Skip to main content

Machine Learning Approaches for Predicting Tree Growth Trends Based on Basal Area Increment

  • Conference paper
  • First Online:
18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Abstract

This paper explores the potential of machine learning in predicting Basal Area Increment (BAI) for the species Abies spectabilis, a commonly used metric for measuring tree growth. Machine learning algorithms are used to analyze environmental factors, biotic responses, growth, and their interactions to obtain accurate predictions of BAI under different climatic scenarios. The study aims to identify vulnerable tree at risk of dieback due to changes in climate or other environmental factors. The methodology includes data acquisition, preprocessing, feature selection, model development, and evaluation. The results reported in the study show M5’ performs better in predicting BAI than other machine learning models. The study’s findings can help in forest management and conservation decisions, such as selective harvesting, reforestation, carbon sequestration, and biodiversity conservation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Climate explorer. https://climexp.knmi.nl/start.cgi, (Accessed 10 May 2023)

  2. World data service for paleoclimatology. https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring, (Accessed 10 May 2023)

  3. Bončina, A., Klopčič, M., Trifković, V., Ficko, A., Simončič, P.: Tree and stand growth differ among soil classes in semi-natural forests in central europe. CATENA 222, 106854 (2023)

    Article  Google Scholar 

  4. Camarero, J.J., Gazol, A., Sangüesa-Barreda, G., Oliva, J., Vicente-Serrano, S.M.: To die or not to die: early warnings of tree dieback in response to a severe drought. J. Ecol. 103(1), 44–57 (2015)

    Article  Google Scholar 

  5. Casas-Gómez, P., Sánchez-Salguero, R., Ribera, P., Linares, J.C.: Contrasting signals of the westerly index and north atlantic oscillation over the drought sensitivity of tree-ring chronologies from the mediterranean basin. Atmosphere 11(6) (2020)

    Google Scholar 

  6. Fu, L., et al.: A basal area increment-based approach of site productivity evaluation for multi-aged and mixed forests. Forests 8(4) (2017)

    Google Scholar 

  7. Hasenauer, H., Merkl, D., Weingartner, M.: Estimating tree mortality of norway spruce stands with neural networks. Adv. Environ. Res. 5(4), 405–414 (2001), international Symposium on Environmental Software Systems (ISESS 2000)

    Google Scholar 

  8. Jevšenak, J., Levanič, T.: Should artificial neural networks replace linear models in tree ring based climate reconstructions? Dendrochronologia 40, 102–109 (2016)

    Article  Google Scholar 

  9. Jevšenak, J., Skudnik, M.: A random forest model for basal area increment predictions from national forest inventory data. For. Ecol. Manage. 479, 118601 (2021)

    Article  Google Scholar 

  10. Liu, Z., Peng, C., Work, T., Candau, J.N., DesRochers, A., Kneeshaw, D.: Application of machine-learning methods in forest ecology: recent progress and future challenges. Environ. Rev. 26(4), 339–350 (2018). https://doi.org/10.1139/er-2018-0034

    Article  Google Scholar 

  11. Miguel, E.P., et al.: Artificial intelligence tools in predicting the volume of trees within a forest stand. African J. Agricult. Res. 11(21), 1914–1923 (2016)

    Article  Google Scholar 

  12. Navarro-Cerrillo, R.M., et al.: Is thinning an alternative when trees could die in response to drought? the case of planted pinus nigra and p. sylvestris stands in southern spain. For. Ecol. Manag. 433, 313–324 (2019)

    Google Scholar 

  13. Pretzsch, H.: The emergent past: past natural and human disturbances of trees can reduce their present resistance to drought stress. Euro. J. For. Res. 141, 87–104 (2022). https://link.springer.com/article/10.1007/s10342-021-01422-8

  14. Vospernik, S.: Basal area increment models accounting for climate and mixture for austrian tree species. For. Ecol. Manage. 480, 118725 (2021)

    Article  Google Scholar 

  15. Özçelık, R., Diamantopoulou, M.J., Eker, M., Gürlevık, N.: Artificial neural network models: An alternative approach for reliable aboveground pine tree biomass prediction. For. Sci. 63(3), 291–302 (2017). https://doi.org/10.5849/FS-16-006

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Spanish Ministry of Science and Innovation and the Junta de Andalucía for their support within the projects PID2020-117954RB-C21 and TED2021-131311B-C22, PY20-00870, UPO-138516, respectively. The authors would also like to thank the Fundación Tatiana Pérez de Guzmán el Buenofor the support offered through the Beca Predoctoral en Medioambiente de 2018.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pablo Casas-Gómez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Casas-Gómez, P., Martínez-Álvarez, F., Troncoso, A., Linares, J.C. (2023). Machine Learning Approaches for Predicting Tree Growth Trends Based on Basal Area Increment. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_22

Download citation

Publish with us

Policies and ethics