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<p>This study provides a four-decade assessment (1984–2024) of land use and land cover (LULC) dynamics in the Ouiouane forest area of the Middle Atlas, Morocco, a core sector of the Atlas Cedar Biosphere Reserve. Using multi-temporal Landsat 5, 7 and 8 imagery and an object-based classification with Support Vector Machines (SVM), we mapped five LULC classes: dense forest, sparse forest, uncultivated land/pasture, agricultural land and water bodies. Dense forest declined by 30.6% (− 2,833&#xa0;ha), while sparse forest and agricultural land increased by 33.5% (+ 2,668&#xa0;ha) and 144.1% (+ 1,109&#xa0;ha), respectively, indicating marked forest degradation and expansion of cultivation. Most conversions occurred between 1984 and 2004, when dense forest was largely transformed into sparse forest and agricultural land; the 2004–2024 period shows slower change, coinciding with the creation of national parks and reforestation programmes. Classification accuracies were consistently high (overall accuracy 84.9–92.0%, Kappa 0.80–0.88), supporting the reliability of the detected trends. By combining object-based SVM classification with long-term change matrices, this work delivers the first spatially explicit reconstruction of LULC trajectories in Ouiouane and highlights their implications for biodiversity conservation, hydrological regulation and climate-resilient, participatory land management in Mediterranean mountain forests.</p>

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Four decades of land use and land cover change in the ouiouane forest area (Middle Atlas, Morocco): a remote sensing and GIS-based assessment (1984–2024)

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Mohammed Chrif El Idrissi,
  • Mehdi El Attar,
  • Er-Riyahi Saber,
  • Khallaf El Ghalbi

摘要

This study provides a four-decade assessment (1984–2024) of land use and land cover (LULC) dynamics in the Ouiouane forest area of the Middle Atlas, Morocco, a core sector of the Atlas Cedar Biosphere Reserve. Using multi-temporal Landsat 5, 7 and 8 imagery and an object-based classification with Support Vector Machines (SVM), we mapped five LULC classes: dense forest, sparse forest, uncultivated land/pasture, agricultural land and water bodies. Dense forest declined by 30.6% (− 2,833 ha), while sparse forest and agricultural land increased by 33.5% (+ 2,668 ha) and 144.1% (+ 1,109 ha), respectively, indicating marked forest degradation and expansion of cultivation. Most conversions occurred between 1984 and 2004, when dense forest was largely transformed into sparse forest and agricultural land; the 2004–2024 period shows slower change, coinciding with the creation of national parks and reforestation programmes. Classification accuracies were consistently high (overall accuracy 84.9–92.0%, Kappa 0.80–0.88), supporting the reliability of the detected trends. By combining object-based SVM classification with long-term change matrices, this work delivers the first spatially explicit reconstruction of LULC trajectories in Ouiouane and highlights their implications for biodiversity conservation, hydrological regulation and climate-resilient, participatory land management in Mediterranean mountain forests.