Are you sure you want to delete this access key?
Legend |
---|
DVC Managed File |
Git Managed File |
Metric |
Stage File |
External File |
Legend |
---|
DVC Managed File |
Git Managed File |
Metric |
Stage File |
External File |
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
Given how the Swiss Land Statistics datasets are provided (see this for more info), 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.
The results are displayed in invest_carbon.ipynb
Create the conda environment
# the environment's name will be `carbonseq_vaud`
conda env create -f environment.yml
Configure your S3 profile (credentials, region and endpoint URL)
Enter the fresh environment
conda activate carbonseq_vaud
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)"
From the repository's root, create a folder named papermill_outputs
Pull the data from the dvc remote
dvc pull
Reproduce the land data frame
dvc repro data/vaud_ldf.csv.dvc
Now you can execute the Notebook invest.ipynb
Press p or to see the previous file or, n or to see the next file
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?