Predicting Cork Oak Suitability in Maâmora Forest Using Random Forest Algorithm
Author(s)
Said Lahssini1,
Hicham Lahlaoi2,
Hicham Mharzi Alaoui3,
El Aid Hlal1,
Martino Bagaram1,
Quentin Ponette4
Affiliation(s)
1 National School of Forestry Engineering, Salé, Morocco. 2 Geosciences Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco. 3 Institut of Agronomy and Veterinary Hassan II, Rabat, Morocco. 4 Catholic University of Louvain, Louvain la Neuve, Belgium.
1 National School of Forestry Engineering, Salé, Morocco. 2 Geosciences Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco. 3 Institut of Agronomy and Veterinary Hassan II, Rabat, Morocco. 4 Catholic University of Louvain, Louvain la Neuve, Belgium.
ABSTRACT
Maamora is considered the most important cork-oak forest in the world with regard to surface. Therefore, anthropic pressure, including cork harvesting, grazing and soft acorn picking up by local communities, has harmful consequences on forest regeneration and the forest become older exceeding harvesting age. Thus, its sustainability depends on the managers’ ability to succeed cork oak plantations. This work presents an assessment approach to evaluate Quercus suber suitability to its plantation which is based on a random forest algorithm (RF). In fact, this suitability has been assessed through analyzing management data related to previous plantation success rates (SR). Then a relationship between SR and a set of environmental and social factors has been investigated using the RF. Application of the fitted model to continuous maps of all involved factors enabled establishment of suitability maps which would help managers to make more rational decisions in terms of cork oak regeneration, ensuring Maamora forest sustainability.
Maamora is considered the most important cork-oak forest in the world with regard to surface. Therefore, anthropic pressure, including cork harvesting, grazing and soft acorn picking up by local communities, has harmful consequences on forest regeneration and the forest become older exceeding harvesting age. Thus, its sustainability depends on the managers’ ability to succeed cork oak plantations. This work presents an assessment approach to evaluate Quercus suber suitability to its plantation which is based on a random forest algorithm (RF). In fact, this suitability has been assessed through analyzing management data related to previous plantation success rates (SR). Then a relationship between SR and a set of environmental and social factors has been investigated using the RF. Application of the fitted model to continuous maps of all involved factors enabled establishment of suitability maps which would help managers to make more rational decisions in terms of cork oak regeneration, ensuring Maamora forest sustainability.
Cite this paper
Lahssini, S. , Lahlaoi, H. , Alaoui, H. , Hlal, E. , Bagaram, M. and Ponette, Q. (2015) Predicting Cork Oak Suitability in Maâmora Forest Using Random Forest Algorithm. Journal of Geographic Information System, 7, 202-210. doi: 10.4236/jgis.2015.72017.
Lahssini, S. , Lahlaoi, H. , Alaoui, H. , Hlal, E. , Bagaram, M. and Ponette, Q. (2015) Predicting Cork Oak Suitability in Maâmora Forest Using Random Forest Algorithm. Journal of Geographic Information System, 7, 202-210. doi: 10.4236/jgis.2015.72017.
References
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http://dx.doi.org/10.1016/s1364-8152(99)00029-8 [10] Benito Garzón, M.B., Blazek, R., Neteler, M., Dios, R.S. de, Ollero, H.S. and Furlanello, C. (2006) Predicting Habitat Suitability with Machine Learning Models: The Potential Area of Pinus sylvestris L. in the Iberian Peninsula. Ecological Modelling, 197, 383-393.
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[1] Hammoudi, A. (2002) La Subéraie: Biodiversité et Paysage. Viveexpo 2002. [2] HCEFLCD (2011) Procès-verbal d’Aménagement de la Forêt de la Maamora (Version Prel.), HCEFLCD-Rabat, Maroc. [3] Marion, J. (1951) La Régénération Naturelle du Chêne-liège en Maamora. Annales de la recherche forestière au Maroc, 25-57. [4] Belghazi, B., Badouzi, M., Belghazi, T. and Moujjani, S. (2013) Semis et Plantations dans la Forêt de Chêne-liège de la Maamora (Maroc). Forêt Méditerranéenne, XXXII, 301-314. [5] Lepoutre, B. (1965) Régénération Artificielle du Chêne-liège et Equilibre Climacique de la Subéraie en Forêt de la Maamora. Annales de la recherche forestière au Maroc, 9, 1-188. [6] Collins, M.G., Steiner, F.R. and Rushman, M.J. (2001) Land-Use Suitability Analysis in the United States: Historical Development and Promising Technological Achievements. Environmental Management, 28, 611-621. http://dx.doi.org/10.1007/s002670010247 [7] Vincenzi, S., Zucchetta, M., Franzoi, P., Pellizzato, M., Pranovi, F., De Leo, G.A. and Torricelli, P. (2011) Application of a Random Forest algorithm to Predict Spatial Distribution of the Potential Yield of Ruditapes philippinarum in the Venice Lagoon, Italy. Ecological Modelling, 222, 1471-1478.
http://dx.doi.org/10.1016/j.ecolmodel.2011.02.007 [8] Santos, X., Brito, J.C., Sillero, N., Pleguezuelos, J.M., Llorente, G.A., Fahd, S. and Parellada, X. (2006) Inferring Habitat-Suitability Areas with Ecological Modelling Techniques and GIS: A Contribution to Assess the Conservation Status of Vipera latastei. Biological Conservation, 130, 416-425.
http://dx.doi.org/10.1016/j.biocon.2006.01.003 [9] Ortigosa, G.R., De Leo, G.A. and Gatto, M. (2000) VVF: Integrating Modelling and GIS in a Software Tool for Habitat Suitability Assessment. Environmental Modelling & Software, 15, 1-12.
http://dx.doi.org/10.1016/s1364-8152(99)00029-8 [10] Benito Garzón, M.B., Blazek, R., Neteler, M., Dios, R.S. de, Ollero, H.S. and Furlanello, C. (2006) Predicting Habitat Suitability with Machine Learning Models: The Potential Area of Pinus sylvestris L. in the Iberian Peninsula. Ecological Modelling, 197, 383-393.
http://dx.doi.org/10.1016/j.ecolmodel.2006.03.015 [11] Recknagel, F. (2001) Applications of Machine Learning to Ecological Modelling. Ecological Modelling, 146, 303-310. http://dx.doi.org/10.1016/S0304-3800(01)00316-7 [12] Olden, J.D., Lawler, J.J. and Poff, N.L. (2008) Machine Learning Methods without Tears: A Primer for Ecologists. The Quarterly Review of Biology, 83, 171-193. http://dx.doi.org/10.1086/587826 [13] Wen, L., Ling, J., Saintilan, N. and Rogers, K. (2009) An Investigation of the Hydrological Requirements of River Red Gum (Eucalyptus camaldulensis) Forest, Using Classification and Regression Tree Modelling. Ecohydrology, 2, 143-155. http://dx.doi.org/10.1002/eco.46 [14] Park, Y.-S., Céréghino, R., Compin, A. and Lek, S. (2003) Applications of Artificial Neural Networks for Patterning and Predicting Aquatic Insect Species Richness in Running Waters. Ecological Modelling, 160, 265-280. http://dx.doi.org/10.1016/S0304-3800(02)00258-2 [15] Termansen, M., McClean, C.J. and Preston, C.D. (2006) The Use of Genetic Algorithms and Bayesian Classification to Model Species Distributions. Ecological Modelling, 192, 410-424.
http://dx.doi.org/10.1016/j.ecolmodel.2005.07.009 [16] Holzkamper, A., Lausch, A. and Seppelt, R. (2006) Optimizing Landscape Configuration to Enhance Habitat Suitability for Species with Contrasting Habitat Requirements. Ecological Modelling, 198, 277-292. http://dx.doi.org/10.1016/j.ecolmodel.2006.05.001 [17] Garzón, M.B., de Dios, R.S. and Ollero, H.S. (2008) Effects of Climate Change on the Distribution of Iberian Tree Species. Applied Vegetation Science, 11, 169-178. http://dx.doi.org/10.3170/2008-7-18348 [18] Prasad, A.M., Iverson, L.R. and Liaw, A. (2006) Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems, 9, 181-199.
http://dx.doi.org/10.1007/s10021-005-0054-1 [19] Cutler, D.R., Edwards Jr., T.C., Beard, K.H. Cutler, A., Hess, K.T., Gibson, J. and Lawler, J.J. (2007) Random Forests for Classification in Ecology. Ecology, 88, 2783-2792. http://dx.doi.org/10.1890/07-0539.1 [20] Croituru, L. and Liagre, L. (2013) Contribution des Forets à une Economie Verte dans la Région MENA. Forêt Méditerranéenne, 34, 283-291. [21] Heusch, B. and Billaux, P. (1966) Carte pédologique du Gharb, de la Maamora Septentrionale et de leur Bordure Orientale. Congrès de Pédologie Méditérranèerme, INRA, Rabat-Maroc. [22] Bagaram, M. (2014) Elaboration d’une Base de Données Géographiques et d’un Catalogue des Stations de la Subéraie de la Maamora. Mémoire de 3ème cycle, ENFI, Salé-Maroc. [23] Emberger, L. (1955) Une classification Biogéographique des Climats. Recueil des Travaux des Laboratoires de Botanique, Géologie et Zoologie de la Faculté des Sciences de L’Université de Montpellier, Série Botanique, 7, 3-43. [24] Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. (2005) Very High Resolution Interpolated Climate Surfaces for Global Land Areas. International Journal of Climatology, 25, 1965-1978. http://dx.doi.org/10.1002/joc.1276 [25] R Development Core Team (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. [26] Liaw, A. and Wiener, M. (2002) Classification and Regression by Random Forest. R News, 2, 18-22. [27] Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.
http://dx.doi.org/10.1023/A:1010933404324 [28] Landis, J.R. and Koch, G.G. (1977) The Measurement of Observer Agreement for Categorical Data. Biometrics, 33, 159-174. http://dx.doi.org/10.2307/2529310