Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Hybrid Machine Learning Approach to Model Cedar Forest Cover Changes in Morocco’s Middle Atlas

Domaine:

environment and energyclimategeospatial

Type de record:

paper
Créateur:
AnaAyoBouSai
Éditeur:
Ass
Hôte:
The Atlas cedar forests in the Moroccan Middle Atlas, particularly the Sidi M'Guild region, are undergoing rapid degradation due to increasing climatic stress and anthropogenic pressure. This study introduces a hybrid modelling approach integrating random forest (RF), cellular automata (CA) and Markov chains to simulate forest cover dynamics from 1990 to 2032. The model integrates remote sensing data from Landsat 4, 8 and Sentinel-2, bioclimatic variables (temperature, seasonality, rainfall of the driest quarter) and indicators of human influence (density of occupancy, proximity to forest edges). The results project a 91% decline in Cedrus atlantica and a 74% decline in juniper, contrasted with a 1,290% expansion of holm oak, indicating a major ecological shift to drought-tolerant hardwoods. The RF–AdaBoost classifier achieved 98% accuracy, and the RF–CA–Markov framework demonstrated strong predictive power (Kappa = 0.72). These results offer a solid tool to anticipate forest transitions and guide adaptive forest management strategies, aligned with Morocco's national reforestation efforts.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by-nc-sa/4.0

Similaires

Modeling the mean annual increment (MAI) of Atlas cedar (Cedrus atlantica (Endl.) Manetti ex Carrière) using advanced machine learning algorithms in the Middle Atlas region, MoroccoHYBRID PREDICTIVE MODEL FOR STUDENTS’ ACADEMIC PERFORMANCE BASED ON MACHINE LEARNING APPROACHBiomass Prediction Using Machine Learning Techniques In Google Earth Engine: A Case Study Of The Azrou Forest In The Middle Atlas Mountains, MoroccoA Hybrid Machine Learning Model for Predicting Surgical Procedure Duration: Integrating Random Forest and K-Means ClusteringPredicting food prices in Kenya using machine learning: a hybrid model approach with XGBoost and gradient boostingBreast Cancer Subtypes Classification with Hybrid Machine Learning Model

Modeling the mean annual increment (MAI) of Atlas cedar (Cedrus atlantica (Endl.) Manetti ex Carrière) using advanced machine learning algorithms in the Middle Atlas region, Morocco

Source Agritrop Cirad (https://agritrop.cirad.fr/617365/) International audience The

HYBRID PREDICTIVE MODEL FOR STUDENTS’ ACADEMIC PERFORMANCE BASED ON MACHINE LEARNING APPROACH

Student academic performance is a critical factor in assessing the quality of education and institut

Biomass Prediction Using Machine Learning Techniques In Google Earth Engine: A Case Study Of The Azrou Forest In The Middle Atlas Mountains, Morocco

In the context of climate change, forests are a vital source of ecosystem services for humankind, ac

A Hybrid Machine Learning Model for Predicting Surgical Procedure Duration: Integrating Random Forest and K-Means Clustering

International audience Efficient operating room (OR) management depends on the accura

Predicting food prices in Kenya using machine learning: a hybrid model approach with XGBoost and gradient boosting

Introduction Food price volatility continues to be a significant concern in Ke

Breast Cancer Subtypes Classification with Hybrid Machine Learning Model

Abstract Background Breast cancer is the most prevailing heterogeneous disease among fem