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and Reforestation","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"---\noutput: github_document\n---\n\n[![License GPLv3](https://img.shields.io/badge/licence-GPLv3-8f10cb.svg)](https://www.gnu.org/licenses/gpl-3.0.html)\n[![Cirad Dataverse](https://img.shields.io/badge/DOI-10.18167/DVN1/7N2BTU-green)](https://doi.org/10.18167/DVN1/7N2BTU)\n[![Website ForestAtRisk](https://img.shields.io/badge/web-ForestAtRisk-blue)](https://forestatrisk.cirad.fr)\n[![forestatrisk Python package](https://img.shields.io/badge/python-forestatrisk-306998?logo=python\u0026logoColor=ffd43b\u0026color=306998)](https://ecology.ghislainv.fr/forestatrisk)\n[![bioRxiv](http://img.shields.io/badge/bioRxiv-10.1101/2022.03.22.485306-B31B1B.svg)](https://doi.org/10.1101/2022.03.22.485306)\n\nThis repository includes the code used to produce the results of the following scientific article:\n\n\u003ca href=\"https://orcid.org/0000-0002-1685-4997\"\u003e\u003cimg alt=\"ORCID logo\" src=\"Website/images/Logo_ORCID.png\" width=\"16\" height=\"16\" /\u003e\u003c/a\u003e\n**Vieilledent G.,**\n\u003ca href=\"https://orcid.org/0000-0003-3851-8588\"\u003e\u003cimg alt=\"ORCID logo\" src=\"Website/images/Logo_ORCID.png\" width=\"16\" height=\"16\" /\u003e\u003c/a\u003e\n**C. Vancutsem,**\n\u003ca href=\"https://orcid.org/0000-0003-4923-3035\"\u003e\u003cimg alt=\"ORCID logo\" src=\"Website/images/Logo_ORCID.png\" width=\"16\" height=\"16\" /\u003e\u003c/a\u003e\n**C. Bourgoin,**\n\u003ca href=\"https://orcid.org/0000-0002-8800-3593\"\u003e\u003cimg alt=\"ORCID logo\" src=\"Website/images/Logo_ORCID.png\" width=\"16\" height=\"16\" /\u003e\u003c/a\u003e\n**P. Ploton,**\n\u003ca href=\"https://orcid.org/0000-0002-5686-2764\"\u003e\u003cimg alt=\"ORCID logo\" src=\"Website/images/Logo_ORCID.png\" width=\"16\" height=\"16\" /\u003e\u003c/a\u003e\n**P. Verley,**\n**and**\n**F. Achard.**\n2023.\nSpatial scenario of tropical deforestation and carbon emissions for the 21^st^ century.\n_bioRxiv_.\ndoi: [10.1101/2022.03.22.485306](https://doi.org/10.1101/2022.03.22.485306).\n[![manuscript in pdf](Website/images/logo-pdf.png \"manuscript in pdf\")](https://www.biorxiv.org/content/10.1101/2022.03.22.485306v3.full.pdf)\nSupplementary 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)\n\n\u003cimg alt=\"ORCID logo\" src=\"Manuscript/Org-mode/figures/prob_zoom.png\" /\u003e\n\nFigure: **Pantropical map of the risk of deforestation.**\n\n## Minimal reproducible example using the `forestatrisk` Python package\n\nThis [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.\n\n## Steps followed to produce the results of the study\n\nWe 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.\n\n### 1. Preparing datasets\n\n```bash\n## Derive past forest cover change maps from the annual product \n## of Vancutsem et al. 2021 using Google Earth Engine.\npython Tropics/forest_gee_jrc.py\n\n## Download raw data from on-line databases (GADM, SRTM, WDPA, OSM), and Google Drive.\npython Tropics/download_raw_data.py\n\n## Compute explanatory variables (elevation, slope, distances, etc.).\npython Tropics/compute_variables.py\n```\n\n### 2. Estimating deforestation intensity\n\n```bash\n## Compute deforestation rates and uncertainty\nRscript Intensity/intensity.R\n\n## Estimate contagious deforestation between states of Brazil\npython Intensity/brazil_fcc_jrc.py\n```\n\n### 3. Spatial modeling and forecasting\n\n```bash\n## Model and forecast\npython Tropics/model_and_forecast.py\n```\n\n### 4. Post-processing and writing\n\n```bash\n## Combine rasters to obtain continental maps\npython Maps/combine.py\n\n## Synthesize results\nRscript Analysis/synthesis.R\n\n## Plot main maps\nRscript Maps/main_maps.R\nRscript Maps/main_maps_prob.R\n\n## Plot supplementary maps\nRscript Maps/supp_maps.R\n\n## Compile documents\nRscript Manuscript/zzz_knitr_compile/compile_book.R\n```\n\n## Website accompanying the article\n\nA website at \u003chttps://forestatrisk.cirad.fr\u003e is accompanying the article cited above. The website includes the following resources:\n\n### Interactive map\n\nWe 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:\n\n- [Map of the tropics](https://forestatrisk.cirad.fr/maps.html)\n\n### Download\n\nRasters of results from this study can be downloaded as Cloud Optimized GeoTIFFs ([COG](https://www.cogeo.org/)):\n\n- [Rasters](https://forestatrisk.cirad.fr/rasters.html)\n- [COG tutorial](https://forestatrisk.cirad.fr/notebooks/cog.html)\n\n### Supplementary data\n\n- [Data S1](https://forestatrisk.cirad.fr/data-s.html): Uncertainty around projected forest cover.\n- [Data S2](https://forestatrisk.cirad.fr/data-s.html): Uncertainty around projected carbon emissions.\n\n### `forestatrisk` Python package\n\nResults from this study have been obtained with the `forestatrisk` Python package:\n\n- [Package website](https://ecology.ghislainv.fr/forestatrisk/) (with full documentation)\n- [Tutorials](https://ecology.ghislainv.fr/forestatrisk/articles.html)\n\n\u003cspan style=\"display: block; height: 15px;\"\u003e\u003c/span\u003e\n\u003cp\u003eCopyright \u0026copy; 2021 \u003ca href=\"https://www.cirad.fr/en/\"\u003eCirad\u003c/a\u003e, \u003ca href=\"https://ec.europa.eu/jrc/en\"\u003eEC JRC\u003c/a\u003e. All rights reserved.\u003c/p\u003e\n\n\u003ca href=\"https://www.cirad.fr/en/\"\u003e\u003cimg alt=\"RF\" src=\"Website/images/Logo_RF.jpg\" height=\"75\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.cirad.fr/en/\"\u003e\u003cimg alt=\"Cirad\" src=\"Website/images/Logo_Cirad.jpg\" height=\"60\"\u003e\u003c/a\u003e\n\u003ca href=\"https://amap.cirad.fr\"\u003e\u003cimg alt=\"AMAP\" src=\"Website/images/Logo_AMAP.jpg\" height=\"60\"\u003e\u003c/a\u003e\n\u003ca href=\"https://ec.europa.eu/jrc/en\"\u003e\u003cimg alt=\"Cirad\" src=\"Website/images/Logo_JRC.png\" height=\"60\"\u003e\u003c/a\u003e\n\n\u003c!-- End of file --\u003e\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.18167/DVN1/7N2BTU","https://doi.org/10.1101/2022.03.22.485306"],"works":{"https://doi.org/10.18167/DVN1/7N2BTU":null,"https://doi.org/10.1101/2022.03.22.485306":{"id":"https://openalex.org/W4220945953","doi":"https://doi.org/10.1101/2022.03.22.485306","title":"Spatial scenario of tropical 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