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https://github.com/ghislainv/forestatrisk-tropics

:earth_africa: :pencil: Modelling and forecasting deforestation in the tropics
https://github.com/ghislainv/forestatrisk-tropics

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

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:earth_africa: :pencil: Modelling and forecasting deforestation in the tropics

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README 

        
---

output: github_document

---



[![License GPLv3](https://img.shields.io/badge/licence-GPLv3-8f10cb.svg)](https://www.gnu.org/licenses/gpl-3.0.html)

[![Cirad Dataverse](https://img.shields.io/badge/DOI-10.18167/DVN1/7N2BTU-green)](https://doi.org/10.18167/DVN1/7N2BTU)

[![Website ForestAtRisk](https://img.shields.io/badge/web-ForestAtRisk-blue)](https://forestatrisk.cirad.fr)

[![forestatrisk Python package](https://img.shields.io/badge/python-forestatrisk-306998?logo=python&logoColor=ffd43b&color=306998)](https://ecology.ghislainv.fr/forestatrisk)

[![bioRxiv](http://img.shields.io/badge/bioRxiv-10.1101/2022.03.22.485306-B31B1B.svg)](https://doi.org/10.1101/2022.03.22.485306)



This repository includes the code used to produce the results of the following scientific article:



ORCID logo

**Vieilledent G.,**

ORCID logo

**C. Vancutsem,**

ORCID logo

**C. Bourgoin,**

ORCID logo

**P. Ploton,**

ORCID logo

**P. Verley,**

**and**

**F. Achard.**

2023.

Spatial scenario of tropical deforestation and carbon emissions for the 21^st^ century.

_bioRxiv_.

doi: [10.1101/2022.03.22.485306](https://doi.org/10.1101/2022.03.22.485306).

[![manuscript in pdf](Website/images/logo-pdf.png "manuscript in pdf")](https://www.biorxiv.org/content/10.1101/2022.03.22.485306v3.full.pdf)

Supplementary Information [![SI](Website/images/logo-zip.png "supplementary information")](https://www.biorxiv.org/content/biorxiv/early/2023/05/12/2022.03.22.485306/DC1/embed/media-1.pdf)



ORCID logo



Figure: **Pantropical map of the risk of deforestation.**



## Minimal reproducible example using the `forestatrisk` Python package



This [notebook](https://ecology.ghislainv.fr/forestatrisk/notebooks/far_tropics.html) provides a minimal and reproducible example presenting the general approach we followed to model and forecast deforestation in each of the 119 study areas (representing 92 countries) considered in the above article. We use the Guadeloupe archipelago as a case study. The notebook is available at the [website](https://ecology.ghislainv.fr/forestatrisk) associated with the `forestatrisk` Python package. This package has been specifically developed for this study and provides functions to model and forecast deforestation in the tropics.



## Steps followed to produce the results of the study



We present below the R and Python scripts which have been used to produce the results of the study, from the datasets preparation to the writing of the manuscript.



### 1. Preparing datasets



```bash

## Derive past forest cover change maps from the annual product 

## of Vancutsem et al. 2021 using Google Earth Engine.

python Tropics/forest_gee_jrc.py



## Download raw data from on-line databases (GADM, SRTM, WDPA, OSM), and Google Drive.

python Tropics/download_raw_data.py



## Compute explanatory variables (elevation, slope, distances, etc.).

python Tropics/compute_variables.py

```



### 2. Estimating deforestation intensity



```bash

## Compute deforestation rates and uncertainty

Rscript Intensity/intensity.R



## Estimate contagious deforestation between states of Brazil

python Intensity/brazil_fcc_jrc.py

```



### 3. Spatial modeling and forecasting



```bash

## Model and forecast

python Tropics/model_and_forecast.py

```



### 4. Post-processing and writing



```bash

## Combine rasters to obtain continental maps

python Maps/combine.py



## Synthesize results

Rscript Analysis/synthesis.R



## Plot main maps

Rscript Maps/main_maps.R

Rscript Maps/main_maps_prob.R



## Plot supplementary maps

Rscript Maps/supp_maps.R



## Compile documents

Rscript Manuscript/zzz_knitr_compile/compile_book.R

```



## Website accompanying the article



A website at  is accompanying the article cited above. The website includes the following resources:



### Interactive map



We release interactive pantropical maps of the past forest cover change (2000--2010--2020), of the risk of deforestation (2020), and of the projected forest cover in 2050 and 2100:



- [Map of the tropics](https://forestatrisk.cirad.fr/maps.html)



### Download



Rasters of results from this study can be downloaded as Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org/)):



- [Rasters](https://forestatrisk.cirad.fr/rasters.html)

- [COG tutorial](https://forestatrisk.cirad.fr/notebooks/cog.html)



### Supplementary data



- [Data S1](https://forestatrisk.cirad.fr/data-s.html): Uncertainty around projected forest cover.

- [Data S2](https://forestatrisk.cirad.fr/data-s.html): Uncertainty around projected carbon emissions.



### `forestatrisk` Python package



Results from this study have been obtained with the `forestatrisk` Python package:



- [Package website](https://ecology.ghislainv.fr/forestatrisk/) (with full documentation)

- [Tutorials](https://ecology.ghislainv.fr/forestatrisk/articles.html)





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