Forestatrisk: A Python package for modelling and forecasting deforestation in the tropics

Vieilledent Ghislain. 2021. Forestatrisk: A Python package for modelling and forecasting deforestation in the tropics. Journal of Open Source Software, 6 (59):2975, 6 p.

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

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Url - jeu de données - Entrepôt autre : https://ecology.ghislainv.fr/forestatrisk/

Résumé : 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 (e.g., _ 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.

Mots-clés Agrovoc : déboisement, modélisation environnementale, modèle de simulation, technique de prévision, couvert forestier, données spatiales, forêt tropicale, changement dans l'usage des terres, émissions de gaz à effet de serre, logiciel

Mots-clés libres : CO2 emissions, Biodiversity scenarios, Deforestation, Deforestation risk, Forecasting, Forest cover change, IPBES, IPCC, Land cover change, Land use change, Protected areas, Python, REDD+, Roads, Spatial modelling, Spatial autocorrelation, Tropical forests