hrcak mascot   Srce   HID

Šumarski list, Vol. 141 No. 3-4, 2017.

Izvorni znanstveni članak

Development of an ensemble classifier with data from description sheets to classify forest stands in site qualities

Kyriaki Kitikidou   ORCID icon
Elias Milios
Panagiota Palavouzi

Puni tekst: engleski, pdf (655 KB) str. 131-137 preuzimanja: 96* citiraj
APA 6th Edition
Kitikidou, K., Milios, E. i Palavouzi, P. (2017). Development of an ensemble classifier with data from description sheets to classify forest stands in site qualities. Šumarski list, 141 (3-4), 131-137.
MLA 8th Edition
Kitikidou, Kyriaki, et al. "Development of an ensemble classifier with data from description sheets to classify forest stands in site qualities." Šumarski list, vol. 141, br. 3-4, 2017, str. 131-137. Citirano 27.05.2019.
Chicago 17th Edition
Kitikidou, Kyriaki, Elias Milios i Panagiota Palavouzi. "Development of an ensemble classifier with data from description sheets to classify forest stands in site qualities." Šumarski list 141, br. 3-4 (2017): 131-137.
Kitikidou, K., Milios, E., i Palavouzi, P. (2017). 'Development of an ensemble classifier with data from description sheets to classify forest stands in site qualities', Šumarski list, 141(3-4), str. 131-137. doi:
Kitikidou K, Milios E, Palavouzi P. Development of an ensemble classifier with data from description sheets to classify forest stands in site qualities. Šumarski list [Internet]. 2017 [pristupljeno 27.05.2019.];141(3-4):131-137. doi:
K. Kitikidou, E. Milios i P. Palavouzi, "Development of an ensemble classifier with data from description sheets to classify forest stands in site qualities", Šumarski list, vol.141, br. 3-4, str. 131-137, 2017. [Online]. doi:

Rad u XML formatu

Aim of study: In this work, we tested the technique of combining the predictions of classifiers for the development of a single, ensemble classifier, in order to classify forest stands in site qualities. Area of study: We used data of the forest stands of Dadia – Lefkimi – Soufli forest (north-eastern Greece). Materials and methods: The variables that we used as input were the altitude, slope, age and canopy density. For the ensemble classifier development we applied the boosting algorithm. Main results: The canopy density was the most important predictor; topography which replaced altitudes and slopes was the second important predictor, while the developed ensemble classifier gave a percentage of correct classification up to 98.59% (for the worst site quality). Research essentials: If we consider that the initial site classification comprised over 70% of the Dadia-Lefkimi –Soufli forest area in the worst site quality, then the usage of boosting method for creating a collective classifier for site qualities in the studied forest can be characterized as fully successful. The application of this method using these input parameters do not need background information regarding the tree age and (or) other difficult to access information. Moreover, in a quite high degree, this site classification is not influenced by disturbances. The boosting method for creating a collective classifier for site qualities obviously will give far more accurate classifications of site productivity, if a more sophisticated scheme of data collection is used.

Ključne riječi
ensemble classifiers; forest stands; site qualities

Hrčak ID: 181410




Adamopoulos S, Milios E, Doganos D, Bistinas I 2009: Ring width, latewood proportion and dry density in stems of Pinus brutia Ten. European Journal of Wood Products , 67(4), pp: 471-477.


Adamopoulos S, Wimmer R, Milios E 2012: Tracheid length – growth relationships of young Pinus brutia Ten. grown on reforestation sites. IAWA Journal, 33(1), pp: 39-49.


Aertsen W, Kint V, Von Wilpert K, Zirlewagen D, Muys B, Van Orshoven J 2012: Comparison of location-based, attribute-based and hybrid regionalization techniques for mapping forest site productivity. Forestry , 85(4), pp: 539-550.


Anderson R, Rubin H 1956: Statistical inference in factor analysis. Proceedings of the Third Berkeley Symposium of Mathematical Statistics and Probability , 5, pp: 111-150.


Barnes B, Zak D, Denton S, Spurr S 1998: Forest Ecology. 4th ed. New York: John Willey & Sons Inc.. pp: 792


Bontemps J, Bouriaud O 2014: Predictive approaches to forest site productivity: Recent trends, challenges and future perspectives. Forestry , 87(1), pp: 109-128.


Bravo F, Montero G 2001: Site index estimation in Scots pine (Pinus sylvestris L.) stands in the High Ebro Basin (northern Spain) using soil attributes. Forestry , 74(4), pp: 395-406.


Breiman L 1996: Bagging predictors. Machine Learning , 24(2), pp: 123-140.


Clemen R 1989: Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), pp: 559-583.


Clutter J, Fortson J, Pienaar L, Brister G, Bailey R 1983: Timber management: a quantitative approach. 1st ed. Wiley, New York. 333 PP.


Consorzio Forestale Del Ticino 2005: Study of protection and management of the public forest Dadia - Lefkimi - Soufli, for the period 2005-2014..


Coops N, Waring R, Landsberg J 1998: Assessing forest productivity in Australia and New Zealand using a physiologically-based model driven with averaged monthly weather data and satellite-derived estimates of canopy photosynthetic capacity. Forest Ecology and Management , 104(1-3), pp: 113-127.


Dafis S 1986: Forest Ecology (in Greek). 1st ed. Thessaloniki: Giahoudi Giapouli.


Dafis S 1992: Applied silviculture (in Greek). 1st ed. Giahoudi Giapouli, Thessaloniki, Greece.


Drucker H 1997: Improving regressor using boosting techniques. In: Proceedings of the 14th International Conferences on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp: 107-115.


Freund Y, Schapire R 1997: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), pp: 119-139.


Hatzistathis A, Dafis S 1989: Reforestations – Forest Nurseries. (in Greek). 1st ed. Thessaloniki: Giahoudi Giapouli.


Huang S 1997: Development of compatible height and site index models for young and mature stands within an ecosystem-based management framework. In: Empirical and process based models for forest tree and stand growth simulation; Amaro A, Tomé M (eds). pp: 61-98. Lisboa,


IBM Corp. 2012: : IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp


Kayahara G, Carter R, Klinka K 1995: Site index of western hemlock (Tsuga heterophylla) in relation to soil nutrient and foliar chemical measures. Forest Ecology and Management, 74(1-3), pp: 161-169.


Krumland B, Eng H 2005: Site index systems for major young-growth forest and woodland species in northern California (No. 4). California Department of Forestry & Fire Protection..


Laamrani A, Valeria O, Bergeron Y, Fenton N, Cheng L, Anyomi K 2014: Effects of topography and thickness of organic layer on productivity of black spruce boreal forests of the canadian clay belt region. Forest Ecology and Management, 330, pp: 144-157.


Laubhann D, Sterba H, Reinds G, De Vries W 2009: The impact of atmospheric deposition and climate on forest growth in European monitoring plots: An individual tree growth model. Forest Ecology and Management, 258(8), pp: 1751-1761.


McKenney D, Pedlar J 2003: Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. Forest Ecology and Management, 175(1-3), pp: 497-507.


Milios E, Pipinis E, Petrou P, Akritidou S, Kitikidou K, Smiris P 2012: The Influence of Position and Site on the Height Growth of Young Populus tremula L. Ramets in Low Elevation Formations in Northeastern Greece. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 40(2), pp: 302-307.


Oliver C, Larson B 1996: Forest Stand Dynamics. New York: John Willey & Sons Inc.. 509 pp.


Opitz D, Shavlik J 1996: Actively Searching for an Effective Neural Network Ensemble. Connection Science, 8(3-4), pp: 337-354.


Papalexandris C, Milios E 2010: Analysis of natural Fagus sylvatica L. s.l. regeneration in low elevation stands located in the central part of Evros region in the Νortheast of Greece. Is sprout origin regeneration significant for the species maintenance? . Plant Biosystems, 144(4), pp: 784-792.


Perrone M 1993: Improving regression estimation: averaging methods for variance reduction with extension to general convex measure optimization. Ph.D. thesis, Brown University, Providence RI. 509 pp.


Pokharel B, Dech P 2011: An ecological land classification approach to modelling the production of forest biomass. The Forestry Chronicle , 87(1), pp: 23-32.


Pinno B, Paré D, Guindon L, Bélanger N 2009: Predicting productivity of trembling aspen in the Boreal Shield ecozone of Quebec using different sources of soil and site information. Forest Ecology and Management , 289(3), pp: 782-783.


Raulier F, Lambert M, Pothier D, Ung C 2003: Impact of dominant tree dynamics on site index curves. Forest Ecology and Management , 184(1), pp: 65-78.


Rokach L 2009: Ensemble-based classifiers. Artificial Intelligence Review , 33(1-2), pp: 1-39.


Skovsgaard J, Vanclay J 2013: Forest site productivity: A review of spatial and temporal variability in natural site conditions. Forestry , 86(3), pp: 305-315.


Skovsgaard J, Vanclay J 2008: Forest site productivity: a review of the evolution of dendrometric concepts for evenaged stands. Forestry , 81(1), pp: 13-31.


Smith D, Larson B, Kelty M, Ashton P, Mark S 1997: The practice of silviculture. Applied Forest Ecology. New York: John Willey & Sons Inc.. 560 pp.


Stampoulidis A, Milios E, Kitikidou K 2013: The regeneration of pure Juniperus excelsa Bieb. Stands in Pespa National Park in Greece. Šumarski list , 137(3-4), pp: 163-172.


UNESCO World Heritage Centre 2012-2015. 2015:


Van Laar A, Akca A 2007: Forest mensuration (Vol. 13). . Springer Science & Business Media. 385 pp.


Vanclay J 1992: Assessing site productivity in tropical moist forests: a review. Forest Ecology and Management, 54(1-4), pp: 257-287.


Watt M, Dash J, Bhandari S, Watt P 2015: Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning. Forest Ecology and Management, 357(1), pp: 1-9.


Wolpert D, Dash J, Bhandari S, Watt P 1992: Stacked generalization. Neural Networks, 5(2), pp: 241-259.


Posjeta: 198 *