diff --git a/README.rst b/README.rst index 4aeac1c..90f8c0a 100644 --- a/README.rst +++ b/README.rst @@ -22,15 +22,16 @@ Overview ======== -The ``forestatrisk`` Python package can be used to **model** and -**forecast** deforestation in the tropics. It provides functions to -estimate the spatial probability of deforestation as a function of -various spatial explanatory variables. +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 +deforestation), or land conservation status (eg. protected area) for example. .. image:: https://ecology.ghislainv.fr/forestatrisk/_static/forestatrisk.png @@ -39,6 +40,43 @@ example. :alt: prob_AFR :width: 800px +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. :math:`\leq` 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 ============ @@ -118,12 +156,14 @@ and the version number: .. code-block:: shell - # ForestAtRisk: Modelling and forecasting deforestation in the tropics. + # forestatrisk: modelling and forecasting deforestation in the tropics. # https://ecology.ghislainv.fr/forestatrisk/ - # ForestAtRisk version 0.2. + # forestatrisk version x.x. You can also test the package executing the commands in the `Get -started `__ tutorial. +started +`__ +tutorial. Main functionalities ==================== @@ -178,9 +218,10 @@ 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 +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 `__. diff --git a/docs/_modules/forestatrisk/forestatrisk.html b/docs/_modules/forestatrisk/forestatrisk.html index c5ae66d..b0234aa 100644 --- a/docs/_modules/forestatrisk/forestatrisk.html +++ b/docs/_modules/forestatrisk/forestatrisk.html @@ -127,7 +127,7 @@

Source code for forestatrisk.forestatrisk

     ``forestatrisk`` Python package can be correctly imported.
 
     """
-    print("# ForestAtRisk version {}.".format(__version__))
+    print("# forestatrisk version {}.".format(__version__))
     return None
# End diff --git a/docs/index.html b/docs/index.html index 2f8484d..b07ea0f 100644 --- a/docs/index.html +++ b/docs/index.html @@ -108,17 +108,53 @@

forestatriskPython version PyPI version GitHub Actions License GPLv3 Zenodo JOSS

Overview

-

The forestatrisk Python package can be used to model and -forecast deforestation in the tropics. It provides functions to -estimate the spatial probability of deforestation as a function of -various spatial explanatory variables.

+

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

prob_AFR
+
+

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. \(\leq\) 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 +(https://forestatrisk.cirad.fr).

+

Installation

You will need several dependencies to run the forestatrisk Python @@ -178,13 +214,14 @@

Installation testing

This should return a short description of the forestatrisk package and the version number:

-
# ForestAtRisk: Modelling and forecasting deforestation in the tropics.
+
# forestatrisk: modelling and forecasting deforestation in the tropics.
 # https://ecology.ghislainv.fr/forestatrisk/
-# ForestAtRisk version 0.2.
+# forestatrisk version x.x.
 

 

-
You can also test the package executing the commands in the Get
-started tutorial.

+
You can also test the package executing the commands in the Get
+started
+tutorial.
@@ -233,10 +270,10 @@

Validate

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.

+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.

Table of contents

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\ No newline at end of file diff --git a/forestatrisk/__init__.py b/forestatrisk/__init__.py index c1de57e..66b1371 100644 --- a/forestatrisk/__init__.py +++ b/forestatrisk/__init__.py @@ -46,7 +46,7 @@ from .validate import validation_npix # Welcome message -print("# ForestAtRisk: Modelling and forecasting deforestation in the tropics.") +print("# forestatrisk: modelling and forecasting deforestation in the tropics.") print("# https://ecology.ghislainv.fr/forestatrisk/") # EOF diff --git a/forestatrisk/forestatrisk.py b/forestatrisk/forestatrisk.py index f4db5fb..69afcfe 100644 --- a/forestatrisk/forestatrisk.py +++ b/forestatrisk/forestatrisk.py @@ -22,7 +22,7 @@ def main(): ``forestatrisk`` Python package can be correctly imported. """ - print("# ForestAtRisk version {}.".format(__version__)) + print("# forestatrisk version {}.".format(__version__)) return None # End diff --git a/paper/paper.md b/paper/paper.md index a6f22fe..a1684c4 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -40,7 +40,7 @@ link-citations: yes # Summary -The `forestatrisk` Python package can be used to model the spatial probability of deforestation and predict future forest cover in the tropics. The spatial data used to model deforestation comes 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. $\leq$ 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 statistical models for example). The Python package `forestatrisk` was recently used to model deforestation and predict future forest cover by 2100 across the humid tropics. +The `forestatrisk` Python package can be used to model the spatial probability of deforestation and predict 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. $\leq$ 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.