A Random Forest-Cellular Automata Modeling Approach to Predict Future Forest Cover Change in Middle Atlas Morocco, Under Anthropic, Biotic and Abiotic Parameters.

This study aims to predict forest species cover changes in the Sidi M’Guild Forest (Mid Atlas, Morocco). Used approach combines remote sensing and GIS and is based on training Cellular Automata and Random Forest (RF) regression model for predicting species cover transition. Five covariates that precludes such transition have been chosen according to Pearson’s test. The model was trained and validated based on the use of forest cover stratum transition probabilities between 1990 and 2004 and then validated using 2018 forest species cover map. Validation of the predicted map with that of 2018 shows an overall agreement between the two maps (72%) for each number of RF’s trees used. The 2032 projected forest species cover map indicate a strong regression of Cedar atlas and thuriferous juniper cover and a medium regression of mixture holm oak and thuriferous juniper, mixture of atlas cedar and thuriferous juniper, and sylvatic and asylvatic vacuums, a very strong progression of holm oak, and of mixture atlas cedar, holm oak and thuriferous juniper and medium progression of mixture of atlas cedar and holm oak. These findings provide important insights to planners, natural resource managers and policy-makers to reconsider their strategies to ensure the sustainability goals.