Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm
DOI:
https://doi.org/10.2478/foecol-2022-0010Keywords:
artificial intelligence, non-linear regression, over bark tree bole diameters, Pinus brutiaAbstract
The difficulty of locating and measuring the over-bark tree bole diameters at heights that are far from the ground, is a serious problem in ground-truth data measurements in the field. This problem could be addressed through the application of intelligent systems methods. The paper explores the possibility of applying the Random Forest regression method (RFr) in order to assess, as accurately as possible, the size of the tree bole diameters at any height above the ground, considering data that can be easily measured in the field. For this purpose, diameter measurements of pine trees (Pinus brutia Ten.) from the Seich–Sou urban forest of Thessaloniki, Greece, were used. The effectiveness of the Random Forest regression technique is compared with the results of non-linear regression models that fitted to the available data and evaluated. This research has shown that the RFr method can be a reliable alternative methodology in order to receive accurate information provided by the model, saving time and effort in field.
References
Algera, M., Kätsch, C., Chirwa, P.W., 2019. Developing a taper model for the Pinus elliottii x P. caribaea var. hondurensis hybrid in South Africa. Southern Forests: a Journal of Forest Science, 81: 141–150. https://doi.org/10.2989/20702620.2018.1520027
Arkes, J., 2019. Regression analysis. A practical introduction. London: Routledge. 362 p. https://doi.org/10.4324/9781351011099
Avery, T.E., Burkhart, H.E., 2002. Forest measurements. New York, USA: Mc Graw Hill. 456 p.
Barna, M., Ferezliev, A., Tsakov, H., Mihál, I., 2020. Investigations of mature Scots pine stands in windthrow areas in Norway spruce forests in Western Rhodopes. Folia Oecologica, 47: 1–9. doi: https://doi.org/10.2478/foecol-2020-0001
Bayat, M., Bettinger, P., Heidari, S., Henareh Khalyani, A., Jourgholami, M., Hamidi, S.K, 2020. Estimation of tree heights in an uneven-aged, mixed forest in northern Iran using artificial intelligence and empirical models. Forests, 11 (3): 324. https://doi.org/10.3390/f11030324
Bi, H., Hamilton, F., 1998. Stem volume equations for native tree species in southern New South Wales and Victoria. Australian Forestry, 61 (4): 275–286. https://doi.org/10.1080/00049158.1998.10674752
Boroughani, M., Soltani, S., Ghezelseflu, N., Pazhouhan, I., 2022. A comparative assessment between artificial neural network, neuro-fuzzy, and support vector machine models in splash erosion modelling under simulation circumstances. Folia Oecologica, 49 (1): 23–34. https://doi.org/10.2478/foecol-2022-0003
Breiman, L., 2001. Random forests. Machine Learning, 45: 5–32. http://dx.doi.org/10.1023/A:1010933404324 https://doi.org/10.1023/A:1010933404324
Cutler, A., Cutler, D.R., Stevens, J.R., 2012. Random forests. In Zhang, C., Ma, Y.Q. (eds). Ensemble machine learning. New York: Springer, 2015, p. 157–175. http://dx.doi.org/10.1007/978-1-4419-9326-7_5 https://doi.org/10.1007/978-1-4419-9326-7_5
Diamantopoulou, M.J., 2005. Artificial neural networks as an alternative tool in pine bark volume estimation. Computers and Electronics in Agriculture, 48: 235–244. https://doi.org/10.1016/j.compag.2005.04.002
Diamantopoulou, M.J., Milios, E., Doganos, D., Bistinas, I., 2009. Artificial neural network modeling for reforestation design through the dominant trees bolevolume estimation. Natural Resource Modeling, 22: 511–543. https://doi.org/10.1111/j.1939-7445.2009.00051.x
Diamantopoulou, M.J., Özçelik, R., Yavuz, H., 2018. Treebark volume prediction via machine learning: a case study based on black alder’s tree-bark production. Computers and Electronics in Agriculture, 151: 431–440. https://doi.org/10.1016/j.compag.2018.06.039
Draper, N.R., Smith, H., 1998. Applied regression analysis. New York: Wiley. 706 p. https://doi.org/10.1002/9781118625590
Furnival, G., 1961. An index for comparing equations used in constructing volume tables. Forest Science, 7: 337–341.
Güner, S.T., Diamantopoulou, M.J., Poudel, K.P., Çömez, A., Özçelik, R., 2022. Employing artificial neural network for effective biomass prediction: an alternative approach. Computers and Electronics in Agriculture, 192: 106596. https://doi.org/10.1016/j.compag.2021.106596
Hastie, T., Tibshirani, R., Friedman, J., 2017. The elements of statistical learning data mining, inference, and prediction. 2nd ed. New York: Springer. 764 p.
Hoaglin, D.C., Mosteller, F., Tukey, J.W., 2006. Exploring data tables, trends and shapes. New York: John Wiley and Sons Inc. 538 p. https://doi.org/10.1002/9781118150702
Hirsch, R.P., 1991. Validation samples. Biometrics, 47: 1193–1194. [cit. 2022-01-09]. http://www.jstor.org/stable/2532672
IBM SPSS Corp., released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.
Leite, H.G., Da Silva, M.L.M., Binoti, D.H.B., Fardin, L., Takizawa, F.H., 2011. Estimation of inside-bark diameter and heartwood diameter for Tectona grandis Linn. trees using artificial neural networks. European Journal of Forest Research, 130: 263–269. https://doi.org/10.1007/s10342-010-0427-7
Lilliefors, H.W., 1967. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association 62: 399–402. https://doi.org/10.2307/2283970
Koulelis, P.P., Ioannidis, K., 2021. Constructing single-entry stem volume models for four economically important tree species of Greece. Folia Oecologica, 48 (2): 136–146. https://doi.org/10.2478/foecol-2021-0014
Marchi, M., Scotti, R., Rinaldini, G., Cantiani, P., 2020. Taper function for Pinus nigra in central Italy: Is a more complex computational system required? Forests, 11: 405. https://doi.org/10.3390/f11040405
Matis, K.G., 2004. Forest biometry: II. Dendrometry. 2nd ed. Thessaloniki, Greece: Pegasus. 674 p. (In Greek).
Meng, C.H., Tsai, W.Y., 1986. Selection of weights for a weighted regression of tree volume. Canadian Journal of Forest Research, 16 (3): 671–673. https://doi.org/10.1139/x86-118
Moore, J.A., Zhang, L., Stuck, D., 1996. Height-diameter equations for ten tree species in the Inland Northwest. Western Journal of Applied Forestry, 11: 132–137. https://doi.org/10.1093/wjaf/11.4.132
Nanos, N., Montero, G., 2002. Spatial prediction of diameter distribution models. Forest Ecology and Management, 161: 147–158. https://doi.org/10.1016/S0378-1127(01)00498-4
Nicoletti, M.F., Carvalho, S.D.P.C., Do Amaral Machado, S., Costa, V.J., Silva, C.A., Topanotti, L.R., 2020. Bivariate and generalized models for taper stem representation and assortments production of loblolly pine (Pinus taeda L.). Journal of Environmental Management, 270:110865. https://doi.org/10.1016/j.jenvman.2020.110865
Nunes, M.H., Görgens, E.B., 2016. Artificial intelligence procedures for tree taper estimation within a complex vegetation mosaic in Brazil. PLoS ONE, 11: e0154738. https://doi.org/10.1371/journal.pone.0154738
Özçelik, R., Karatepe, Y., Gürlevik, N., Cañellas, I., Crecente-Campo, F., 2016. Development of ecoregion-based merchantable volume systems for Pinus brutia Ten. and Pinus nigra Arnold. in southern Turkey. Journal of Forestry Research, 27: 101–117. https://doi.org/10.1007/s11676-015-0147-4
Özçelik, R., Diamantopoulou, M.J., Trincado, G., 2019. Evaluation of potential modeling approaches for Scots pine stem diameter prediction in north-eastern Turkey. Computers and Electronics in Agriculture, 162: 773–782. https://doi.org/10.1016/j.compag.2019.05.033
Özçelik, R., Alkan, O., 2020. Fitting and calibrating a mixed-effects segmented taper model. Cerne, 26: 464–473. https://doi.org/10.1590/01047760202026032737
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12: 2825–2830.
Philip, M.S., 1994. Measuring trees and forests. 2nd ed. Wallingford, UK: CAB International. 310 p. https://doi.org/10.1079/9780851988832.0000
Prasad, A.M., Iverson, L.R., Liaw, A., 2006. Newer classification and regression techniques: bagging and random forests for ecological prediction. Ecosystems, 9: 181–199. https://doi.org/10.1007/s10021-005-0054-1
Python Software Foundation, 2022. Python 3.9. https://docs.python.org/3.9/index.html
Ratkowsky, D.A., 1990. Handbook of nonlinear regression models. Statistics: Textbooks and Monographs, vol. 107. New York: Marcel Dekker Inc. 241 p.
Salekin, S., Catalan, C.H., Boczniewicz, D., Phiri, D., Morgenroth, J., Meason, D.F., Mason, E.G., 2021. Global tree taper modelling: a review of applications, methods, functions, and their parameters. Forests, 12: 913. https://doi.org/10.3390/f12070913
Segal, M.R., 2003. Machine learning benchmarks and random forest regression. San Francisco: Center for Bioinformatics and Molecular Biostatistics, University of California. [cit.2022-01-09]. https://escholarship.org/uc/item/35x3v9t4
Soares, F.A.A.M.N., Flôres, E.L., Cabacinha, C.D., Carrijo, G.A., Veiga, A.C.P., 2011. Recursive diameter prediction and volume calculation of Eucalyptus trees using multilayer perceptron networks. Computers and Electronics in Agriculture, 78: 19–27. https://doi.org/10.1016/j.compag.2011.05.008
Trincado, G., Burchart, H.E., 2006. A generalized approach for modeling and localizing stem profile curves. Forest Science, 52: 670–682.
Van Laar, A., Akça, A., 2007. Forest mensuration. Dordrecht, The Netherlands: Springer. 383 p. https://doi.org/10.1007/978-1-4020-5991-9
Van Rossum, G., Drake, F.L., 2011. The Python language reference manual. Network Theory Ltd. 150 p.
West, P.W., 2009. Tree and forest measurement. 2nd ed. Berlin: Springer-Verlag. 191 p. https://doi.org/10.1007/978-3-540-95966-3
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