This repository contains the code for a spatial planning tool to support implementation of the 30 x 30 initiative targets in the Democratic Republic of Congo (DRC). Specifically, this tool utilizes several geospatial datasets to find the most spatially optimized scenario for protecting new areas within DRC to meet the 30 x 30 target.
The tool is built based on a dataset developed at spatial planning units of 1 km x 1 km. For each planning unit, a set of variables are calculated from existing products (e.g. above ground biomass). Existing protected areas are also mapped to the 1 km x 1 km grid, and they are input as constraints to the model.
This code should be used as a tool to run different scenarios for protection. As an example, two scenarios are included in the configuration files as follows:
config_pas.yml: This configuration assumes expansion under protected areas only scenario.config_mixed.yml: This configuration assumes expansion under the mixed approach where both protected areas and OECMs are considered.
This toolbox is developed in Python. To ensure reproducibility of results, the tool is wrapped in a Docker environment. To run the code locally, you need to install Docker for free and follow the instructions below.
If you are running this on Windows, it's recommended to run it inside Windows Subsystem for Linux (WSL). Follow the instructions here to enable WSL in your Windows.
The input data to this tool is a CSV file containing all the required variables derived from products available on Google Earth Engine (GEE). The CSV file should be added to the local directory of the project for the code to execute. You can use the tool implemented here to generate the CSV file.
The files named config_XXX.yml contain all the parameters that are used in the toolbox. These include name of the CSV input file and name of all variables used for optimization. Other parameters include the weight of each variable in the optimization function, and list of variables that should be used as constraint in the optimization.
In order to develop other scenarios, or create variations of the existing scenario by changing the weights of different covariates, you need to change the config file (or create a new one).
git clone git@github.com:ClarkCGA/drc_30x30_opt.git
cd drc_30x30_optdocker build -t drc-30x30-opt .docker run -it -p 8888:8888 -v $(pwd):/app drc-30x30-optThis will print out the URL to the Jupyter Lab (including its token). Copy the URL, and paste into a browser to launch Jupyter Lab.
Open and run all cells in the Jupyter notebook main_opt.ipynb
This project is funded by the Wildlife Conservation Society (WCS) through a contract with Clark CGA.
If you run into any issues running this code, or if you have questions about it you can open a ticket on this GitHub repository or contact Clark CGA team at cga@clarku.edu.