https://github.com/12rambau/forestatrisk-tropics

:earth_africa: Modelling and forecasting deforestation in the tropics

https://github.com/12rambau/forestatrisk-tropics

Repository

:earth_africa: Modelling and forecasting deforestation in the tropics

https://github.com/12rambau/forestatrisk-tropics/blob/master/

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


**Vieilledent G.,**

**C. Vancutsem,** **F. Achard.** Forest refuge areas and carbon
emissions from tropical deforestation in the 21st century.



Figure 2: **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
(200020102020), 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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