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Forest Mapping

Predicting Pacific Northwest forest types from remotely-sensed data.

This repository includes data cleaning, model-fitting, and applications of predictive models to estimate basic forest attributes using lidar data, satellite and aerial imagery, and down-scaled climate information.


This effort has been supported by two Conservation Innovation Grants from the USDA Oregon Natural Resources Conservation Service:

  • "Technology Transfer for Rapid Family Forest Assessment and Stewardship Planning" - FY 2017 Oregon Conservation Innovation Grant, Award # 69-0436-17-036.
  • "Modern Land Mapping Toolkit to Streamline Forest Stewardship Planning" -
    FY 2019 Oregon Conservation Innovation Grant, Award # NR190436XXXXG012

This effort has also been supported by a grant of cloud storage and computing services made available to Ecotrust under the Microsoft AI for Earth Program in a project entitled "Mining Public Datasets to Automate Forest Stand Delineation and Labeling."

Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Working versions of data during processing.
│   ├── processed      <- Processed datasets ready for modeling.
│   └── raw            <- Raw data
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Reports (PDF, etc.)
│   └── figures        <- Generated graphics and figures used in reports.
│
├── environment.yml    <- The requirements file for reproducing the analysis environment, e.g.
│                         using `conda create env --file environment.yml`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    │
    ├── data           <- Scripts to download or generate data
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │
    ├── models         <- Scripts to train models and then use trained models to make
    │                     predictions
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualization

Project organization based on the cookiecutter data science project template. #cookiecutterdatascience

forestmapping's People

Contributors

d-diaz avatar sloreno avatar

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