https://github.com/martibosch/carbon-sequestration-vaud

Evaluation of the carbon sequestation for the canton of Vaud
https://github.com/martibosch/carbon-sequestration-vaud

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Evaluation of the carbon sequestation for the canton of Vaud

• Host: GitHub
• URL: https://github.com/martibosch/carbon-sequestration-vaud
• Owner: martibosch
• License: gpl-3.0
• Created: 2018-11-07T18:21:15.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-07-14T10:36:07.000Z (over 5 years ago)
• Last Synced: 2024-06-11T16:30:17.207Z (7 months ago)
• Language: Jupyter Notebook
• Homepage: https://doi.org/10.1007/s10980-019-00850-7
• Size: 310 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Evaluation of the carbon sequestration for the canton of Vaud

**Citation**: Jaligot, R., Chenal, J. & Bosch, M. "Assessing spatial temporal patterns of ecosystem services in Switzerland". *Landscape Ecol* (2019): 1-16. https://doi.org/10.1007/s10980-019-00850-7

![Evolution of the carbon stock](evolution-carbon-stock.png)

## Analysis DAG

Given how the Swiss Land Statistics datasets are provided (see [this for more info](https://github.com/martibosch/swisslandstats-geopy)), we work with "LandDataFrames", i.e., tables where each row correspond to an (x, y) geo-referenced pixel, and columns provide categorical information, such as the land use/land cover, elevation, production regions and organic soil. This information is used to compute the carbon stock with the InVEST's carbon model.

![analysis-dag](analysis-dag.png)

The results are displayed in [invest_carbon.ipynb](https://github.com/martibosch/carbon-sequestration-vaud/blob/master/invest_carbon.ipynb)

## Instructions to reproduce the repository

### Preparing the environment

1. Create the conda environment

        # the environment's name will be `carbonseq_vaud`

        conda env create -f environment.yml

2. Configure your S3 profile (credentials, region and endpoint URL)

3. Enter the fresh environment

        conda activate carbonseq_vaud

4. Already within the environment, make it available as a `jupyter` kernel as in:

        python -m ipykernel install --user --name carbonseq_vaud --display-name "Python (carbonseq_vaud)"

### Reproducing

1. From the repository's root, create a folder named `papermill_outputs`

2. Pull the data from the dvc remote

        dvc pull

        

3. Reproduce the land data frame

        dvc repro data/vaud_ldf.csv.dvc

        

Now you can execute the Notebook `invest.ipynb`