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https://github.com/ghislainv/riskmapjnr

📦🐍 Mapping deforestation risk following JNR methodology
https://github.com/ghislainv/riskmapjnr

climate-change-mitigation co2-emissions deforestation-risk forest-conservation jurisdictional-and-nested-redd verified-carbon-standard

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📦🐍 Mapping deforestation risk following JNR methodology

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README 

        
..

   # ==============================================================================

   # author          :Ghislain Vieilledent

   # email           :[email protected], [email protected]

   # web             :https://ecology.ghislainv.fr

   # license         :GPLv3

   # ==============================================================================



.. image:: https://ecology.ghislainv.fr/riskmapjnr/_static/logo-riskmapjnr.svg

   :align: right

   :target: https://ecology.ghislainv.fr/riskmapjnr

   :alt: Logo riskmapjnr

   :width: 140px



``riskmapjnr`` Python package

*****************************



|Python version| |PyPI version| |GitHub Actions| |License| |Zenodo|



Overview

========



The ``riskmapjnr`` Python package can be used to obtain **maps of the

spatial risk of deforestation and forest degradation** following the

methodology developed in the context of the Jurisdictional and Nested

REDD+ (`JNR`_) and using only a forest cover change map as input.



.. _JNR:

   https://verra.org/project/jurisdictional-and-nested-redd-framework/



.. image:: https://ecology.ghislainv.fr/riskmapjnr/_static/riskmapjnr-example.png

   :align: center

   :target: https://ecology.ghislainv.fr/riskmapjnr

   :alt: riskmapjnr-example

   :width: 1300px



Statement of Need

=================



The `VCS`_ (Verified Carbon Standard) program allows certified

projects to turn their greenhouse gas (GHG) emission reductions and

removals into tradable carbon credits. Since its launch in 2006, the

VCS program has grown into the world's largest voluntary GHG program.



In the forest sector, programs to mitigate GHG emissions across entire

national or subnational jurisdictions (called `REDD+`_ programs,

i.e. programs aiming at Reducing Emissions from Deforestation and

Forest Degradation) can be accounted for and credited using a

jurisdictional-scale framework, the Jurisdictional and Nested REDD+

(`JNR`_) framework. JNR integrates government-led and project-level

REDD+ activities and establishes a clear pathway for subnational- and

project-level activities to be incorporated within broader REDD+

programs. The JNR framework ensures all projects and other reducing

emissions from deforestation and degradation activities in a given

jurisdiction are developed using consistent baselines and crediting

approaches. They mitigate the risk of "leakage", i.e. the displacement

of emission-causing activities to areas outside the project boundary,

by monitoring emissions across an entire jurisdictional area.



The `JNR Risk Mapping Tool`_ is a "benchmark" methodology that

provides a standardized approach for developing deforestation and

forest degradation risk maps for users of the `JNR Allocation Tool`_

in the context of Jurisdictional and Nested REDD+ (JNR)

requirements. The methodology allows deriving a map of the

deforestation (or degradation) risk based on a minimal spatial

information provided by the past deforestation (or degradation) map at

the jurisdictional scale.



The `JNR Risk Mapping Tool`_ allows the creation of categorical and

spatially static maps whose categories represent different levels of

risk of deforestation or forest degradation in the validity period of

the Forest Reference Emissions Level (FREL) and throughout the

jurisdictional geographical boundaries. In the `JNR Allocation Tool`_,

the level of risk determines how the jurisdictional FREL is spatially

distributed to nested lower-level jurisdictional programs and

projects.



While the `JNR Risk Mapping Tool`_ methodology favors simplicity,

obtaining the risk map is not straightforward. The approach requires

several geoprocessing steps on raster data that can be large,

i.e. covering large spatial extent (eg. national scale) at high

spatial resolution (eg. 30 m). The ``riskmapjnr`` Python package

includes functions to perform these geoprocessing steps and derive a

risk map on any jurisdiction and at any spatial resolution following

the `JNR Risk Mapping Tool`_ methodology.



.. _VCS:

   https://verra.org/project/vcs-program/



.. _REDD+:

   https://redd.unfccc.int/



.. _JNR:

   https://verra.org/project/jurisdictional-and-nested-redd-framework/

   

.. _JNR Risk Mapping Tool:

   https://verra.org/wp-content/uploads/2021/04/DRAFT_JNR_Risk_Mapping_Tool_15APR2021.pdf



.. _JNR Allocation Tool:

   https://verra.org/wp-content/uploads/2021/04/JNR_Allocation_Tool_Guidance_v4.0.pdf



Installation

============



You will need several dependencies to run the ``riskmapjnr`` Python

package. The best way to install the package is to create a Python

virtual environment, either through ``conda`` (recommended) or

``virtualenv``.



Using ``conda`` (recommended)

+++++++++++++++++++++++++++++



You first need to have ``miniconda3`` installed (see `here

`__).



Then, create a conda environment (details `here

`__)

and install the ``riskmapjnr`` package with the following commands:



.. code-block:: shell

		

   conda create --name conda-rmj -c conda-forge python=3 gdal numpy matplotlib pandas pip scipy --yes

   conda activate conda-rmj

   pip install riskmapjnr # For PyPI version

   # pip install https://github.com/ghislainv/riskmapjnr/archive/master.zip # For GitHub dev version

   # conda install -c conda-forge sphinx flake8 jedi jupyter geopandas descartes folium --yes  # Optional additional packages



To deactivate and delete the conda environment:



.. code-block:: shell

		

   conda deactivate

   conda env remove --name conda-rmj



Using ``virtualenv``

++++++++++++++++++++



You first need to have the ``virtualenv`` package installed (see `here `__).



Then, create a virtual environment and install the ``riskmapjnr``

package with the following commands:



.. code-block:: shell



   cd ~

   mkdir venvs # Directory for virtual environments

   cd venvs

   virtualenv --python=/usr/bin/python3 venv-rmj

   source ~/venvs/venv-rmj/bin/activate

   # Install numpy first

   pip install numpy

   # Install gdal (the correct version) 

   pip install --global-option=build_ext --global-option="-I/usr/include/gdal" gdal==$(gdal-config --version)

   pip install riskmapjnr # For PyPI version, this will install all other dependencies

   # pip install https://github.com/ghislainv/riskmapjnr/archive/master.zip # For GitHub dev version

   # pip install sphinx flake8 jedi jupyter geopandas descartes folium # Optional additional packages



To deactivate and delete the virtual environment:



.. code-block:: shell

		

   deactivate

   rm -R ~/venvs/venv-rmj # Just remove the repository



Installation testing

++++++++++++++++++++



You can test that the package has been correctly installed using the

command ``riskmapjnr`` in a terminal:



.. code-block:: shell



  riskmapjnr



This should return a short description of the ``riskmapjnr`` package

and the version number:



.. code-block:: shell



  # riskmapjnr: Map of deforestation risk following JNR methodology.

  # https://ecology.ghislainv.fr/riskmapjnr/

  # riskmapjnr version x.x.



You can also test the package executing the commands in the `Get

started

`__

tutorial.

   

Main functionalities

====================



The ``riskmapjnr`` package includes functions to:



1. Estimate the distance to forest edge beyond which the deforestation

   risk is negligible: ``dist_edge_threshold()``.

2. Compute local deforestation rates using a moving window whose size

   can vary: ``local_defor_rate()``.

3. Transform local deforestation rates into categories of

   deforestation risks using several slicing algorithms:

   ``set_defor_cat_zero()`` and ``defor_cat()``

4. Validate maps of deforestation risk and select the map with the

   higher accuracy: ``defrate_per_cat()`` and ``validation()``.



The ``riskmapjnr`` package uses several known Python scientific

packages such as ``NumPy``, ``SciPy``, and ``Pandas`` for fast matrix

and vector operations and ``gdal`` for processing georeferenced raster

data. Raster data are divided into blocks of data for in-memory

processing. Such an approach allow processing large raster files with

large geographical extents (e.g. country scale) and high spatial

resolutions (eg. 30 m).



Contributing

============



The ``riskmapjnr`` Python package is Open Source and released under

the `GNU GPL version 3 license

`__. Anybody

who is interested can contribute to the package development following

our `Community guidelines

`__. Every

contributor must agree to follow the project's `Code of conduct

`__.



.. |Python version| image:: https://img.shields.io/pypi/pyversions/riskmapjnr?logo=python&logoColor=ffd43b&color=306998

   :target: https://pypi.org/project/riskmapjnr

   :alt: Python version



.. |PyPI version| image:: https://img.shields.io/pypi/v/riskmapjnr

   :target: https://pypi.org/project/riskmapjnr

   :alt: PyPI version



.. |GitHub Actions| image:: https://github.com/ghislainv/riskmapjnr/actions/workflows/python-package.yml/badge.svg

   :target: https://github.com/ghislainv/riskmapjnr/actions/workflows/python-package.yml

   :alt: GitHub Actions

	 

.. |License| image:: https://img.shields.io/badge/licence-GPLv3-8f10cb.svg

   :target: https://www.gnu.org/licenses/gpl-3.0.html

   :alt: License GPLv3	 



.. |Zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.6670011.svg

   :target: https://doi.org/10.5281/zenodo.6670011

   :alt: Zenodo