forestatrisk

A Python package to model and forecast the risk of deforestation.
https://github.com/ghislainv/forestatrisk

biodiversity-scenario co2-emissions deforestation deforestation-risk forecasting forest-cover-change ipbes ipcc land-use-change protected-areas python redd roads spatial-analysis spatial-autocorrelation spatial-modelling tropical-forests

Last synced: about 7 hours ago
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Repository metadata

📦🐍 Python package to model and forecast the risk of deforestation

• Host: GitHub
• URL: https://github.com/ghislainv/forestatrisk
• Owner: ghislainv
• License: gpl-3.0
• Created: 2016-12-01T10:45:12.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-04-24T03:11:58.000Z (18 days ago)
• Last Synced: 2024-05-02T17:53:52.139Z (9 days ago)
• Topics: biodiversity-scenario, co2-emissions, deforestation, deforestation-risk, forecasting, forest-cover-change, ipbes, ipcc, land-use-change, protected-areas, python, redd, roads, spatial-analysis, spatial-autocorrelation, spatial-modelling, tropical-forests
• Language: Python
• Homepage: https://ecology.ghislainv.fr/forestatrisk
• Size: 87.9 MB
• Stars: 114
• Watchers: 6
• Forks: 27
• Open Issues: 6
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.rst
  • Codemeta: codemeta.json

README

        
..

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

   # author          :Ghislain Vieilledent

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

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

   # license         :GPLv3

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

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

   :align: right

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

   :alt: Logo forestatrisk

   :width: 140px

``forestatrisk`` Python package

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

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

Overview

========

The ``forestatrisk`` Python package can be used to **model** the

tropical deforestation spatially, **predict** the spatial risk of

deforestation, and **forecast** the future forest cover in the

tropics. It provides functions to estimate the spatial probability of

deforestation as a function of various spatial explanatory variables.

Spatial explanatory variables can be derived from topography

(altitude, slope, and aspect), accessibility (distance to roads,

towns, and forest edge), deforestation history (distance to previous

deforestation), or land conservation status (eg. protected area) for

example.

.. image:: https://ecology.ghislainv.fr/forestatrisk/_static/forestatrisk.png

   :align: center

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

   :alt: prob_AFR

   :width: 800px

Scientific publication

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

**Vieilledent G.** 2021. ``forestatrisk``: a Python package for

modelling and forecasting deforestation in the tropics.

*Journal of Open Source Software*. 6(59): 2975.

[doi: `10.21105/joss.02975 `__]. |pdf|

	   

Statement of Need

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

Spatial modelling of the deforestation allows identifying the main

factors determining the spatial risk of deforestation and quantifying

their relative effects. Forecasting forest cover change is paramount

as it allows anticipating the consequences of deforestation (in terms

of carbon emissions or biodiversity loss) under various technological,

political and socio-economic scenarios, and informs decision makers

accordingly. Because both biodiversity and carbon vary greatly in

space, it is necessary to provide spatial forecasts of forest cover

change to properly quantify biodiversity loss and carbon emissions

associated with future deforestation.

The ``forestatrisk`` Python package can be used to model the tropical

deforestation spatially, predict the spatial risk of deforestation,

and forecast the future forest cover in the tropics. The spatial data

used to model deforestation come from georeferenced raster files,

which can be very large (several gigabytes). The functions available

in the ``forestatrisk`` package process large rasters by blocks of

data, making calculations fast and efficient. This allows

deforestation to be modeled over large geographic areas (e.g. at the

scale of a country) and at high spatial resolution

(eg. ≤ 30 m). The ``forestatrisk`` package offers the possibility

of using logistic regression with auto-correlated spatial random

effects to model the deforestation process. The spatial random effects

make possible to structure the residual spatial variability of the

deforestation process, not explained by the variables of the model and

often very large. In addition to these new features, the

``forestatrisk`` Python package is open source (GPLv3 license),

cross-platform, scriptable (via Python), user-friendly (functions

provided with full documentation and examples), and easily extendable

(with additional statistical models for example). The ``forestatrisk``

Python package has been used to model deforestation and predict future

forest cover by 2100 across the humid tropics

(``__).

Installation

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

You will need several dependencies to run the ``forestatrisk`` 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 ``forestatrisk`` package with the following commands:

.. code-block:: shell

		

   conda create --name conda-far -c conda-forge python=3.9 gdal numpy matplotlib pandas patsy pip statsmodels earthengine-api --yes

   conda activate conda-far

   pip install pywdpa scikit-learn # Packages not available with conda

   pip install forestatrisk # For PyPI version

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

   # conda install -c conda-forge python-dotenv rclone --yes  # Potentially interesting libraries

To deactivate and delete the conda environment:

.. code-block:: shell

		

   conda deactivate

   conda env remove --name conda-far

Using ``virtualenv``

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

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

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

package with the following commands:

.. code-block:: shell

   cd ~

   mkdir venvs # Directory for virtual environments

   cd venvs

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

   source ~/venvs/venv-far/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 forestatrisk # For PyPI version, this will install all other dependencies

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

   pip install statsmodels # Optional additional packages

To deactivate and delete the virtual environment:

.. code-block:: shell

		

   deactivate

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

Installation testing

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

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

command ``forestatrisk`` in a terminal:

.. code-block:: shell

  forestatrisk

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

and the version number:

.. code-block:: shell

  # forestatrisk: modelling and forecasting deforestation in the tropics.

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

  # forestatrisk version x.x.

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

started

`__

tutorial.

   

Main functionalities

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

Sample

++++++

Function ``.sample()`` sample observations points from a forest cover

change map. The sample is balanced and stratified between deforested

and non-deforested pixels. The function also retrieves information

from explanatory variables for each sampled point. Sampling is done by

block to allow computation on large study areas (e.g. country or

continental scale) with a high spatial resolution (e.g. 30m).

Model

+++++

Function ``.model_binomial_iCAR()`` can be used to fit the

deforestation model. A linear Binomial logistic regression model is

used in this case. The model includes an intrinsic Conditional

Autoregressive (iCAR) process to account for the spatial

autocorrelation of the observations. Parameter inference is done in a

hierarchical Bayesian framework. The function calls a Gibbs sampler

with a Metropolis algorithm written in pure C code to reduce

computation time.

Other models (such as a simple GLM or a Random Forest model) can also

be used.

Predict and project

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

Function ``.predict()`` allows predicting the deforestation

probability on the whole study area using the deforestation model

fitted with ``.model_*()`` functions. The prediction is done by block

to allow the computation on large study areas (e.g. country or

continental scale) with a high spatial resolution (e.g. 30m).

Function ``.deforest()`` predicts the future forest cover map based on a

raster of probability of deforestation (rescaled from 1 to 65535),

which is obtained from function ``.predict()``, and an area (in

hectares) to be deforested.

Validate

++++++++

A set of functions (eg. ``.cross_validation()`` or

``.map_accuracy()``\ ) is also provided to perform model and map

validation.

Contributing

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

The ``forestatrisk`` 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/forestatrisk?logo=python&logoColor=ffd43b&color=306998

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

   :alt: Python version

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

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

   :alt: PyPI version

.. |GitHub Actions| image:: https://github.com/ghislainv/forestatrisk/workflows/PyPkg/badge.svg

   :target: https://github.com/ghislainv/forestatrisk/actions

   :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.996337.svg

   :target: https://doi.org/10.5281/zenodo.996337

   :alt: Zenodo

.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.02975/status.svg

   :target: https://doi.org/10.21105/joss.02975

   :alt: JOSS

.. |pdf| image:: https://ecology.ghislainv.fr/forestatrisk/_static/logo-pdf.png

   :target: https://www.theoj.org/joss-papers/joss.02975/10.21105.joss.02975.pdf

   :alt: pdf

      

---

Owner metadata

• Name: Ghislain Vieilledent
• Login: ghislainv
• Email:
• Kind: user
• Description: Researcher in ecology and applied statistics at Cirad
• Website: https://ecology.ghislainv.fr
• Location: Montpellier
• Twitter: ghislainv
• Company: Cirad
• Icon url: https://avatars.githubusercontent.com/u/4931332?u=d1e3de1f38eeb6fc74d6d4f8adc6285c961f5524&v=4
• Repositories: 72
• Last ynced at: 2023-08-13T06:33:18.729Z
• Profile URL: https://github.com/ghislainv

---

GitHub Events

Total

• Create event: 21
• Issues event: 39
• Release event: 6
• Watch event: 122
• Delete event: 13
• Member event: 1
• Issue comment event: 41
• Push event: 446
• Pull request event: 102
• Fork event: 28

Last Year

• Create event: 6
• Delete event: 5
• Fork event: 6
• Issue comment event: 2
• Issues event: 5
• Pull request event: 2
• Push event: 68
• Release event: 3
• Watch event: 17

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Committers metadata

Last synced: 1 day ago

Name	Email	Commits
Ghislain Vieilledent	g****t@c****r	564
Pierrick Rambaud	p****9@g****m	8
Daniel S. Katz	d****z@i****g	2
thomasarsouze	t****e@z****m	1
Ethan White (he/him)	e****n@w****g	1
Juan	m****r@g****m	1
Katy Barnhart	k****t@u****v	1

Committer domains:

• usgs.gov: 1
• weecology.org: 1
• zoho.com: 1
• ieee.org: 1
• cirad.fr: 1

---

Issue and Pull Request metadata

Last synced: 1 day ago

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/ghislainv/forestatrisk

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Package metadata

• Total packages: 1
• Total downloads:
  • pypi: 852 last-month
• Total dependent packages: 0
• Total dependent repositories: 1
• Total versions: 8
• Total maintainers: 1

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Dependencies

Score: 13.48333308562593