Vol. 120 No. 1 (2026)
Research Papers

GIS-Fuzzy Logic Approach for Ecological Restoration Suitability Mapping in Semi-Arid Steppe Ecosystems: A Case Study of Naâma Province, Algeria

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  • Abderrahmane Mebarki,
  • Tayeb Sitayeb
Abderrahmane Mebarki
Laboratory of Biotoxicology, Pharmacognosy and Biological Valorization of Plants. Department of Agronomy and Nutritional Science, Faculty of Natural Science and Life, University of Saida Dr. Moulay Tahar, BP 138 City ENNASR 20000, Saida, Algeria
Tayeb Sitayeb
Laboratory of Biotoxicology, Pharmacognosy and Biological Valorization of Plants. Department of Agronomy and Nutritional Science, Faculty of Natural Science and Life, University of Saida Dr. Moulay Tahar, BP 138 City ENNASR 20000, Saida, Algeria
DOI: https://doi.org/10.36253/jaeid-19875

Published 2026-06-29

Keywords

  • Ecological restoration,
  • fuzzy membership functions,
  • restoration prioritization,
  • semi-arid land degradation,
  • steppe landscapes,
  • spatial modeling
    • ...More
    Less

How to Cite

Mebarki, A., & Sitayeb, T. (2026). GIS-Fuzzy Logic Approach for Ecological Restoration Suitability Mapping in Semi-Arid Steppe Ecosystems: A Case Study of Naâma Province, Algeria. Journal of Agriculture and Environment for International Development (JAEID), 120(1), 37–64. https://doi.org/10.36253/jaeid-19875

Copyright (c) 2026 Abderrahmane Mebarki, Tayeb Sitayeb

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

This research evaluates the ecological restoration suitability in the northeastern region of Naâma Province, Algeria, a fragile environment undergoing increasing degradation from both climatic stresses and human activities. The study area, including the municipalities of El Biodh and Mecheria, is located in a semi-arid steppe zone, known for its biodiversity and restoration potential. The objective of this study is to produce a spatially explicit map of restoration suitability by integrating fuzzy logic, GIS-based multi-criteria analysis, and environmental variables. A total of 20 indicators (topographic, pedological, climatic, and vegetative) were normalized and combined using fuzzy membership functions. The fuzzy overlay was followed by a defuzzification step using the centroid method to produce a composite suitability map, which was categorized into three classes: suitable, less suitable, and unsuitable. The results showed a heterogeneous spatial distribution, with 47.65% of the landscape classified as unsuitable, 36.08% as less suitable, and only 16.27% as suitable. For model validation, 300 random points were generated, and multiple linear regression was applied to evaluate the influence of each variable group. Climatic variables showed the strongest correlation with suitability (R = 0.924, p < 0.001), followed by proximity factors (R = 0.719, p < 0.005), topography (R = 0.647, p < 0.001), soil properties (R = 0.521, p < 0.001), and vegetation indices (R = 0.337, p < 0.001). The study confirms the effectiveness of combining fuzzy logic and GIS for ecological restoration planning and highlights priority areas for intervention within arid and semi-arid landscapes.

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