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Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: case of upper Ziz, southeastern Morocco

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Abstract

The selection of appropriate areas for reforestation remains a complex task because of influence by several factors, which requires the use of new techniques. Based on the accurate outcomes obtained through machine learning in prior investigations, the current study evaluates the capacities of six machine learning techniques (MLT) for delineating optimal areas for reforestation purposes specifically targeting Quercus ilex, an important local species to protect soil and water in upper Ziz, southeast Morocco. In the initial phase, the remaining stands of Q. ilex were identified, and at each site, measurements were taken for a set of 12 geo-environmental parameters including slope, aspect, elevation, geology, distance to stream, rainfall, slope length, plan curvature, profile curvature, erodibility, soil erosion, and land use/land cover. Subsequently, six machine learning algorithms were applied to model optimal areas for reforestation. In terms of models’ performance, the results were compared, and the best were obtained by Bagging (area under the curve (AUC) = 0.98) and Naive Bayes (AUC = 0.97). Extremely favorable areas represent 8% and 17% of the study area according to Bagging and NB respectively, located to the west where geological unit of Bathonian-Bajocian with low erodibility index (K) and where rainfall varies between 250 and 300 mm/year. This work provides a roadmap for decision-makers to increase the chances of successful reforestation at lower cost and in less time.

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Data availability

The data used to support the results of this research are available from the corresponding author upon request.

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Acknowledgements

The authors are very grateful to the Guir-Ziz-Rhris watershed agency for the facilities and data provided during this study. The authors would like to thank Mr. N. Khan for his valuable help during the validation step.

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Authors and Affiliations

  1. Department of Geography, Faculty of Humanities and Social Sciences, University of Ibn Tofail, 14000, Kenitra, Morocco

    Mohamed Manaouch, Mohcine Batchi & Jamal Al Karkouri

  2. Department of Geology, Faculty of Sciences, University of Ibn Tofail, 14000, Kenitra, Morocco

    Mohamed Sadiki

  3. Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska street 60, 41-200, Sosnowiec, Poland

    Quoc Bao Pham

  4. Quality Control of Silvicultural Operations, Rexforêt, Quebec, Canada

    Anis Zouagui

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  1. Mohamed Manaouch
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Contributions

Conceptualization: Mohamed Manaouch; methodology and software processing: Anis Zouagui; data curation and writing—original draft: Mohamed Manaouch; visualization and investigation: Mohcine Batchi; supervision and validation: Mohamed Sadiki and Jamal Al Karkouri; writing—review and editing: Quoc Bao Pham.

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Correspondence to Mohamed Manaouch.

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Manaouch, M., Sadiki, M., Pham, Q.B. et al. Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: case of upper Ziz, southeastern Morocco. Environ Monit Assess 195, 1094 (2023). https://doi.org/10.1007/s10661-023-11680-1

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