoom.set,\n",
46
- " center=center.value,\n",
47
- " on_center=center.set,\n",
48
- " on_bounds=bounds.set,\n",
49
- " scroll_wheel_zoom=True,\n",
50
- " height=\"600px\",\n",
51
- " )\n",
52
- " solara.Text(f\"Zoom: {zoom.value}\")\n",
53
- " solara.Text(f\"Center: {center.value}\")\n",
54
- " solara.Text(f\"Bounds: {bounds.value}\")"
55
- ]
56
- },
57
- {
58
- "cell_type": "code",
59
- "execution_count": null,
60
- "metadata": {},
61
- "outputs": [],
62
- "source": [
63
- "Page()"
64
- ]
65
- }
66
- ],
67
- "metadata": {
68
- "kernelspec": {
69
- "display_name": "geo",
70
- "language": "python",
71
- "name": "python3"
72
- },
73
- "language_info": {
74
- "codemirror_mode": {
75
- "name": "ipython",
76
- "version": 3
77
- },
78
- "file_extension": ".py",
79
- "mimetype": "text/x-python",
80
- "name": "python",
81
- "nbconvert_exporter": "python",
82
- "pygments_lexer": "ipython3",
83
- "version": "3.11.8"
84
- }
85
- },
86
- "nbformat": 4,
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- "nbformat_minor": 2
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/02_inspector.ipynb DELETED
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- {
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- "cells": [
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- {
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- "cell_type": "markdown",
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- "metadata": {},
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- "source": [
7
- "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/02_inspector.ipynb)"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": null,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "# %pip install -U geemap solara"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
25
- "import ee\n",
26
- "import geemap\n",
27
- "import solara\n",
28
- "\n",
29
- "\n",
30
- "class Map(geemap.Map):\n",
31
- " def __init__(self, **kwargs):\n",
32
- " super().__init__(**kwargs)\n",
33
- " self.add_ee_data()\n",
34
- " self.add(\"layer_manager\")\n",
35
- " self.add(\"inspector\")\n",
36
- "\n",
37
- " def add_ee_data(self):\n",
38
- " # Add Earth Engine dataset\n",
39
- " dem = ee.Image(\"USGS/SRTMGL1_003\")\n",
40
- " landsat7 = ee.Image(\"LANDSAT/LE7_TOA_5YEAR/1999_2003\").select(\n",
41
- " [\"B1\", \"B2\", \"B3\", \"B4\", \"B5\", \"B7\"]\n",
42
- " )\n",
43
- " states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
44
- "\n",
45
- " # Set visualization parameters.\n",
46
- " vis_params = {\n",
47
- " \"min\": 0,\n",
48
- " \"max\": 4000,\n",
49
- " \"palette\": [\"006633\", \"E5FFCC\", \"662A00\", \"D8D8D8\", \"F5F5F5\"],\n",
50
- " }\n",
51
- "\n",
52
- " # Add Earth Engine layers to Map\n",
53
- " self.addLayer(\n",
54
- " landsat7,\n",
55
- " {\"bands\": [\"B4\", \"B3\", \"B2\"], \"min\": 20, \"max\": 200, \"gamma\": 2.0},\n",
56
- " \"Landsat 7\",\n",
57
- " True,\n",
58
- " )\n",
59
- " self.addLayer(dem, vis_params, \"SRTM DEM\", True, 1)\n",
60
- " self.addLayer(states, {}, \"US States\")\n",
61
- "\n",
62
- "\n",
63
- "@solara.component\n",
64
- "def Page():\n",
65
- " with solara.Column(style={\"min-width\": \"500px\", \"isolation\": \"isolate\"}):\n",
66
- " Map.element(\n",
67
- " center=[40, -100],\n",
68
- " zoom=4,\n",
69
- " height=\"600px\",\n",
70
- " )"
71
- ]
72
- },
73
- {
74
- "cell_type": "code",
75
- "execution_count": null,
76
- "metadata": {},
77
- "outputs": [],
78
- "source": [
79
- "Page()"
80
- ]
81
- }
82
- ],
83
- "metadata": {
84
- "kernelspec": {
85
- "display_name": "geo",
86
- "language": "python",
87
- "name": "python3"
88
- },
89
- "language_info": {
90
- "codemirror_mode": {
91
- "name": "ipython",
92
- "version": 3
93
- },
94
- "file_extension": ".py",
95
- "mimetype": "text/x-python",
96
- "name": "python",
97
- "nbconvert_exporter": "python",
98
- "pygments_lexer": "ipython3",
99
- "version": "3.11.8"
100
- }
101
- },
102
- "nbformat": 4,
103
- "nbformat_minor": 2
104
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/03_plotting.ipynb DELETED
@@ -1,97 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/03_plotting.ipynb)"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": null,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "# %pip install -U geemap solara"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
21
- "execution_count": null,
22
- "metadata": {},
23
- "outputs": [],
24
- "source": [
25
- "import ee\n",
26
- "import geemap\n",
27
- "import solara\n",
28
- "\n",
29
- "\n",
30
- "class Map(geemap.Map):\n",
31
- " def __init__(self, **kwargs):\n",
32
- " super().__init__(**kwargs)\n",
33
- " self.add_ee_data()\n",
34
- " self.add_plot_gui()\n",
35
- "\n",
36
- " def add_ee_data(self):\n",
37
- " landsat7 = ee.Image(\"LANDSAT/LE7_TOA_5YEAR/1999_2003\").select(\n",
38
- " [\"B1\", \"B2\", \"B3\", \"B4\", \"B5\", \"B7\"]\n",
39
- " )\n",
40
- "\n",
41
- " landsat_vis = {\"bands\": [\"B4\", \"B3\", \"B2\"], \"gamma\": 1.4}\n",
42
- " self.addLayer(landsat7, landsat_vis, \"Landsat\")\n",
43
- "\n",
44
- " hyperion = ee.ImageCollection(\"EO1/HYPERION\").filter(\n",
45
- " ee.Filter.date(\"2016-01-01\", \"2017-03-01\")\n",
46
- " )\n",
47
- "\n",
48
- " hyperion_vis = {\n",
49
- " \"min\": 1000.0,\n",
50
- " \"max\": 14000.0,\n",
51
- " \"gamma\": 2.5,\n",
52
- " }\n",
53
- " self.addLayer(hyperion, hyperion_vis, \"Hyperion\")\n",
54
- "\n",
55
- "\n",
56
- "@solara.component\n",
57
- "def Page():\n",
58
- " with solara.Column(style={\"min-width\": \"500px\", \"isolation\": \"isolate\"}):\n",
59
- " Map.element(\n",
60
- " center=[40, -100],\n",
61
- " zoom=4,\n",
62
- " height=\"600px\",\n",
63
- " )"
64
- ]
65
- },
66
- {
67
- "cell_type": "code",
68
- "execution_count": null,
69
- "metadata": {},
70
- "outputs": [],
71
- "source": [
72
- "Page()"
73
- ]
74
- }
75
- ],
76
- "metadata": {
77
- "kernelspec": {
78
- "display_name": "geo",
79
- "language": "python",
80
- "name": "python3"
81
- },
82
- "language_info": {
83
- "codemirror_mode": {
84
- "name": "ipython",
85
- "version": 3
86
- },
87
- "file_extension": ".py",
88
- "mimetype": "text/x-python",
89
- "name": "python",
90
- "nbconvert_exporter": "python",
91
- "pygments_lexer": "ipython3",
92
- "version": "3.11.8"
93
- }
94
- },
95
- "nbformat": 4,
96
- "nbformat_minor": 2
97
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/04_split_map.ipynb DELETED
@@ -1,115 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/04_split_map.ipynb)"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": null,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "# %pip install -U geemap solara"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
21
- "execution_count": null,
22
- "metadata": {},
23
- "outputs": [],
24
- "source": [
25
- "import ee\n",
26
- "import geemap\n",
27
- "import solara\n",
28
- "\n",
29
- "\n",
30
- "class Map(geemap.Map):\n",
31
- " def __init__(self, **kwargs):\n",
32
- " super().__init__(**kwargs)\n",
33
- " self.add_ee_data()\n",
34
- "\n",
35
- " def add_ee_data(self):\n",
36
- " # Select the eight NLCD epochs after 2000.\n",
37
- " years = [\"2001\", \"2004\", \"2006\", \"2008\", \"2011\", \"2013\", \"2016\", \"2019\"]\n",
38
- "\n",
39
- " # Get an NLCD image by year.\n",
40
- " def getNLCD(year):\n",
41
- " # Import the NLCD collection.\n",
42
- " dataset = ee.ImageCollection(\"USGS/NLCD_RELEASES/2019_REL/NLCD\")\n",
43
- "\n",
44
- " # Filter the collection by year.\n",
45
- " nlcd = dataset.filter(ee.Filter.eq(\"system:index\", year)).first()\n",
46
- "\n",
47
- " # Select the land cover band.\n",
48
- " landcover = nlcd.select(\"landcover\")\n",
49
- " return landcover\n",
50
- "\n",
51
- " ## Create an NLCD image collection for the selected years.\n",
52
- " collection = ee.ImageCollection(ee.List(years).map(lambda year: getNLCD(year)))\n",
53
- "\n",
54
- " # Create a list of labels to populate the dropdown list.\n",
55
- " labels = [f\"NLCD {year}\" for year in years]\n",
56
- "\n",
57
- " # Add a split-panel map for visualizing NLCD land cover change.\n",
58
- " self.ts_inspector(\n",
59
- " left_ts=collection,\n",
60
- " right_ts=collection,\n",
61
- " left_names=labels,\n",
62
- " right_names=labels,\n",
63
- " )\n",
64
- "\n",
65
- " # Add the NLCD legend to the map.\n",
66
- " self.add_legend(\n",
67
- " title=\"NLCD Land Cover Type\",\n",
68
- " builtin_legend=\"NLCD\",\n",
69
- " height=\"460px\",\n",
70
- " add_header=False,\n",
71
- " )\n",
72
- "\n",
73
- "\n",
74
- "@solara.component\n",
75
- "def Page():\n",
76
- " with solara.Column(style={\"min-width\": \"500px\", \"isolation\": \"isolate\"}):\n",
77
- " Map.element(\n",
78
- " center=[40, -100],\n",
79
- " zoom=4,\n",
80
- " height=\"600px\",\n",
81
- " )"
82
- ]
83
- },
84
- {
85
- "cell_type": "code",
86
- "execution_count": null,
87
- "metadata": {},
88
- "outputs": [],
89
- "source": [
90
- "Page()"
91
- ]
92
- }
93
- ],
94
- "metadata": {
95
- "kernelspec": {
96
- "display_name": "geo",
97
- "language": "python",
98
- "name": "python3"
99
- },
100
- "language_info": {
101
- "codemirror_mode": {
102
- "name": "ipython",
103
- "version": 3
104
- },
105
- "file_extension": ".py",
106
- "mimetype": "text/x-python",
107
- "name": "python",
108
- "nbconvert_exporter": "python",
109
- "pygments_lexer": "ipython3",
110
- "version": "3.11.8"
111
- }
112
- },
113
- "nbformat": 4,
114
- "nbformat_minor": 2
115
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/05_timelapse.ipynb DELETED
@@ -1,79 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/05_timelapse.ipynb)"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": null,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "# %pip install -U geemap solara"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
21
- "execution_count": null,
22
- "metadata": {},
23
- "outputs": [],
24
- "source": [
25
- "import geemap\n",
26
- "import solara\n",
27
- "\n",
28
- "\n",
29
- "class Map(geemap.Map):\n",
30
- " def __init__(self, **kwargs):\n",
31
- " super().__init__(**kwargs)\n",
32
- " self.add_basemap(\"Esri.WorldImagery\")\n",
33
- " self.add_gui(\"timelapse\", basemap=None)\n",
34
- "\n",
35
- "\n",
36
- "@solara.component\n",
37
- "def Page():\n",
38
- " with solara.Column(style={\"min-width\": \"500px\", \"isolation\": \"isolate\"}):\n",
39
- " Map.element(\n",
40
- " center=[20, -0],\n",
41
- " zoom=2,\n",
42
- " height=\"750px\",\n",
43
- " zoom_ctrl=False,\n",
44
- " measure_ctrl=False,\n",
45
- " )"
46
- ]
47
- },
48
- {
49
- "cell_type": "code",
50
- "execution_count": null,
51
- "metadata": {},
52
- "outputs": [],
53
- "source": [
54
- "Page()"
55
- ]
56
- }
57
- ],
58
- "metadata": {
59
- "kernelspec": {
60
- "display_name": "geo",
61
- "language": "python",
62
- "name": "python3"
63
- },
64
- "language_info": {
65
- "codemirror_mode": {
66
- "name": "ipython",
67
- "version": 3
68
- },
69
- "file_extension": ".py",
70
- "mimetype": "text/x-python",
71
- "name": "python",
72
- "nbconvert_exporter": "python",
73
- "pygments_lexer": "ipython3",
74
- "version": "3.11.8"
75
- }
76
- },
77
- "nbformat": 4,
78
- "nbformat_minor": 2
79
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/06_timeseries.ipynb DELETED
@@ -1,348 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/06_timeseries.ipynb)"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": null,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "# %pip install -U geemap solara"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
21
- "execution_count": null,
22
- "metadata": {},
23
- "outputs": [],
24
- "source": [
25
- "import geemap\n",
26
- "import ipywidgets as widgets\n",
27
- "import solara\n",
28
- "from geemap import get_current_year, jslink_slider_label\n",
29
- "\n",
30
- "\n",
31
- "class Map(geemap.Map):\n",
32
- " def __init__(self, **kwargs):\n",
33
- " super().__init__(**kwargs)\n",
34
- " self.add_basemap(\"Esri.WorldImagery\")\n",
35
- " self.add_ts_gui(position=\"topright\")\n",
36
- "\n",
37
- " def clean_up(self):\n",
38
- " if hasattr(self, \"slider_ctrl\") and self.slider_ctrl is not None:\n",
39
- " self.remove(self.slider_ctrl)\n",
40
- " delattr(self, \"slider_ctrl\")\n",
41
- "\n",
42
- " layer = self.find_layer(\"Time series\")\n",
43
- " if layer is not None:\n",
44
- " self.remove(layer)\n",
45
- " layer = self.find_layer(\"Image X\")\n",
46
- " if layer is not None:\n",
47
- " self.remove(layer)\n",
48
- "\n",
49
- " draw_layer = self.find_layer(\"Drawn Features\")\n",
50
- " if draw_layer is not None:\n",
51
- " self.remove(draw_layer)\n",
52
- "\n",
53
- " def add_ts_gui(self, position=\"topright\", **kwargs):\n",
54
- "\n",
55
- " widget_width = \"350px\"\n",
56
- " padding = \"0px 0px 0px 5px\" # upper, right, bottom, left\n",
57
- " style = {\"description_width\": \"initial\"}\n",
58
- " current_year = get_current_year()\n",
59
- "\n",
60
- " collection = widgets.Dropdown(\n",
61
- " options=[\n",
62
- " \"Landsat TM-ETM-OLI Surface Reflectance\",\n",
63
- " ],\n",
64
- " value=\"Landsat TM-ETM-OLI Surface Reflectance\",\n",
65
- " description=\"Collection:\",\n",
66
- " layout=widgets.Layout(width=widget_width, padding=padding),\n",
67
- " style=style,\n",
68
- " )\n",
69
- " bands = widgets.Dropdown(\n",
70
- " description=\"Bands:\",\n",
71
- " options=[\n",
72
- " \"Red/Green/Blue\",\n",
73
- " \"NIR/Red/Green\",\n",
74
- " \"SWIR2/SWIR1/NIR\",\n",
75
- " \"NIR/SWIR1/Red\",\n",
76
- " \"SWIR2/NIR/Red\",\n",
77
- " \"SWIR2/SWIR1/Red\",\n",
78
- " \"SWIR1/NIR/Blue\",\n",
79
- " \"NIR/SWIR1/Blue\",\n",
80
- " \"SWIR2/NIR/Green\",\n",
81
- " \"SWIR1/NIR/Red\",\n",
82
- " ],\n",
83
- " value=\"SWIR1/NIR/Red\",\n",
84
- " style=style,\n",
85
- " layout=widgets.Layout(width=\"195px\", padding=padding),\n",
86
- " )\n",
87
- "\n",
88
- " frequency = widgets.Dropdown(\n",
89
- " description=\"Frequency:\",\n",
90
- " options=[\"year\", \"quarter\", \"month\"],\n",
91
- " value=\"year\",\n",
92
- " style=style,\n",
93
- " layout=widgets.Layout(width=\"150px\", padding=padding),\n",
94
- " )\n",
95
- "\n",
96
- " start_year = widgets.IntSlider(\n",
97
- " description=\"Start Year:\",\n",
98
- " value=1984,\n",
99
- " min=1984,\n",
100
- " max=current_year,\n",
101
- " readout=False,\n",
102
- " style=style,\n",
103
- " layout=widgets.Layout(width=\"138px\", padding=padding),\n",
104
- " )\n",
105
- "\n",
106
- " start_year_label = widgets.Label(\"1984\")\n",
107
- " jslink_slider_label(start_year, start_year_label)\n",
108
- "\n",
109
- " end_year = widgets.IntSlider(\n",
110
- " description=\"End Year:\",\n",
111
- " value=current_year,\n",
112
- " min=1984,\n",
113
- " max=current_year,\n",
114
- " readout=False,\n",
115
- " style=style,\n",
116
- " layout=widgets.Layout(width=\"138px\", padding=padding),\n",
117
- " )\n",
118
- " end_year_label = widgets.Label(str(current_year))\n",
119
- " jslink_slider_label(end_year, end_year_label)\n",
120
- "\n",
121
- " start_month = widgets.IntSlider(\n",
122
- " description=\"Start Month:\",\n",
123
- " value=5,\n",
124
- " min=1,\n",
125
- " max=12,\n",
126
- " readout=False,\n",
127
- " style=style,\n",
128
- " layout=widgets.Layout(width=\"145px\", padding=padding),\n",
129
- " )\n",
130
- "\n",
131
- " start_month_label = widgets.Label(\n",
132
- " \"5\",\n",
133
- " layout=widgets.Layout(width=\"20px\", padding=padding),\n",
134
- " )\n",
135
- " jslink_slider_label(start_month, start_month_label)\n",
136
- "\n",
137
- " end_month = widgets.IntSlider(\n",
138
- " description=\"End Month:\",\n",
139
- " value=10,\n",
140
- " min=1,\n",
141
- " max=12,\n",
142
- " readout=False,\n",
143
- " style=style,\n",
144
- " layout=widgets.Layout(width=\"155px\", padding=padding),\n",
145
- " )\n",
146
- "\n",
147
- " end_month_label = widgets.Label(\"10\")\n",
148
- " jslink_slider_label(end_month, end_month_label)\n",
149
- "\n",
150
- " output = widgets.Output()\n",
151
- "\n",
152
- " button_width = \"113px\"\n",
153
- " apply_btn = widgets.Button(\n",
154
- " description=\"Time slider\",\n",
155
- " button_style=\"primary\",\n",
156
- " tooltip=\"Click to create timeseries\",\n",
157
- " style=style,\n",
158
- " layout=widgets.Layout(padding=\"0px\", width=button_width),\n",
159
- " )\n",
160
- "\n",
161
- " split_btn = widgets.Button(\n",
162
- " description=\"Split map\",\n",
163
- " button_style=\"primary\",\n",
164
- " tooltip=\"Click to create timeseries\",\n",
165
- " style=style,\n",
166
- " layout=widgets.Layout(padding=\"0px\", width=button_width),\n",
167
- " )\n",
168
- "\n",
169
- " reset_btn = widgets.Button(\n",
170
- " description=\"Reset\",\n",
171
- " button_style=\"primary\",\n",
172
- " style=style,\n",
173
- " layout=widgets.Layout(padding=\"0px\", width=button_width),\n",
174
- " )\n",
175
- "\n",
176
- " vbox = widgets.VBox(\n",
177
- " [\n",
178
- " collection,\n",
179
- " widgets.HBox([bands, frequency]),\n",
180
- " widgets.HBox([start_year, start_year_label, end_year, end_year_label]),\n",
181
- " widgets.HBox(\n",
182
- " [start_month, start_month_label, end_month, end_month_label]\n",
183
- " ),\n",
184
- " widgets.HBox([apply_btn, split_btn, reset_btn]),\n",
185
- " output,\n",
186
- " ]\n",
187
- " )\n",
188
- " self.add_widget(vbox, position=position, add_header=True)\n",
189
- "\n",
190
- " def apply_btn_click(change):\n",
191
- "\n",
192
- " if hasattr(self, \"slider_ctrl\") and self.slider_ctrl is not None:\n",
193
- " self.remove(self.slider_ctrl)\n",
194
- " delattr(self, \"slider_ctrl\")\n",
195
- "\n",
196
- " with output:\n",
197
- " output.clear_output()\n",
198
- " if self.user_roi is None:\n",
199
- " output.append_stdout(\"Please draw a ROI first.\")\n",
200
- " else:\n",
201
- " output.append_stdout(\"Creating time series...\")\n",
202
- " collection = geemap.landsat_timeseries(\n",
203
- " roi=self.user_roi,\n",
204
- " start_year=start_year.value,\n",
205
- " end_year=end_year.value,\n",
206
- " start_date=str(start_month.value).zfill(2) + \"-01\",\n",
207
- " end_date=str(end_month.value).zfill(2) + \"-01\",\n",
208
- " frequency=frequency.value,\n",
209
- " )\n",
210
- " vis_params = {\n",
211
- " \"bands\": bands.value.split(\"/\"),\n",
212
- " \"min\": 0,\n",
213
- " \"max\": 0.4,\n",
214
- " }\n",
215
- "\n",
216
- " if frequency.value == \"year\":\n",
217
- " date_format = \"YYYY\"\n",
218
- " elif frequency.value == \"quarter\":\n",
219
- " date_format = \"YYYY-MM\"\n",
220
- " elif frequency.value == \"month\":\n",
221
- " date_format = \"YYYY-MM\"\n",
222
- "\n",
223
- " self.add_time_slider(\n",
224
- " collection,\n",
225
- " region=self.user_roi,\n",
226
- " vis_params=vis_params,\n",
227
- " date_format=date_format,\n",
228
- " )\n",
229
- " self._draw_control.clear()\n",
230
- " draw_layer = self.find_layer(\"Drawn Features\")\n",
231
- " if draw_layer is not None:\n",
232
- " self.remove(draw_layer)\n",
233
- " output.clear_output()\n",
234
- "\n",
235
- " apply_btn.on_click(apply_btn_click)\n",
236
- "\n",
237
- " def split_btn_click(change):\n",
238
- "\n",
239
- " if hasattr(self, \"slider_ctrl\") and self.slider_ctrl is not None:\n",
240
- " self.remove(self.slider_ctrl)\n",
241
- " delattr(self, \"slider_ctrl\")\n",
242
- "\n",
243
- " with output:\n",
244
- " output.clear_output()\n",
245
- " if self.user_roi is None:\n",
246
- " output.append_stdout(\"Please draw a ROI first.\")\n",
247
- " else:\n",
248
- " output.append_stdout(\"Creating time series...\")\n",
249
- " collection = geemap.landsat_timeseries(\n",
250
- " roi=self.user_roi,\n",
251
- " start_year=start_year.value,\n",
252
- " end_year=end_year.value,\n",
253
- " start_date=str(start_month.value).zfill(2) + \"-01\",\n",
254
- " end_date=str(end_month.value).zfill(2) + \"-01\",\n",
255
- " frequency=frequency.value,\n",
256
- " )\n",
257
- " vis_params = {\n",
258
- " \"bands\": bands.value.split(\"/\"),\n",
259
- " \"min\": 0,\n",
260
- " \"max\": 0.4,\n",
261
- " }\n",
262
- "\n",
263
- " if frequency.value == \"year\":\n",
264
- " date_format = \"YYYY\"\n",
265
- " dates = geemap.image_dates(collection, date_format).getInfo()\n",
266
- " elif frequency.value == \"quarter\":\n",
267
- " date_format = \"YYYY-MM\"\n",
268
- " dates = geemap.image_dates(collection, date_format).getInfo()\n",
269
- " elif frequency.value == \"month\":\n",
270
- " date_format = \"YYYY-MM\"\n",
271
- " dates = geemap.image_dates(collection, date_format).getInfo()\n",
272
- "\n",
273
- " self.ts_inspector(\n",
274
- " collection,\n",
275
- " left_names=dates,\n",
276
- " left_vis=vis_params,\n",
277
- " add_close_button=True,\n",
278
- " )\n",
279
- " output.clear_output()\n",
280
- "\n",
281
- " try:\n",
282
- " self._draw_control.clear()\n",
283
- " draw_layer = self.find_layer(\"Drawn Features\")\n",
284
- " if draw_layer is not None:\n",
285
- " self.remove(draw_layer)\n",
286
- " except Exception as e:\n",
287
- " print(e)\n",
288
- "\n",
289
- " split_btn.on_click(split_btn_click)\n",
290
- "\n",
291
- " def reset_btn_click(change):\n",
292
- " output.clear_output()\n",
293
- " self.clean_up()\n",
294
- "\n",
295
- " reset_btn.on_click(reset_btn_click)\n",
296
- "\n",
297
- "\n",
298
- "@solara.component\n",
299
- "def Page():\n",
300
- " with solara.Column(style={\"min-width\": \"500px\"}):\n",
301
- " Map.element(\n",
302
- " center=[20, -0],\n",
303
- " zoom=2,\n",
304
- " height=\"750px\",\n",
305
- " zoom_ctrl=False,\n",
306
- " measure_ctrl=False,\n",
307
- " )"
308
- ]
309
- },
310
- {
311
- "cell_type": "code",
312
- "execution_count": null,
313
- "metadata": {},
314
- "outputs": [],
315
- "source": [
316
- "Page()"
317
- ]
318
- },
319
- {
320
- "cell_type": "code",
321
- "execution_count": null,
322
- "metadata": {},
323
- "outputs": [],
324
- "source": []
325
- }
326
- ],
327
- "metadata": {
328
- "kernelspec": {
329
- "display_name": "geo",
330
- "language": "python",
331
- "name": "python3"
332
- },
333
- "language_info": {
334
- "codemirror_mode": {
335
- "name": "ipython",
336
- "version": 3
337
- },
338
- "file_extension": ".py",
339
- "mimetype": "text/x-python",
340
- "name": "python",
341
- "nbconvert_exporter": "python",
342
- "pygments_lexer": "ipython3",
343
- "version": "3.11.8"
344
- }
345
- },
346
- "nbformat": 4,
347
- "nbformat_minor": 2
348
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/07_jrc.ipynb DELETED
@@ -1,205 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/07_jrc.ipynb)"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": null,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "# %pip install -U geemap solara"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
21
- "execution_count": null,
22
- "metadata": {},
23
- "outputs": [],
24
- "source": [
25
- "import ee\n",
26
- "import geemap\n",
27
- "import ipywidgets as widgets\n",
28
- "from IPython.display import display\n",
29
- "import solara\n",
30
- "\n",
31
- "\n",
32
- "class Map(geemap.Map):\n",
33
- " def __init__(self, **kwargs):\n",
34
- " super().__init__(**kwargs)\n",
35
- " self.add_basemap(\"Esri.WorldImagery\")\n",
36
- " self.add_ee_data()\n",
37
- " self.add_buttons(add_header=True)\n",
38
- "\n",
39
- " def add_ee_data(self):\n",
40
- "\n",
41
- " dataset = ee.Image(\"JRC/GSW1_4/GlobalSurfaceWater\")\n",
42
- " image = dataset.select([\"occurrence\"])\n",
43
- " vis_params = {\n",
44
- " \"min\": 0.0,\n",
45
- " \"max\": 100.0,\n",
46
- " \"palette\": [\"ffffff\", \"ffbbbb\", \"0000ff\"],\n",
47
- " }\n",
48
- " self.addLayer(image, vis_params, \"Occurrence\")\n",
49
- " self.add_colorbar(\n",
50
- " vis_params, label=\"Water occurrence (%)\", layer_name=\"Occurrence\"\n",
51
- " )\n",
52
- "\n",
53
- " def add_buttons(self, position=\"topright\", **kwargs):\n",
54
- " padding = \"0px 5px 0px 5px\"\n",
55
- " widget = widgets.VBox(layout=widgets.Layout(padding=padding))\n",
56
- " layout = widgets.Layout(width=\"auto\")\n",
57
- " style = {\"description_width\": \"initial\"}\n",
58
- " hist_btn = widgets.Button(description=\"Occurrence\", layout=layout)\n",
59
- " bar_btn = widgets.Button(description=\"Monthly history\", layout=layout)\n",
60
- " reset_btn = widgets.Button(description=\"Reset\", layout=layout)\n",
61
- " scale = widgets.IntSlider(\n",
62
- " min=30, max=1000, value=90, description=\"Scale\", layout=layout, style=style\n",
63
- " )\n",
64
- " month_slider = widgets.IntRangeSlider(\n",
65
- " description=\"Months\",\n",
66
- " value=[5, 10],\n",
67
- " min=1,\n",
68
- " max=12,\n",
69
- " step=1,\n",
70
- " layout=layout,\n",
71
- " style=style,\n",
72
- " )\n",
73
- " widget.children = [\n",
74
- " widgets.HBox([hist_btn, bar_btn, reset_btn]),\n",
75
- " month_slider,\n",
76
- " scale,\n",
77
- " ]\n",
78
- " self.add_widget(widget, position=position, **kwargs)\n",
79
- " output = widgets.Output()\n",
80
- " self.add_widget(output, position=\"bottomleft\", add_header=False)\n",
81
- "\n",
82
- " def hist_btn_click(b):\n",
83
- " region = self.user_roi\n",
84
- " if region is not None:\n",
85
- " output.clear_output()\n",
86
- " output.append_stdout(\"Computing histogram...\")\n",
87
- " image = ee.Image(\"JRC/GSW1_4/GlobalSurfaceWater\").select([\"occurrence\"])\n",
88
- " self.default_style = {\"cursor\": \"wait\"}\n",
89
- " hist = geemap.image_histogram(\n",
90
- " image,\n",
91
- " region,\n",
92
- " scale=scale.value,\n",
93
- " height=350,\n",
94
- " width=550,\n",
95
- " x_label=\"Water Occurrence (%)\",\n",
96
- " y_label=\"Pixel Count\",\n",
97
- " layout_args={\n",
98
- " \"title\": dict(x=0.5),\n",
99
- " \"margin\": dict(l=0, r=0, t=10, b=0),\n",
100
- " },\n",
101
- " return_df=False,\n",
102
- " )\n",
103
- "\n",
104
- " with output:\n",
105
- " output.clear_output()\n",
106
- " display(hist)\n",
107
- " self.default_style = {\"cursor\": \"default\"}\n",
108
- " else:\n",
109
- " output.clear_output()\n",
110
- " with output:\n",
111
- " output.append_stdout(\"Please draw a region of interest first.\")\n",
112
- "\n",
113
- " hist_btn.on_click(hist_btn_click)\n",
114
- "\n",
115
- " def bar_btn_click(b):\n",
116
- " region = self.user_roi\n",
117
- " if region is not None:\n",
118
- " self.default_style = {\"cursor\": \"wait\"}\n",
119
- " output.clear_output()\n",
120
- " output.append_stdout(\"Computing monthly history...\")\n",
121
- " bar = geemap.jrc_hist_monthly_history(\n",
122
- " region=region,\n",
123
- " scale=scale.value,\n",
124
- " height=350,\n",
125
- " width=550,\n",
126
- " layout_args={\n",
127
- " \"title\": dict(x=0.5),\n",
128
- " \"margin\": dict(l=0, r=0, t=10, b=0),\n",
129
- " },\n",
130
- " frequency=\"month\",\n",
131
- " start_month=month_slider.value[0],\n",
132
- " end_month=month_slider.value[1],\n",
133
- " denominator=1e4,\n",
134
- " y_label=\"Area (ha)\",\n",
135
- " )\n",
136
- "\n",
137
- " with output:\n",
138
- " output.clear_output()\n",
139
- " display(bar)\n",
140
- " self.default_style = {\"cursor\": \"default\"}\n",
141
- " else:\n",
142
- " output.clear_output()\n",
143
- " with output:\n",
144
- " output.append_stdout(\"Please draw a region of interest first.\")\n",
145
- "\n",
146
- " bar_btn.on_click(bar_btn_click)\n",
147
- "\n",
148
- " def reset_btn_click(b):\n",
149
- " self._draw_control.clear()\n",
150
- " output.clear_output()\n",
151
- "\n",
152
- " reset_btn.on_click(reset_btn_click)\n",
153
- "\n",
154
- "\n",
155
- "@solara.component\n",
156
- "def Page():\n",
157
- " with solara.Column(style={\"min-width\": \"500px\"}):\n",
158
- " Map.element(\n",
159
- " center=[20, -0],\n",
160
- " zoom=2,\n",
161
- " height=\"750px\",\n",
162
- " zoom_ctrl=False,\n",
163
- " measure_ctrl=False,\n",
164
- " )"
165
- ]
166
- },
167
- {
168
- "cell_type": "code",
169
- "execution_count": null,
170
- "metadata": {},
171
- "outputs": [],
172
- "source": [
173
- "Page()"
174
- ]
175
- },
176
- {
177
- "cell_type": "code",
178
- "execution_count": null,
179
- "metadata": {},
180
- "outputs": [],
181
- "source": []
182
- }
183
- ],
184
- "metadata": {
185
- "kernelspec": {
186
- "display_name": "geo",
187
- "language": "python",
188
- "name": "python3"
189
- },
190
- "language_info": {
191
- "codemirror_mode": {
192
- "name": "ipython",
193
- "version": 3
194
- },
195
- "file_extension": ".py",
196
- "mimetype": "text/x-python",
197
- "name": "python",
198
- "nbconvert_exporter": "python",
199
- "pygments_lexer": "ipython3",
200
- "version": "3.11.8"
201
- }
202
- },
203
- "nbformat": 4,
204
- "nbformat_minor": 2
205
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/08_compare.ipynb DELETED
@@ -1,335 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/solara-geemap/blob/main/notebooks/08_compare.ipynb)"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": null,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "# %pip install -U geemap solara"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
21
- "execution_count": null,
22
- "metadata": {},
23
- "outputs": [],
24
- "source": [
25
- "import ee\n",
26
- "import geemap\n",
27
- "import ipywidgets as widgets\n",
28
- "import solara\n",
29
- "from datetime import date\n",
30
- "\n",
31
- "\n",
32
- "class Map(geemap.Map):\n",
33
- " def __init__(self, **kwargs):\n",
34
- " super().__init__(**kwargs)\n",
35
- " self.add_basemap(\"Esri.WorldImagery\")\n",
36
- " self.add_gui_widget(add_header=True)\n",
37
- "\n",
38
- " def clean_up(self):\n",
39
- "\n",
40
- " layers = [\n",
41
- " \"Pre-event Image\",\n",
42
- " \"Post-event Image\",\n",
43
- " \"Pre-event NDWI\",\n",
44
- " \"Post-event NDWI\",\n",
45
- " \"Pre-event Water\",\n",
46
- " \"Post-event Water\",\n",
47
- " \"Disappeared Water\",\n",
48
- " \"New Water\",\n",
49
- " ]\n",
50
- " for layer_name in layers:\n",
51
- " layer = self.find_layer(layer_name)\n",
52
- " if layer is not None:\n",
53
- " self.remove(layer)\n",
54
- "\n",
55
- " def add_gui_widget(self, position=\"topright\", **kwargs):\n",
56
- "\n",
57
- " widget = widgets.VBox(layout=widgets.Layout(padding=\"0px 5px 0px 5px\"))\n",
58
- " pre_widget = widgets.HBox()\n",
59
- " post_widget = widgets.HBox()\n",
60
- " layout = widgets.Layout(width=\"auto\")\n",
61
- " style = {\"description_width\": \"initial\"}\n",
62
- " padding = \"0px 5px 0px 5px\"\n",
63
- " pre_start_date = widgets.DatePicker(\n",
64
- " description=\"Start\",\n",
65
- " value=date(2014, 1, 1),\n",
66
- " style=style,\n",
67
- " layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
68
- " )\n",
69
- " pre_end_date = widgets.DatePicker(\n",
70
- " description=\"End\",\n",
71
- " value=date(2014, 12, 31),\n",
72
- " style=style,\n",
73
- " layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
74
- " )\n",
75
- " pre_cloud_cover = widgets.IntSlider(\n",
76
- " description=\"Cloud\",\n",
77
- " min=0,\n",
78
- " max=100,\n",
79
- " value=25,\n",
80
- " step=1,\n",
81
- " readout=False,\n",
82
- " style=style,\n",
83
- " layout=widgets.Layout(padding=padding, width=\"130px\"),\n",
84
- " )\n",
85
- " pre_cloud_label = widgets.Label(value=str(pre_cloud_cover.value))\n",
86
- " geemap.jslink_slider_label(pre_cloud_cover, pre_cloud_label)\n",
87
- " pre_widget.children = [\n",
88
- " pre_start_date,\n",
89
- " pre_end_date,\n",
90
- " pre_cloud_cover,\n",
91
- " pre_cloud_label,\n",
92
- " ]\n",
93
- " post_start_date = widgets.DatePicker(\n",
94
- " description=\"Start\",\n",
95
- " value=date(2024, 1, 1),\n",
96
- " style=style,\n",
97
- " layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
98
- " )\n",
99
- " post_end_date = widgets.DatePicker(\n",
100
- " description=\"End\",\n",
101
- " value=date(2024, 12, 31),\n",
102
- " style=style,\n",
103
- " layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
104
- " )\n",
105
- " post_cloud_cover = widgets.IntSlider(\n",
106
- " description=\"Cloud\",\n",
107
- " min=0,\n",
108
- " max=100,\n",
109
- " value=30,\n",
110
- " step=1,\n",
111
- " readout=False,\n",
112
- " style=style,\n",
113
- " layout=widgets.Layout(padding=padding, width=\"130px\"),\n",
114
- " )\n",
115
- " post_cloud_label = widgets.Label(value=str(post_cloud_cover.value))\n",
116
- " geemap.jslink_slider_label(post_cloud_cover, post_cloud_label)\n",
117
- " post_widget.children = [\n",
118
- " post_start_date,\n",
119
- " post_end_date,\n",
120
- " post_cloud_cover,\n",
121
- " post_cloud_label,\n",
122
- " ]\n",
123
- "\n",
124
- " apply_btn = widgets.Button(description=\"Apply\", layout=layout)\n",
125
- " reset_btn = widgets.Button(description=\"Reset\", layout=layout)\n",
126
- " buttons = widgets.HBox([apply_btn, reset_btn])\n",
127
- " output = widgets.Output()\n",
128
- "\n",
129
- " use_split = widgets.Checkbox(\n",
130
- " value=False,\n",
131
- " description=\"Split map\",\n",
132
- " style=style,\n",
133
- " layout=widgets.Layout(padding=padding, width=\"100px\"),\n",
134
- " )\n",
135
- "\n",
136
- " use_ndwi = widgets.Checkbox(\n",
137
- " value=False,\n",
138
- " description=\"Compute NDWI\",\n",
139
- " style=style,\n",
140
- " layout=widgets.Layout(padding=padding, width=\"160px\"),\n",
141
- " )\n",
142
- "\n",
143
- " ndwi_threhold = widgets.FloatSlider(\n",
144
- " description=\"Threshold\",\n",
145
- " min=-1,\n",
146
- " max=1,\n",
147
- " value=0,\n",
148
- " step=0.05,\n",
149
- " readout=True,\n",
150
- " style=style,\n",
151
- " layout=widgets.Layout(padding=padding, width=\"230px\"),\n",
152
- " )\n",
153
- "\n",
154
- " options = widgets.HBox(\n",
155
- " [\n",
156
- " use_split,\n",
157
- " use_ndwi,\n",
158
- " ndwi_threhold,\n",
159
- " ]\n",
160
- " )\n",
161
- "\n",
162
- " widget.children = [pre_widget, post_widget, options, buttons, output]\n",
163
- " self.add_widget(widget, position=position, **kwargs)\n",
164
- "\n",
165
- " def apply_btn_click(b):\n",
166
- "\n",
167
- " marker_layer = self.find_layer(\"Search location\")\n",
168
- " if marker_layer is not None:\n",
169
- " self.remove(marker_layer)\n",
170
- " self.clean_up()\n",
171
- "\n",
172
- " if self.user_roi is None:\n",
173
- " output.clear_output()\n",
174
- " output.append_stdout(\"Please draw a ROI first.\")\n",
175
- " elif (\n",
176
- " pre_start_date.value is None\n",
177
- " or pre_end_date.value is None\n",
178
- " or post_start_date.value is None\n",
179
- " or post_end_date.value is None\n",
180
- " ):\n",
181
- " output.clear_output()\n",
182
- " output.append_stdout(\"Please select start and end dates.\")\n",
183
- "\n",
184
- " elif self.user_roi is not None:\n",
185
- " output.clear_output()\n",
186
- " output.append_stdout(\"Computing... Please wait.\")\n",
187
- " roi = ee.FeatureCollection(self.user_roi)\n",
188
- " vis_params = {\"bands\": [\"B6\", \"B5\", \"B4\"], \"min\": 0, \"max\": 0.4}\n",
189
- " if pre_start_date.value.strftime(\"%Y-%m-%d\") < \"2013-04-11\":\n",
190
- " pre_col = geemap.landsat_timeseries(\n",
191
- " roi,\n",
192
- " start_year=pre_start_date.value.year,\n",
193
- " end_year=pre_end_date.value.year,\n",
194
- " ).select([\"SWIR1\", \"NIR\", \"Red\", \"Green\"], [\"B6\", \"B5\", \"B4\", \"B3\"])\n",
195
- " else:\n",
196
- " pre_col = (\n",
197
- " ee.ImageCollection(\"NASA/HLS/HLSL30/v002\")\n",
198
- " .filterBounds(roi)\n",
199
- " .filterDate(\n",
200
- " pre_start_date.value.strftime(\"%Y-%m-%d\"),\n",
201
- " pre_end_date.value.strftime(\"%Y-%m-%d\"),\n",
202
- " )\n",
203
- " .filter(ee.Filter.lt(\"CLOUD_COVERAGE\", pre_cloud_cover.value))\n",
204
- " )\n",
205
- "\n",
206
- " if post_start_date.value.strftime(\"%Y-%m-%d\") < \"2013-04-11\":\n",
207
- " post_col = geemap.landsat_timeseries(\n",
208
- " roi,\n",
209
- " start_year=post_start_date.value.year,\n",
210
- " end_year=post_end_date.value.year,\n",
211
- " ).select([\"SWIR1\", \"NIR\", \"Red\", \"Green\"], [\"B6\", \"B5\", \"B4\", \"B3\"])\n",
212
- " else:\n",
213
- " post_col = (\n",
214
- " ee.ImageCollection(\"NASA/HLS/HLSL30/v002\")\n",
215
- " .filterBounds(roi)\n",
216
- " .filterDate(\n",
217
- " post_start_date.value.strftime(\"%Y-%m-%d\"),\n",
218
- " post_end_date.value.strftime(\"%Y-%m-%d\"),\n",
219
- " )\n",
220
- " .filter(ee.Filter.lt(\"CLOUD_COVERAGE\", post_cloud_cover.value))\n",
221
- " )\n",
222
- "\n",
223
- " pre_img = pre_col.median().clip(roi)\n",
224
- " post_img = post_col.median().clip(roi)\n",
225
- "\n",
226
- " if use_split.value:\n",
227
- " left_layer = geemap.ee_tile_layer(\n",
228
- " pre_img, vis_params, \"Pre-event Image\"\n",
229
- " )\n",
230
- " right_layer = geemap.ee_tile_layer(\n",
231
- " post_img, vis_params, \"Post-event Image\"\n",
232
- " )\n",
233
- " self.split_map(\n",
234
- " left_layer,\n",
235
- " right_layer,\n",
236
- " add_close_button=True,\n",
237
- " left_label=\"Pre-event\",\n",
238
- " right_label=\"Post-event\",\n",
239
- " )\n",
240
- " else:\n",
241
- " pre_img = pre_col.median().clip(roi)\n",
242
- " post_img = post_col.median().clip(roi)\n",
243
- " self.add_layer(pre_img, vis_params, \"Pre-event Image\")\n",
244
- " self.add_layer(post_img, vis_params, \"Post-event Image\")\n",
245
- "\n",
246
- " if use_ndwi.value and (not use_split.value):\n",
247
- " pre_ndwi = pre_img.normalizedDifference([\"B3\", \"B6\"]).rename(\"NDWI\")\n",
248
- " post_ndwi = post_img.normalizedDifference([\"B3\", \"B6\"]).rename(\n",
249
- " \"NDWI\"\n",
250
- " )\n",
251
- " ndwi_vis = {\"min\": -1, \"max\": 1, \"palette\": \"ndwi\"}\n",
252
- " self.add_layer(pre_ndwi, ndwi_vis, \"Pre-event NDWI\", False)\n",
253
- " self.add_layer(post_ndwi, ndwi_vis, \"Post-event NDWI\", False)\n",
254
- "\n",
255
- " pre_water = pre_ndwi.gt(ndwi_threhold.value)\n",
256
- " post_water = post_ndwi.gt(ndwi_threhold.value)\n",
257
- " self.add_layer(\n",
258
- " pre_water.selfMask(), {\"palette\": \"blue\"}, \"Pre-event Water\"\n",
259
- " )\n",
260
- " self.add_layer(\n",
261
- " post_water.selfMask(), {\"palette\": \"red\"}, \"Post-event Water\"\n",
262
- " )\n",
263
- " new_water = post_water.subtract(pre_water).gt(0)\n",
264
- " disappear_water = pre_water.subtract(post_water).gt(0)\n",
265
- " self.add_layer(\n",
266
- " disappear_water.selfMask(),\n",
267
- " {\"palette\": \"brown\"},\n",
268
- " \"Disappeared Water\",\n",
269
- " )\n",
270
- " self.add_layer(\n",
271
- " new_water.selfMask(), {\"palette\": \"cyan\"}, \"New Water\"\n",
272
- " )\n",
273
- "\n",
274
- " with output:\n",
275
- " output.clear_output()\n",
276
- "\n",
277
- " output.clear_output()\n",
278
- "\n",
279
- " apply_btn.on_click(apply_btn_click)\n",
280
- "\n",
281
- " def reset_btn_click(b):\n",
282
- " self.clean_up()\n",
283
- " self._draw_control.clear()\n",
284
- " draw_layer = self.find_layer(\"Drawn Features\")\n",
285
- " if draw_layer is not None:\n",
286
- " self.remove(draw_layer)\n",
287
- " output.clear_output()\n",
288
- "\n",
289
- " reset_btn.on_click(reset_btn_click)\n",
290
- "\n",
291
- "\n",
292
- "@solara.component\n",
293
- "def Page():\n",
294
- " with solara.Column(style={\"min-width\": \"500px\"}):\n",
295
- " Map.element(\n",
296
- " center=[20, -0],\n",
297
- " zoom=2,\n",
298
- " height=\"750px\",\n",
299
- " zoom_ctrl=False,\n",
300
- " measure_ctrl=False,\n",
301
- " )"
302
- ]
303
- },
304
- {
305
- "cell_type": "code",
306
- "execution_count": null,
307
- "metadata": {},
308
- "outputs": [],
309
- "source": [
310
- "Page()"
311
- ]
312
- }
313
- ],
314
- "metadata": {
315
- "kernelspec": {
316
- "display_name": "geo",
317
- "language": "python",
318
- "name": "python3"
319
- },
320
- "language_info": {
321
- "codemirror_mode": {
322
- "name": "ipython",
323
- "version": 3
324
- },
325
- "file_extension": ".py",
326
- "mimetype": "text/x-python",
327
- "name": "python",
328
- "nbconvert_exporter": "python",
329
- "pygments_lexer": "ipython3",
330
- "version": "3.11.8"
331
- }
332
- },
333
- "nbformat": 4,
334
- "nbformat_minor": 2
335
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/notebooks/09_teste.ipynb DELETED
@@ -1,352 +0,0 @@
1
- {
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 11,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/html": [
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- "\n",
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- " <style>\n",
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- " .geemap-dark {\n",
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- " --jp-widgets-color: white;\n",
15
- " --jp-widgets-label-color: white;\n",
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- " --jp-ui-font-color1: white;\n",
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- " --jp-layout-color2: #454545;\n",
18
- " background-color: #383838;\n",
19
- " }\n",
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- "\n",
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- " .geemap-dark .jupyter-button {\n",
22
- " --jp-layout-color3: #383838;\n",
23
- " }\n",
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- "\n",
25
- " .geemap-colab {\n",
26
- " background-color: var(--colab-primary-surface-color, white);\n",
27
- " }\n",
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- "\n",
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- " .geemap-colab .jupyter-button {\n",
30
- " --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
31
- " }\n",
32
- " </style>\n",
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- " "
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- ],
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- "text/plain": [
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- "<IPython.core.display.HTML object>"
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- ]
38
- },
39
- "metadata": {},
40
- "output_type": "display_data"
41
- }
42
- ],
43
- "source": [
44
- "import geemap\n",
45
- "import ee\n",
46
- "import plotly.express as px"
47
- ]
48
- },
49
- {
50
- "cell_type": "code",
51
- "execution_count": 3,
52
- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/html": [
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- "\n",
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- " <style>\n",
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- " .geemap-dark {\n",
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- " --jp-widgets-color: white;\n",
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- " --jp-widgets-label-color: white;\n",
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- " --jp-ui-font-color1: white;\n",
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- " --jp-layout-color2: #454545;\n",
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- " background-color: #383838;\n",
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- " }\n",
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- "\n",
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- " .geemap-dark .jupyter-button {\n",
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- " --jp-layout-color3: #383838;\n",
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- " }\n",
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- "\n",
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- " .geemap-colab {\n",
72
- " background-color: var(--colab-primary-surface-color, white);\n",
73
- " }\n",
74
- "\n",
75
- " .geemap-colab .jupyter-button {\n",
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- " --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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- " }\n",
78
- " </style>\n",
79
- " "
80
- ],
81
- "text/plain": [
82
- "<IPython.core.display.HTML object>"
83
- ]
84
- },
85
- "metadata": {},
86
- "output_type": "display_data"
87
- }
88
- ],
89
- "source": [
90
- "geemap.ee_initialize()"
91
- ]
92
- },
93
- {
94
- "cell_type": "code",
95
- "execution_count": 6,
96
- "metadata": {},
97
- "outputs": [
98
- {
99
- "data": {
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- "text/html": [
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- "\n",
102
- " <style>\n",
103
- " .geemap-dark {\n",
104
- " --jp-widgets-color: white;\n",
105
- " --jp-widgets-label-color: white;\n",
106
- " --jp-ui-font-color1: white;\n",
107
- " --jp-layout-color2: #454545;\n",
108
- " background-color: #383838;\n",
109
- " }\n",
110
- "\n",
111
- " .geemap-dark .jupyter-button {\n",
112
- " --jp-layout-color3: #383838;\n",
113
- " }\n",
114
- "\n",
115
- " .geemap-colab {\n",
116
- " background-color: var(--colab-primary-surface-color, white);\n",
117
- " }\n",
118
- "\n",
119
- " .geemap-colab .jupyter-button {\n",
120
- " --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
121
- " }\n",
122
- " </style>\n",
123
- " "
124
- ],
125
- "text/plain": [
126
- "<IPython.core.display.HTML object>"
127
- ]
128
- },
129
- "metadata": {},
130
- "output_type": "display_data"
131
- }
132
- ],
133
- "source": [
134
- "esa = ee.ImageCollection('ESA/WorldCover/v200').first()\n",
135
- "\n",
136
- "ucs = ee.FeatureCollection(\"projects/ee-curso-gee-rhamon/assets/ucs_estaduais_rj\")\n",
137
- "\n",
138
- " \n",
139
- "esa = esa.clipToCollection(ucs)"
140
- ]
141
- },
142
- {
143
- "cell_type": "code",
144
- "execution_count": 7,
145
- "metadata": {},
146
- "outputs": [
147
- {
148
- "data": {
149
- "text/html": [
150
- "\n",
151
- " <style>\n",
152
- " .geemap-dark {\n",
153
- " --jp-widgets-color: white;\n",
154
- " --jp-widgets-label-color: white;\n",
155
- " --jp-ui-font-color1: white;\n",
156
- " --jp-layout-color2: #454545;\n",
157
- " background-color: #383838;\n",
158
- " }\n",
159
- "\n",
160
- " .geemap-dark .jupyter-button {\n",
161
- " --jp-layout-color3: #383838;\n",
162
- " }\n",
163
- "\n",
164
- " .geemap-colab {\n",
165
- " background-color: var(--colab-primary-surface-color, white);\n",
166
- " }\n",
167
- "\n",
168
- " .geemap-colab .jupyter-button {\n",
169
- " --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
170
- " }\n",
171
- " </style>\n",
172
- " "
173
- ],
174
- "text/plain": [
175
- "<IPython.core.display.HTML object>"
176
- ]
177
- },
178
- "metadata": {},
179
- "output_type": "display_data"
180
- },
181
- {
182
- "name": "stdout",
183
- "output_type": "stream",
184
- "text": [
185
- "Computing ... \n",
186
- "Generating URL ...\n",
187
- "Downloading data from https://earthengine.googleapis.com/v1/projects/346107197273/tables/60f4e2523a74ad9da945ec21ae745a37-75ffe80c0d13be48ca8789157bb6c245:getFeatures\n",
188
- "Please wait ...\n",
189
- "Data downloaded to c:\\Users\\Rhamon\\Desktop\\Solara\\solara-geemap\\notebooks\\esa_stats_ucs_rj.csv\n"
190
- ]
191
- }
192
- ],
193
- "source": [
194
- "esa_stats = 'esa_stats_ucs_rj.csv'\n",
195
- "\n",
196
- "geemap.zonal_stats_by_group(\n",
197
- " esa,\n",
198
- " ucs,\n",
199
- " esa_stats,\n",
200
- " statistics_type='PERCENTAGE',\n",
201
- " denominator=1e6,\n",
202
- " decimal_places=2,\n",
203
- ")"
204
- ]
205
- },
206
- {
207
- "cell_type": "code",
208
- "execution_count": 9,
209
- "metadata": {},
210
- "outputs": [
211
- {
212
- "name": "stderr",
213
- "output_type": "stream",
214
- "text": [
215
- "<>:1: SyntaxWarning: invalid escape sequence '\\e'\n",
216
- "<>:1: SyntaxWarning: invalid escape sequence '\\e'\n"
217
- ]
218
- },
219
- {
220
- "data": {
221
- "text/html": [
222
- "\n",
223
- " <style>\n",
224
- " .geemap-dark {\n",
225
- " --jp-widgets-color: white;\n",
226
- " --jp-widgets-label-color: white;\n",
227
- " --jp-ui-font-color1: white;\n",
228
- " --jp-layout-color2: #454545;\n",
229
- " background-color: #383838;\n",
230
- " }\n",
231
- "\n",
232
- " .geemap-dark .jupyter-button {\n",
233
- " --jp-layout-color3: #383838;\n",
234
- " }\n",
235
- "\n",
236
- " .geemap-colab {\n",
237
- " background-color: var(--colab-primary-surface-color, white);\n",
238
- " }\n",
239
- "\n",
240
- " .geemap-colab .jupyter-button {\n",
241
- " --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
242
- " }\n",
243
- " </style>\n",
244
- " "
245
- ],
246
- "text/plain": [
247
- "<IPython.core.display.HTML object>"
248
- ]
249
- },
250
- "metadata": {},
251
- "output_type": "display_data"
252
- },
253
- {
254
- "name": "stderr",
255
- "output_type": "stream",
256
- "text": [
257
- "C:\\Users\\Rhamon\\AppData\\Local\\Temp\\ipykernel_21848\\2857369937.py:1: SyntaxWarning: invalid escape sequence '\\e'\n",
258
- " df = 'CSVs\\esa_stats_ucs_rj.csv'\n"
259
- ]
260
- }
261
- ],
262
- "source": [
263
- "df = 'CSVs\\esa_stats_ucs_rj.csv'"
264
- ]
265
- },
266
- {
267
- "cell_type": "code",
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310
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
311
- "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
312
- "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_21848\\385931256.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Class_10'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nome\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Cobertura Florestal UCs Estaduais RJ'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
313
- "\u001b[1;32mc:\\Users\\Rhamon\\miniconda3\\envs\\geo\\Lib\\site-packages\\plotly\\express\\_chart_types.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(data_frame, names, values, color, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, color_discrete_sequence, color_discrete_map, hover_name, hover_data, custom_data, category_orders, labels, title, template, width, height, opacity, hole)\u001b[0m\n\u001b[0;32m 1478\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcolor_discrete_sequence\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1479\u001b[0m \u001b[0mlayout_patch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"piecolorway\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcolor_discrete_sequence\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1480\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1481\u001b[0m \u001b[0mlayout_patch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1482\u001b[1;33m return make_figure(\n\u001b[0m\u001b[0;32m 1483\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlocals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1484\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPie\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1485\u001b[0m \u001b[0mtrace_patch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshowlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhole\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhole\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
314
- "\u001b[1;32mc:\\Users\\Rhamon\\miniconda3\\envs\\geo\\Lib\\site-packages\\plotly\\express\\_core.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(args, constructor, trace_patch, layout_patch)\u001b[0m\n\u001b[0;32m 2086\u001b[0m \u001b[0mtrace_patch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrace_patch\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2087\u001b[0m \u001b[0mlayout_patch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlayout_patch\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2088\u001b[0m \u001b[0mapply_default_cascade\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2089\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2090\u001b[1;33m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuild_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2091\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mconstructor\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTreemap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSunburst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIcicle\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"path\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2092\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocess_dataframe_hierarchy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2093\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mconstructor\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPie\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
315
- "\u001b[1;32mc:\\Users\\Rhamon\\miniconda3\\envs\\geo\\Lib\\site-packages\\plotly\\express\\_core.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(args, constructor)\u001b[0m\n\u001b[0;32m 1344\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"to_pandas_df\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1345\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_pandas_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1346\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1347\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1349\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1350\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mdf_provided\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1351\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"data_frame\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
316
- "\u001b[1;32mc:\\Users\\Rhamon\\miniconda3\\envs\\geo\\Lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 882\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 883\u001b[0m \u001b[1;31m# For data is scalar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 884\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 885\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 886\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"DataFrame constructor not properly called!\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 887\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 888\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 889\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
317
- "\u001b[1;31mValueError\u001b[0m: DataFrame constructor not properly called!"
318
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319
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- 0.06,0.86,0.1,0,38.93,0,57.04,111.28,0,14.29,19.0,PROTECAO INTEGRAL,REVIS,ESTADUAL,REFUGIO DA VIDA SILVESTRE ESTADUAL DO MEDIO PARAIBA,11137.1971838,2016,6.13493591535,N�O,0.00976335248,N�O,21,REVIS MEDIO PARAIBA,2,1:25.000,00000000000000000001
4
- 0.08,0.03,0,0.45,0.98,0,94.76,99.67000000000002,0.01,3.36,24.0,PROTECAO INTEGRAL,RESEC,ESTADUAL,RESERVA ECOLOGICA ESTADUAL DA JUATINGA,9967.24995917,1992,0.71060414957,N�O,0.0087980555,N�O,4,RESEC DA JUATINGA,19,Sem Info,00000000000000000012
5
- 0.0,0,0,0,2.93,0,41.05,43.98,0,0,16.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DO MENDANHA,4399.10221912,2013,0.51140355655,N�O,0.00386942761,N�O,20,PE DO MENDANHA,28,1:25.000,0000000000000000001b
6
- 1.65,0.15,0.04,0,26.1,0,1.85,82.49000000000001,39.14,13.56,9.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA LAGOA DO ACU,8249.12246907,2012,0.83549660297,N�O,0.0072137959,N�O,19,PE DA LAGOA DO ACU,29,Sem info,0000000000000000001c
7
- 0,0,0,0,10.51,0,44.24,54.75,0,0,2.0,PROTECAO INTEGRAL,MONA,ESTADUAL,MONUMENTO NATURAL DA SERRA DA BELEZA,5476.83546917,2017,1.13386248167,N�O,0.00480016717,N�O,2,MONA SERRA DA BELEZA,36,1:25.000,00000000000000000023
8
- 0,0.0,0,0,2.61,0,4.14,6.75,0,0,4.0,PROTECAO INTEGRAL,MONA,ESTADUAL,MONUMENTO NATURAL DA SERRA DOS MASCATES,675.057144348,2017,0.18707531907,N�O,5.9145591E-4,N�O,3,MONA SERRA DOS MASCATES,37,1:25.000,00000000000000000024
9
- 0,0.03,0,0,0.73,0,47.31,48.09,0,0.02,18.0,PROTECAO INTEGRAL,REVIS,ESTADUAL,REFUGIO DA VIDA SILVESTRE ESTADUAL DA SERRA DA ESTRELA,4811.73100649,2017,1.19033062973,SIM,0.00422505577,N�O,23,REVIS SERRA DA ESTRELA,1,1:25.000,00000000000000000000
10
- 0,0.02,0,0,1.97,0,0.36,2.69,0.21,0.13,20.0,PROTECAO INTEGRAL,REVIS,ESTADUAL,REFUGIO DA VIDA SILVESTRE ESTADUAL LAGOA DA TURFEIRA,269.493133194,2016,0.15745976676,SIM,2.3640802E-4,N�O,22,REVIS LAGOA DA TURFEIRA,18,1:25.000,00000000000000000011
11
- 0.14,0.0,0,0,0.05,0,32.11,33.11,0.06,0.75,21.0,PROTECAO INTEGRAL,REBIO,ESTADUAL,RESERVA BIOLOGICA ESTADUAL DA PRAIA DO SUL,3311.84134657,2014,0.42874875901,SIM,0.00292054795,N�O,5,REBIO PRAIA DO SUL,20,1:50.000,00000000000000000013
12
- 0.85,0.18,0,18.4,1.82,0,1.88,33.61,4.37,6.11,23.0,PROTECAO INTEGRAL,REBIO,ESTADUAL,RESERVA BIOLOGICA ESTADUAL DE GUARATIBA,3361.18585618,1974,0.57256120811,SIM,0.00296089188,SIM,6,REBIO DE GUARATIBA,21,1:10.000,00000000000000000014
13
- 0,0,0,0,5.46,0.01,32.91,38.379999999999995,0,0,22.0,PROTECAO INTEGRAL,REBIO,ESTADUAL,RESERVA BIOLOGICA ESTADUAL DE ARARAS,3837.86536952,1977,0.46092656765,SIM,0.00336699397,SIM,7,REBIO DE ARARAS,22,1:50.000,00000000000000000015
14
- 0,0.01,0.01,0,21.27,0,629.29,650.5899999999999,0,0.01,17.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DOS TRES PICOS,65080.4867347,2002,9.10823446005,SIM,0.05708718834,SIM,12,PE DOS TRES PICOS,23,1:25.000 ; 1:50.000,00000000000000000016
15
- 0.08,0.09,0,0,1.55,0,30.19,34.31,0.38,2.02,13.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA SERRA DA TIRIRICA,3433.85726444,1991,1.42936381133,SIM,0.00302301907,SIM,13,PE SERRA DA TIRIRICA,24,1:10.000,00000000000000000017
16
- 0,0.02,0.01,0,8.02,0,51.45,59.52,0,0.02,12.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA SERRA DA CONCORDIA,5952.58474185,2016,1.45942906837,SIM,0.00521852499,N�O,11,PE SERRA DA CONCORDIA,25,1:25.000,00000000000000000018
17
- 0,0.0,0.02,0,2.12,0.02,78.25,80.41,0,0,11.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA PEDRA SELADA,8041.49819897,2012,1.66658109283,SIM,0.00704996872,SIM,18,PE DA PEDRA SELADA,26,1:25.000,00000000000000000019
18
- 0.0,0.1,0,0,10.83,0,113.97,124.91999999999999,0,0.02,10.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA PEDRA BRANCA,12494.2417207,1974,1.8845293254,SIM,0.01100042314,SIM,10,PE DA PEDRA BRANCA,27,Sem Info,0000000000000000001a
19
- 0,0.02,0,0,0.02,0,0.51,0.55,0,0,15.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DO GRAJAU,54.7290261485,1978,0.05699572081,SIM,4.817604E-5,SIM,14,PE DO GRAJAU,30,Sem Info,0000000000000000001d
20
- 0,0,0,0,8.76,0,204.47,213.23,0,0,14.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DO DESENGANO,21325.7388368,1970,1.75405439943,SIM,0.01863237605,SIM,16,PE DO DESENGANO,31,Sem info,0000000000000000001e
21
- 0,0,0,0,12.87,0,364.62,380.73,0,3.24,5.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL CUNHAMBEBE,38076.6666531,2008,4.36267236111,SIM,0.03351297978,SIM,8,PE CUNHAMBEBE,32,Sem info,0000000000000000001f
22
- 4.26,1.4,0.03,0,22.11,0.04,44.33,98.27000000000001,14.65,11.45,7.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA COSTA DO SOL,9828.61554842,2011,3.29315109101,SIM,0.0086502762,SIM,17,PE DA COSTA DO SOL,33,1:25.000,00000000000000000020
23
- 0,0.0,0,0,0,0,0.04,0.04,0,0,6.0,PROTECAO INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA CHACRINHA,3.71173380368,1969,0.00787836058,SIM,3.26827E-6,SIM,15,PE DA CHACRINHA,34,Sem Info,00000000000000000021
24
- 0,0.0,0.51,0,10.26,0,16.53,32.58,4.81,0.47,1.0,PROTECAO INTEGRAL,EEE,ESTADUAL,ESTACAO ECOLOGICA ESTADUAL DE GUAXINDIBA,3259.22390746,2002,0.47132646001,SIM,0.00283886233,SIM,1,EEE DE GUAXINDIBA,35,1:50.000,00000000000000000022
25
- 0.15,0.05,0,0,0.63,0,118.68,120.89,0.02,1.36,8.0,PROTE��O INTEGRAL,PARQUE,ESTADUAL,PARQUE ESTADUAL DA ILHA GRANDE,12083.7871718,1971,1.47283767448,SIM,0.01066040264,SIM,8,PE DA ILHA GRANDE,40,1:50.000 - IBGE,00000000000000000027
26
- 0.37,8.45,0.37,0,62.51,0,142.49,221.10999999999999,5.25,1.67,35.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DO ALTO IGUACU,22111.9931221,2013,0.78497409664,N�O SE APLICA,0.01942446261,SIM,36,APA DO ALTO IGUACU,3,1:25.000,00000000000000000002
27
- 0.04,2.96,2.66,1.57,49.86,0,130.79,194.67,6.49,0.3,25.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DA BACIA DO RIO MACACU,19502.1400409,2002,33.798675024,N�O SE APLICA,0.01711874796,N�O,27,APA DA BACIA DO RIO MACACU,4,Sem Info,00000000000000000003
28
- 0,0,0.13,0,11.13,0,17.46,28.720000000000002,0,0.0,26.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DA BACIA DOS FRADES,2871.72642178,1990,0.59921403936,N�O SE APLICA,0.0025174733,N�O,31,APA DOS FRADES,5,1:25.000 ; 1:50.000,00000000000000000004
29
- 0,0.32,0.02,0,25.23,0,324.44,350.01,0,0,29.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE MACAE DE CIMA,35003.0809078,2001,1.03394504309,N�O SE APLICA,0.03069270346,SIM,30,APA DE MACAE DE CIMA,6,1:50.000,00000000000000000005
30
- 0.04,0.05,0,0,14.7,0,64.94,79.75,0,0.02,28.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE GERICINO-MENDANHA,7974.14336908,2005,0.66988583505,N�O SE APLICA,0.00701359206,N�O,25,APA DE GERICINO-MENDANHA,7,Sem Info,00000000000000000006
31
- 0,0.35,0,0.99,10.44,0,241.09,253.09,0.05,0.17,30.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE MANGARATIBA,25312.3537777,1987,2.73219986964,N�O SE APLICA,0.02228427968,SIM,24,APA DE MANGARATIBA,8,1:20.000,00000000000000000007
32
- 1.37,0.36,0,0,2.99,0.01,4.11,9.690000000000001,0.47,0.38,31.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE MARICA,969.239448931,1984,0.30140385146,N�O SE APLICA,8.5341915E-4,SIM,33,APA DE MARICA,9,Sem info,00000000000000000008
33
- 0.62,8.76,0.02,0,27.58,0,23.12,91.25,18.32,12.83,32.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE MASSAMBABA,9124.45193908,2011,0.8822736963,N�O SE APLICA,0.00803179984,SIM,32,APA DE MASSAMBABA,10,1:50.000,00000000000000000009
34
- 0.69,2.28,0,0,4.17,0,18.42,103.52000000000001,1.7,76.26,36.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DO PAU-BRASIL,10353.1623171,2002,0.42686675166,N�O SE APLICA,0.00910866994,SIM,28,APA PAU BRASIL,11,1:50.000,0000000000000000000a
35
- 0.26,6.66,0.28,0,29.88,0,21.28,59.53999999999999,0.5,0.68,27.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DA SERRA DE SAPIATIBA,5955.22943195,1990,0.34376268569,N�O SE APLICA,0.00523829411,SIM,29,APA DA SERRA DE SAPIATIBA,12,1:50.000,0000000000000000000b
36
- 0.14,12.69,1.29,0,264.0,0,456.92,742.91,0.81,7.06,37.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DO RIO GUANDU,74295.2023405,2007,3.89480581005,N�O SE APLICA,0.06525411525,N�O,26,APA DO RIO GUANDU,13,1:50.000,0000000000000000000c
37
- 0,0.0,0,0,0.01,0,0.25,0.29000000000000004,0,0.03,38.0,USO SUSTENTAVEL,FLOR,ESTADUAL,FLORESTA ESTADUAL JOSE ZAGO,29.1364229911,2016,0.02136871955,N�O SE APLICA,2.549498E-5,N�O,37,FLOE JOSE ZAGO,14,1:25.000,0000000000000000000d
38
- 0.03,0.12,0,0,0.56,0,0.96,1.7200000000000002,0.05,0,33.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE SEPETIBA II,171.666213409,2004,0.05801156773,N�O SE APLICA,1.5116278E-4,N�O,34,APA DE SEPETIBA II,15,1:10.000,0000000000000000000e
39
- 0.13,0,0,0,0.02,0,1.16,18.99,0,17.68,39.0,USO SUSTENTAVEL,RDS,ESTADUAL,RESERVA DE DESENVOLVIMENTO SUSTENTAVEL DO AVENTUREIRO,1899.91188332,2014,0.21453857684,N�O SE APLICA,0.00167576192,N�O,39,RDS DO AVENTUREIRO,16,1:50.000,0000000000000000000f
40
- 0.22,0.01,0,0,0.1,0,0.1,39.42,0.04,38.95,40.0,USO SUSTENTAVEL,RESEX,ESTADUAL,RESERVA EXTRATIVISTA MARINHA DE ITAIPU,3942.53457822,2013,0.4358579572,N�O SE APLICA,0.00347203423,N�O,38,RESEX MARINHA DE ITAIPU,17,1:25.000,00000000000000000010
41
- 0.54,6.03,0,3.98,3.28,0,200.16,225.33999999999997,0.32,11.03,34.0,USO SUSTENTAVEL,APA,ESTADUAL,AREA DE PROTECAO AMBIENTAL DE TAMOIOS,22545.1793314,1986,6.34858345439,N�O SE APLICA,0.01987470585,SIM,35,APA DE TAMOIOS,38,Sem info,00000000000000000025
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/00_home.py DELETED
@@ -1,80 +0,0 @@
1
- import solara
2
-
3
-
4
- @solara.component
5
- def Page():
6
- with solara.Column(align="center"):
7
- markdown = """
8
- ## Projetos de Carbono: Arborização, Reflorestamento e Restauração (ARR)
9
-
10
- ### Introdução
11
-
12
- **Uma visão geral dos projetos de carbono focados em Arborização, Reflorestamento e Restauração (ARR), seu impacto global e seu papel nos esforços de sustentabilidade do Brasil.**
13
-
14
- Os projetos de carbono do tipo ARR têm como objetivo aumentar a cobertura florestal e restaurar terras degradadas, contribuindo para a captura de carbono e o combate às mudanças climáticas. Essas iniciativas estão alinhadas a marcos globais como o Acordo de Paris e são essenciais para alcançar metas de emissões líquidas zero.
15
-
16
- - Saiba mais: <https://www.ipcc.ch/reports/>
17
- - Explore metodologias ARR: <https://verra.org/methodologies/>
18
- - Confira projetos ARR no Brasil: <https://www.forestcarbonpartnership.org/>
19
-
20
- ### Impacto Global dos Projetos ARR
21
-
22
- Os projetos ARR ganharam destaque globalmente como uma estratégia econômica para mitigação climática. Esses projetos oferecem múltiplos benefícios:
23
-
24
- - Captura de CO₂ atmosférico por meio do sequestro de carbono em biomassa e solo.
25
- - Apoio à biodiversidade por meio da restauração de ecossistemas nativos.
26
- - Melhoria das condições de vida em comunidades rurais e indígenas.
27
-
28
- De acordo com o [Global Forest Watch](https://www.globalforestwatch.org/), milhares de hectares estão envolvidos em projetos ARR, com contribuições significativas de nações em desenvolvimento em regiões tropicais.
29
-
30
- ![Mapa Global ARR](https://i.imgur.com/hyEMbuq.png)
31
-
32
- ## ARR no Brasil: Um Exemplo de Liderança
33
-
34
- O Brasil ocupa uma posição estratégica em iniciativas ARR devido às suas vastas paisagens florestais, especialmente na Amazônia, Mata Atlântica e Cerrado. Com programas como o Fundo Amazônia e políticas estaduais, o país demonstra seu compromisso com a restauração florestal:
35
-
36
- - **Fundo Amazônia**: Financia projetos que previnem o desmatamento e promovem o desenvolvimento sustentável. Saiba mais: <http://www.amazonfund.gov.br/>
37
- - **Pacto pela Mata Atlântica**: Busca restaurar 15 milhões de hectares até 2050. Detalhes: <https://www.pactomataatlantica.org.br/>
38
-
39
- #### Principais Estatísticas, em estudo do BNDES, envolvendo a possibilidade de Projetos ARR em Terras Públicas no Brasil:
40
- #### (Fonte: [BNDES](https://agenciadenoticias.bndes.gov.br/blogdodesenvolvimento/detalhe/Potencial-de-florestas-publicas-em-projetos-de-pagamento-por-servicos-ambientais/))
41
-
42
-
43
-
44
- - O estudo foi realizado em 273 UCs federais e 1.451 Glebas Arrecadadas, em todos os biomas,e utilizando dados georreferenciados disponíveis na base oficial do MMA e na base do Cadastro Nacional de Florestas Públicas.
45
- Da área total analisada, foram identificados 2.477.992 hectares de área elegível e aplicável para o desenvolvimento de projetos de ARR. Cerca de 79(%) desse total está localizado em Glebas, seguido pelas UCs federais de uso sustentável do tipo Flona e Resex, com
46
- 7(%) e 5(%) das áreas elegíveis e aplicáveis, respectivamente.
47
-
48
- ![Quantitativo de áreas viáveis para projetos de ARR em diferentes categorias de terras públicas](https://i.imgur.com/UzVxUh9.png)
49
-
50
- - Quanto aos biomas que possuem maior número de áreas elegíveis, a Amazônia detém 77,7(%) de todas as áreas públicas elegíveis para projetos de ARR, seguida por Cerrado
51
- e Pantanal. Também vale destacar que os estados com maior número de áreas elegíveis são Pará (45,7%), Rondônia (14,4%) e Mato Grosso (10,7%)
52
-
53
- ![Quantitativo de áreas viáveis para projetos de ARR nas terras públicas por estados e biomas](https://i.imgur.com/qcqM73U.png)
54
-
55
-
56
-
57
- # Projetos de ARR no Brasil avaliados nesta aplicação:
58
-
59
- - 1º: Reforestation Grouped Project at Pratigi Environmental Protection Area
60
- - 2º: Corridors for Life ARR Grouped Project
61
- - 3º: Fazenda Nascente Do Luar Agroforestry Project
62
- - 4º: Fazenda Sao Paulo Agroforestry
63
- - 5º: The ARR Horizonte Carbon Project
64
- - 6º: The Amazon Reforestation Consortium Project
65
- - 7º: Carbon Project in the Emas-Taquari Biodiversity Corridor
66
- - 8º: The ARR Cerrado Carbon Project
67
- - 9º: Multi-Species Reforestation in Mato Grosso, Brazil
68
-
69
- **Fonte**: Karnik, A., Kilbride, J., Goodbody, T., Rachel, R., & Ayrey, E. (2024). A global database of nature-based carbon offset project boundaries [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11459391
70
-
71
-
72
- ### Conclusão
73
-
74
- Os projetos ARR são indispensáveis para alcançar as metas climáticas globais. Como um dos líderes em esforços ARR, o Brasil demonstra o potencial de combinar políticas públicas, tecnologia e engajamento comunitário para enfrentar os desafios climáticos enquanto promove benefícios socioeconômicos.
75
-
76
- ![](https://media4.giphy.com/media/v1.Y2lkPTc5MGI3NjExYmlieWYzdjdpb3ZlaGJocnUxbDk3eGo0eWU2Mmpndm1wanY2N3o3cSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/RCtKcMeeIlIFskmH7C/giphy.webp)
77
-
78
- """
79
-
80
- solara.Markdown(markdown)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/01_ARR 1º.py DELETED
@@ -1,102 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[0]
13
- project_name= df['Project Na'].iloc[0]
14
- project_start = df['Project St'].iloc[0]
15
- project_end = df['Project En'].iloc[0]
16
- project_ID = df['ProjectID'].iloc[0]
17
- registry = df['Registry N'].iloc[0]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_basemap("Esri.WorldImagery")
23
- self.add_ee_data()
24
-
25
- def add_ee_data(self):
26
- # Add Earth Engine dataset
27
-
28
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
29
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
30
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(0)
31
-
32
- ARR_project= ee.Feature(ARR_project_list)
33
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
34
-
35
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
36
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
37
-
38
- #26- Ano de 2023
39
-
40
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
41
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
42
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
43
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
44
-
45
-
46
- #38-Ano de 2023
47
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
48
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
49
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
50
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
51
-
52
- legend_dict = {
53
- '3 - Formação Florestal ': '#1f8d49',
54
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
55
- '15 - Pastagem ': '#edde8e',
56
- '18 - Agricultura/Silvicultura': '#E974ED',
57
- '22 - Área não Vegetada': '#d4271e' ,
58
- "26 - Corpo D'água": '#2532e4',
59
- }
60
-
61
- vis_params = {
62
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
63
- #'min': 3,
64
- #'max': 26,
65
- }
66
-
67
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
68
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
69
-
70
-
71
-
72
- # Add Earth Engine layers to Map
73
-
74
- self.centerObject(ARR_project, zoom=14)
75
-
76
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
77
-
78
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
79
-
80
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict, add_header=False)
81
-
82
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
83
-
84
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
85
-
86
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
87
-
88
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
89
-
90
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
91
-
92
-
93
-
94
-
95
-
96
-
97
- @solara.component
98
- def Page():
99
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
100
- Map.element(
101
- height="600px",
102
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/02_ARR 2º.py DELETED
@@ -1,100 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[1]
13
- project_name= df['Project Na'].iloc[1]
14
- project_start = df['Project St'].iloc[1]
15
- project_end = df['Project En'].iloc[1]
16
- project_ID = df['ProjectID'].iloc[1]
17
- registry = df['Registry N'].iloc[1]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_basemap("Esri.WorldImagery")
23
- self.add_ee_data()
24
-
25
- def add_ee_data(self):
26
- # Add Earth Engine dataset
27
-
28
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
29
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
30
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(1)
31
-
32
- ARR_project= ee.Feature(ARR_project_list)
33
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
34
-
35
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
36
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
37
-
38
- #26- Ano de 2023
39
-
40
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
41
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
42
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
43
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
44
-
45
-
46
- #38-Ano de 2023
47
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
48
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
49
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
50
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
51
-
52
- legend_dict = {
53
- '3 - Formação Florestal ': '#1f8d49',
54
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
55
- '15 - Pastagem ': '#edde8e',
56
- '18 - Agricultura/Silvicultura': '#E974ED',
57
- '22 - Área não Vegetada': '#d4271e' ,
58
- "26 - Corpo D'água": '#2532e4',
59
- }
60
-
61
- vis_params = {
62
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
63
- #'min': 3,
64
- #'max': 26,
65
- }
66
-
67
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
68
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
69
-
70
-
71
-
72
- # Add Earth Engine layers to Map
73
-
74
- self.centerObject(ARR_project, zoom=11)
75
-
76
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
77
-
78
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
79
-
80
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict)
81
-
82
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
83
-
84
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
85
-
86
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
87
-
88
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
89
-
90
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
91
-
92
-
93
-
94
-
95
- @solara.component
96
- def Page():
97
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
98
- Map.element(
99
- height="600px",
100
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/03_ARR 3°.py DELETED
@@ -1,100 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[2]
13
- project_name= df['Project Na'].iloc[2]
14
- project_start = df['Project St'].iloc[2]
15
- project_end = df['Project En'].iloc[2]
16
- project_ID = df['ProjectID'].iloc[2]
17
- registry = df['Registry N'].iloc[2]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_basemap("Esri.WorldImagery")
23
- self.add_ee_data()
24
-
25
- def add_ee_data(self):
26
- # Add Earth Engine dataset
27
-
28
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
29
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
30
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(2)
31
-
32
- ARR_project= ee.Feature(ARR_project_list)
33
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
34
-
35
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
36
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
37
-
38
- #26- Ano de 2023
39
-
40
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
41
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
42
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
43
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
44
-
45
-
46
- #38-Ano de 2023
47
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
48
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
49
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
50
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
51
-
52
- legend_dict = {
53
- '3 - Formação Florestal ': '#1f8d49',
54
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
55
- '15 - Pastagem ': '#edde8e',
56
- '18 - Agricultura/Silvicultura': '#E974ED',
57
- '22 - Área não Vegetada': '#d4271e' ,
58
- "26 - Corpo D'água": '#2532e4',
59
- }
60
-
61
- vis_params = {
62
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
63
- #'min': 3,
64
- #'max': 26,
65
- }
66
-
67
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
68
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
69
-
70
-
71
-
72
- # Add Earth Engine layers to Map
73
-
74
- self.centerObject(ARR_project, zoom=13)
75
-
76
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
77
-
78
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
79
-
80
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict)
81
-
82
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
83
-
84
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
85
-
86
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
87
-
88
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
89
-
90
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
91
-
92
-
93
-
94
-
95
- @solara.component
96
- def Page():
97
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
98
- Map.element(
99
- height="600px",
100
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/04_ARR 4º.py DELETED
@@ -1,102 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[3]
13
- project_name= df['Project Na'].iloc[3]
14
- project_start = df['Project St'].iloc[3]
15
- project_end = df['Project En'].iloc[3]
16
- project_ID = df['ProjectID'].iloc[3]
17
- registry = df['Registry N'].iloc[3]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_basemap("Esri.WorldImagery")
23
- self.add_ee_data()
24
-
25
- def add_ee_data(self):
26
- # Add Earth Engine dataset
27
-
28
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
29
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
30
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(3)
31
-
32
- ARR_project= ee.Feature(ARR_project_list)
33
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
34
-
35
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
36
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
37
-
38
- #26- Ano de 2023
39
-
40
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
41
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
42
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
43
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
44
-
45
-
46
- #38-Ano de 2023
47
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
48
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
49
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
50
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
51
-
52
- legend_dict = {
53
- '3 - Formação Florestal ': '#1f8d49',
54
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
55
- '15 - Pastagem ': '#edde8e',
56
- '18 - Agricultura/Silvicultura': '#E974ED',
57
- '22 - Área não Vegetada': '#d4271e' ,
58
- "26 - Corpo D'água": '#2532e4',
59
- }
60
-
61
- vis_params = {
62
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
63
- #'min': 3,
64
- #'max': 26,
65
- }
66
-
67
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
68
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
69
-
70
-
71
-
72
- # Add Earth Engine layers to Map
73
-
74
- self.centerObject(ARR_project, zoom=13)
75
-
76
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
77
-
78
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
79
-
80
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict)
81
-
82
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
83
-
84
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
85
-
86
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
87
-
88
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
89
-
90
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
91
-
92
-
93
-
94
-
95
-
96
-
97
- @solara.component
98
- def Page():
99
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
100
- Map.element(
101
- height="600px",
102
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/05_ARR 5º.py DELETED
@@ -1,103 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[4]
13
- project_name= df['Project Na'].iloc[4]
14
- project_start = df['Project St'].iloc[4]
15
- project_end = df['Project En'].iloc[4]
16
- project_ID = df['ProjectID'].iloc[4]
17
- registry = df['Registry N'].iloc[4]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_basemap("Esri.WorldImagery")
23
- self.add_ee_data()
24
-
25
- def add_ee_data(self):
26
- # Add Earth Engine dataset
27
-
28
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
29
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
30
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(4)
31
-
32
- ARR_project= ee.Feature(ARR_project_list)
33
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
34
-
35
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
36
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
37
-
38
- #26- Ano de 2023
39
-
40
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
41
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
42
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
43
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
44
-
45
-
46
- #38-Ano de 2023
47
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
48
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
49
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
50
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
51
-
52
- legend_dict = {
53
- '3 - Formação Florestal ': '#1f8d49',
54
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
55
- '15 - Pastagem ': '#edde8e',
56
- '18 - Agricultura/Silvicultura': '#E974ED',
57
- '22 - Área não Vegetada': '#d4271e' ,
58
- "26 - Corpo D'água": '#2532e4',
59
- }
60
-
61
- vis_params = {
62
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
63
- #'min': 3,
64
- #'max': 26,
65
- }
66
-
67
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
68
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
69
-
70
-
71
-
72
- # Add Earth Engine layers to Map
73
-
74
- self.centerObject(ARR_project, zoom=9)
75
-
76
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
77
-
78
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
79
-
80
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict)
81
-
82
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
83
-
84
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
85
-
86
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
87
-
88
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
89
-
90
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
91
-
92
-
93
-
94
-
95
-
96
-
97
-
98
- @solara.component
99
- def Page():
100
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
101
- Map.element(
102
- height="600px",
103
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/06_ARR 6º.py DELETED
@@ -1,103 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[5]
13
- project_name= df['Project Na'].iloc[5]
14
- project_start = df['Project St'].iloc[5]
15
- project_end = df['Project En'].iloc[5]
16
- project_ID = df['ProjectID'].iloc[5]
17
- registry = df['Registry N'].iloc[5]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_basemap("Esri.WorldImagery")
23
- self.add_ee_data()
24
-
25
- def add_ee_data(self):
26
- # Add Earth Engine dataset
27
-
28
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
29
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
30
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(5)
31
-
32
- ARR_project= ee.Feature(ARR_project_list)
33
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
34
-
35
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
36
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
37
-
38
- #26- Ano de 2023
39
-
40
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
41
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
42
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
43
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
44
-
45
-
46
- #38-Ano de 2023
47
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
48
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
49
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
50
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
51
-
52
- legend_dict = {
53
- '3 - Formação Florestal ': '#1f8d49',
54
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
55
- '15 - Pastagem ': '#edde8e',
56
- '18 - Agricultura/Silvicultura': '#E974ED',
57
- '22 - Área não Vegetada': '#d4271e' ,
58
- "26 - Corpo D'água": '#2532e4',
59
- }
60
-
61
- vis_params = {
62
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
63
- #'min': 3,
64
- #'max': 26,
65
- }
66
-
67
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
68
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
69
-
70
-
71
-
72
- # Add Earth Engine layers to Map
73
-
74
- self.centerObject(ARR_project, zoom=11)
75
-
76
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
77
-
78
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
79
-
80
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict)
81
-
82
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
83
-
84
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
85
-
86
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
87
-
88
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
89
-
90
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
91
-
92
-
93
-
94
-
95
-
96
-
97
-
98
- @solara.component
99
- def Page():
100
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
101
- Map.element(
102
- height="600px",
103
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/07_ARR 7º.py DELETED
@@ -1,103 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[6]
13
- project_name= df['Project Na'].iloc[6]
14
- project_start = df['Project St'].iloc[6]
15
- project_end = df['Project En'].iloc[6]
16
- project_ID = df['ProjectID'].iloc[6]
17
- registry = df['Registry N'].iloc[6]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_ee_data()
23
- self.add_basemap("Esri.WorldImagery")
24
- self.add_ee_data()
25
-
26
- def add_ee_data(self):
27
- # Add Earth Engine dataset
28
-
29
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
30
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
31
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(6)
32
-
33
- ARR_project= ee.Feature(ARR_project_list)
34
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
35
-
36
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
37
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
38
-
39
- #26- Ano de 2023
40
-
41
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
42
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
43
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
44
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
45
-
46
-
47
- #38-Ano de 2023
48
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
49
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
50
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
51
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
52
-
53
- legend_dict = {
54
- '3 - Formação Florestal ': '#1f8d49',
55
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
56
- '15 - Pastagem ': '#edde8e',
57
- '18 - Agricultura/Silvicultura': '#E974ED',
58
- '22 - Área não Vegetada': '#d4271e' ,
59
- "26 - Corpo D'água": '#2532e4',
60
- }
61
-
62
- vis_params = {
63
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
64
- #'min': 3,
65
- #'max': 26,
66
- }
67
-
68
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
69
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
70
-
71
-
72
-
73
- # Add Earth Engine layers to Map
74
-
75
- self.centerObject(ARR_project, zoom=13)
76
-
77
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
78
-
79
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
80
-
81
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict)
82
-
83
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
84
-
85
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
86
-
87
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
88
-
89
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
90
-
91
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
92
-
93
-
94
-
95
-
96
-
97
-
98
- @solara.component
99
- def Page():
100
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
101
- Map.element(
102
- height="600px",
103
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/08_ARR 8º.py DELETED
@@ -1,102 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[7]
13
- project_name= df['Project Na'].iloc[7]
14
- project_start = df['Project St'].iloc[7]
15
- project_end = df['Project En'].iloc[7]
16
- project_ID = df['ProjectID'].iloc[7]
17
- registry = df['Registry N'].iloc[7]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_basemap("Esri.WorldImagery")
23
- self.add_ee_data()
24
-
25
- def add_ee_data(self):
26
- # Add Earth Engine dataset
27
-
28
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
29
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
30
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(7)
31
-
32
- ARR_project= ee.Feature(ARR_project_list)
33
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
34
-
35
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
36
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
37
-
38
- #26- Ano de 2023
39
-
40
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
41
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
42
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
43
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
44
-
45
-
46
- #38-Ano de 2023
47
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
48
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
49
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
50
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
51
-
52
- legend_dict = {
53
- '3 - Formação Florestal ': '#1f8d49',
54
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
55
- '15 - Pastagem ': '#edde8e',
56
- '18 - Agricultura/Silvicultura': '#E974ED',
57
- '22 - Área não Vegetada': '#d4271e' ,
58
- "26 - Corpo D'água": '#2532e4',
59
- }
60
-
61
- vis_params = {
62
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
63
- #'min': 3,
64
- #'max': 26,
65
- }
66
-
67
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
68
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
69
-
70
-
71
-
72
- # Add Earth Engine layers to Map
73
-
74
- self.centerObject(ARR_project, zoom=10)
75
-
76
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
77
-
78
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
79
-
80
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict)
81
-
82
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
83
-
84
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
85
-
86
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
87
-
88
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
89
-
90
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
91
-
92
-
93
-
94
-
95
-
96
-
97
- @solara.component
98
- def Page():
99
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
100
- Map.element(
101
- height="600px",
102
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/pages/09_ARR 9º.py DELETED
@@ -1,103 +0,0 @@
1
- import ee
2
- import geemap
3
- import plotly.express as px
4
- import pandas as pd
5
- import solara
6
- import dataclasses
7
- from typing import Callable, cast
8
-
9
-
10
- df= pd.read_csv('CSVs\ARR_projects.csv')
11
-
12
- developer= df['Project De'].iloc[8]
13
- project_name= df['Project Na'].iloc[8]
14
- project_start = df['Project St'].iloc[8]
15
- project_end = df['Project En'].iloc[8]
16
- project_ID = df['ProjectID'].iloc[8]
17
- registry = df['Registry N'].iloc[8]
18
-
19
- class Map(geemap.Map):
20
- def __init__(self, **kwargs):
21
- super().__init__(**kwargs)
22
- self.add_basemap("Esri.WorldImagery")
23
- self.add_ee_data()
24
-
25
- def add_ee_data(self):
26
- # Add Earth Engine dataset
27
-
28
- carbonoffsetscol = ee.FeatureCollection('projects/sat-io/open-datasets/CARBON-OFFSET-PROJECTS-GLOBAL').filter(ee.Filter.eq('Country', 'Brazil'))
29
- ARR_projects= carbonoffsetscol.filter(ee.Filter.eq('Project Ty', 'ARR'))
30
- ARR_project_list= ARR_projects.toList(ARR_projects.size()).get(8)
31
-
32
- ARR_project= ee.Feature(ARR_project_list)
33
- ARR_project_df= geemap.ee_to_gdf(ARR_project)
34
-
35
- fromList = [1,3,4,5,6,49,10,11,12,32,29,50,13,15,18,19,39,20,40,62,41,36,46,47,35,48,21,14,9,26,33,31,22,23,24,30,25,27]
36
- toList = [3,3,3,3,3,3,10,10,10,10,10,10,10,15,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,26,26,26,22,22,22,22,22,22]
37
-
38
- #26- Ano de 2023
39
-
40
- land_use_2011=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(26)
41
- land_use_2011= land_use_2011.clipToCollection(ARR_projects)
42
- land_use_2011_remap= land_use_2011.remap(fromList, toList, 0)
43
- land_use_2011_remap= land_use_2011_remap.clip(ARR_project)
44
-
45
-
46
- #38-Ano de 2023
47
- land_use_2023=ee.Image('projects/mapbiomas-public/assets/brazil/lulc/collection9/mapbiomas_collection90_integration_v1').select(38)
48
- land_use_2023= land_use_2023.clipToCollection(ARR_projects)
49
- land_use_2023_remap= land_use_2023.remap(fromList, toList, 0)
50
- land_use_2023_remap= land_use_2023_remap.clip(ARR_project)
51
-
52
- legend_dict = {
53
- '3 - Formação Florestal ': '#1f8d49',
54
- '10 - Vegetação Herbácea e Arbustiva': '#d6bc74',
55
- '15 - Pastagem ': '#edde8e',
56
- '18 - Agricultura/Silvicultura': '#E974ED',
57
- '22 - Área não Vegetada': '#d4271e' ,
58
- "26 - Corpo D'água": '#2532e4',
59
- }
60
-
61
- vis_params = {
62
- 'palette': ['#1f8d49','#1f8d49','#d6bc74', '#d6bc74','#edde8e', '#edde8e', '#E974ED', '#d4271e', '#2532e4'],
63
- #'min': 3,
64
- #'max': 26,
65
- }
66
-
67
- left_layer = geemap.ee_tile_layer(land_use_2011_remap, vis_params, 'Uso de Solo Mapbiomas 2011')
68
- right_layer = geemap.ee_tile_layer(land_use_2023_remap, vis_params, 'Uso de Solo Mapbiomas 2023')
69
-
70
-
71
-
72
- # Add Earth Engine layers to Map
73
-
74
- self.centerObject(ARR_project, zoom=13)
75
-
76
- self.add_layer(ARR_project,{},'Projeto de Carbono ARR')
77
-
78
- self.split_map(left_layer, right_layer, left_label='MapBiomas 2011', right_label='MapBiomas 2023',add_close_button=False, layer_control=True ,left_position='topleft',right_position='topright',zoom_control=False,fullscreen_control=False,draw_control=False)
79
-
80
- self.add_legend(title='Uso de Solo Mapbiomas', legend_dict=legend_dict)
81
-
82
- self.add_text(text='Nome: '+ project_name, position='bottomleft', offset_x=0, offset_y=0)
83
-
84
- self.add_text(text='Desenvolvedor do Projeto: ' + developer, position='bottomleft', offset_x=0, offset_y=0)
85
-
86
- self.add_text(text='Data de Término: ' + project_end, position='bottomleft', offset_x=0, offset_y=0)
87
-
88
- self.add_text(text='Data de Início: ' + project_start, position='bottomleft', offset_x=0, offset_y=0)
89
-
90
- self.add_text(text='Registro: '+ registry + ' // ID do Projeto: ' + project_ID, position='bottomleft', offset_x=0, offset_y=0)
91
-
92
-
93
-
94
-
95
-
96
-
97
-
98
- @solara.component
99
- def Page():
100
- with solara.Column(style={"max-width": "100%",'margin': "10vw"}):
101
- Map.element(
102
- height="600px",
103
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
solara-carbonARR-geemap/requirements.txt DELETED
@@ -1,5 +0,0 @@
1
- geemap
2
- geopandas
3
- pydantic< 2.0
4
- solara
5
- plotly