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.
Keywords: Forest restoration; Holm oak; Machine learning; Semi-arid areas; Site suitability.
© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.