Bulletin of Forestry Science / Volume 8 / Issue 1 / Pages 93-103
previous article | next article

Machine learning approximation of Járó-table (table of applicable targeted forest stands and their growth for each forest site)

Kornél Czimber, Csaba Mátyás, András Bidló & Borbála Gálos


Correspondence: Czimber Kornél

Postal address: H-9400 Sopron, Bajcsy-Zsilinszky u. 4.

e-mail: czimber.kornel[at]uni-sopron.hu


In this article, we would like to present a machine learning algorithm that processes the data of Járó’s target stands and their growth for each forest site variation. The method is able to propose stand types and growths on the basis of existing data for new variations due to climate change and for a newly entering forest climate zone. The essence of this process is to place the entries of the Járó’s table in a five-dimensional space, and use distance kernels to select the closest target stand types and weight their growth rate. It defines for a specific forest site, which target stands are likely to be in the area and what kind of growth can be characterized. The results will be incorporated into the decision support system of the Agrárklima project after proper validation.

Keywords: machine learning, forest site, targeted stand, growth

  • Balogh L., Kovács G. & Tímár G. 2005: Az egyes termőhelytípus-változatokon alkalmazható célállományok. Állami Erdészeti Szolgálat, Budapest.
  • Berki I., Rasztovits E., Móricz N. & Mátyás Cs. 2009: Determination of the drought tolerance limit of beech forests and forecasting their future distribution in Hungary. Cereal Research Communications 37: 613–616. DOI: 10.1556/CRC.37.2009.Suppl.4
  • Czimber K. & Gálos B. 2016: A new decision support system to analyse the impacts of climate change on the Hungarian forestry and agricultural sectors. Scandinavian Journal of Forest Research 31: 664–673. DOI: 10.1080/02827581.2016.1212088
  • Falcão A. & Borges J.G. 2005: Designing decision support tools for Mediterranean forest ecosystems management: a case study in Portugal. Annals of Forest Science 62: 751–760. DOI: 10.1051/forest:2005061
  • Führer E. 2010: A fák növekedése és a klíma. Klíma-21 Füzetek 61: 98–107.
  • Führer E., Horváth L., Jagodics A., Machon A. & Szabados I. 2011: Application of a new aridity index in Hungarian forestry practice. Időjárás 115: 103–118.
  • Gálos B., Führer E., Czimber K., Gulyás K., Bidló A., Hänsler A., et al. 2015: Climatic threats determining future adaptive forest management – a case study of Zala County. Időjárás 119(4): 425–441.
  • Mátyás Cs. & Czimber K. 2000: Zonális erdőtakaró mezoklíma szintű modellezése: lehetőségek a klímaváltozás hatásainak előrejelzésére. In: Tar K. (ed): III. Erdő és Klíma Konferencia. DE-TTK, Debrecen, 83–97.
  • Mátyás Cs. & Czimber K. 2004: A zonális zárt erdőtakaró alsó határának klímaérzékenysége Magyarországon. In: Mátyás Cs. & Vig P. (eds): Erdő és Klíma IV. Nyugat-Magyarországi Egyetem, Sopron, 35–44.
  • Ray D. 2001: Ecological site classification decision support system. Version 1.7. Edinburgh: Forestry Commission.
  • Reynolds K.M., Twery M., Lexer M.J., Vacik H., Ray D., Shao G., et al. 2008: Decision Support Systems in Forest Management. In: Burstein F. & Holsapple C. (eds): Handbook on Decision Support System 2. International Handbooks on Information Systems Series, Springer-Verlag Berlin Heidelberg, 499–534. DOI: 10.1007/978-3-540-48716-6
  • Rosenblatt M. 1956: Remarks on Some Nonparametric Estimates of a Density Function. The Annals of Mathematical Statistics 27(3): 832–837. DOI: 10.1214/aoms/1177728190
  • Silverman B.W. 1986: Density Estimation for Statistics and Data Analysis. Routledge, New York.
  • Vacik H., Lexer M.J., Rammer W., Seidl R., Hochbichler E., Strauss M., et al. 2010: ClimChAlp – a web based decision support system to explore adaptation options for silviculture in secondary Norway spruce forests in Austria. In: Falcao A. & Rosset C. (eds): Proceedings of the Workshop on Decision Support Systems in Sustainable Forest Management, Lisbon.
  • Open Acces

    For non-commercial purposes, let others distribute and copy the article, and include in a collective work, as long as they cite the author(s) and the journal, and provided they do not alter or modify the article.

    Cite this article as:

    Czimber, K., Mátyás, Cs., Bidló, A. & Gálos, B. (2018): Machine learning approximation of Járó-table (table of applicable targeted forest stands and their growth for each forest site). Bulletin of Forestry Science, 8(1): 93-103. (in Hungarian) DOI: 10.17164/EK.2018.006

    Volume 8, Issue 1
    Pages: 93-103

    DOI: 10.17164/EK.2018.006

    First published:
    29 May 2018

    Related content


    More articles
    by this authors


    Related content in the Bulletin of Forestry Science*

    More articles by this authors in the Bulletin of Forestry Science

  • Csáki, P., Kalicz, P., Csóka, G., Brolly, G. B., Czimber, K. & Gribovszki, Z. (2014): Hydrological impacts of different land cover types in the context of climate change for Zala County. Bulletin of Forestry Science, 4(2): 65-76.
  • Molnár, D., Barton, I., Czimber, K., Bazsó, T. & Frank, N. (2016): Investigations on stand structure in the Roth Memorial Forest. Bulletin of Forestry Science, 6(2): 127-136.
  • Gálos, B., Mátyás, Cs. & Jacob, D. (2012): The role of afforestation in mitigating climate change. Bulletin of Forestry Science, 2(1): 35-45.
  • Bordács, S., Nagy, L., Pintér, B., Bach, I., Borovics, A., Kottek, P., Szepesi, A., Fekete, Z., Wisnovszky, K. & Mátyás, Cs. (2013): State of Hungary’s forest genetic resources, 2010-2011. Bulletin of Forestry Science, 3(1): 21-37.
  • Czúcz, B., Gálhidy, L. & Mátyás, Cs. (2013): Present and forecasted distribution of beech and sessile oak at the xeric climatic limits in Central Europe. Bulletin of Forestry Science, 3(1): 39-53.
  • Horváth, A. & Mátyás, Cs. (2014): Estimation of increment decline caused by climate change, based on data of a beech provenance trial. Bulletin of Forestry Science, 4(2): 91-99.
  • Mátyás, Cs. & Kramer, K. (2016): Adaptive management of forests and their genetic resources in the face of climate change. Bulletin of Forestry Science, 6(1): 7-16.
  • Mátyás, Cs. & Borovics, A. (2014): "Agrárklíma". Bulletin of Forestry Science, 4(2): 7-8.
  • Mátyás, Cs. (2018): In the whirl of passing time. Bulletin of Forestry Science, 8(1): 9-10.
  • Mátyás, Cs., Kóczán-Horváth, A., Antoine, K. & Cuauhtémoc, S. (2018): Juvenile height growth response of sessile oak populations to simulated climatic change based on provenance test data. Bulletin of Forestry Science, 8(1): 131-148.
  • Visiné, R. E., Hofmann, T., Albert, L. & Mátyás, Cs. (2018): Antioxidant system as a potential indicator of the climatic adaptation of beech (Fagus sylvatica L.). Bulletin of Forestry Science, 8(2): 25-35.
  • Szűcs, P. & Bidló, A. (2013): Comparison of bryophyte communities in Norway spruce (Picea abies) and beech (Fagus sylvatica) forest stands in Sopron Hills (NW-Hungary). Bulletin of Forestry Science, 3(1): 157-166.
  • Bidló, A., Szűcs, P., Horváth, A., Király, É., Németh, E. & Somogyi, Z. (2014): The effect of afforestations on the carbon stock of soil in Transdanubian Region (Hungary). Bulletin of Forestry Science, 4(2): 121-133.
  • Bidló, A. & Horváth, A. (2018): Role of soils in climate change. Bulletin of Forestry Science, 8(1): 57-71.
  • Gálos, B. & Somogyi, Z. (2017): New climate scenarios – smaller drought risk for European beech?. Bulletin of Forestry Science, 7(2): 85-98.
  • * Automatically generated recommendations based on the occurrence of keywords given by authors in the titles and abstracts of other articles. For more detailed search please use the manual search.