Harvesting wind damaged trees: a study of prediction of windthrow damage in mixed-broadleaf stands via a machine learning model

ABSTRACT

Due to climate change, windstorms are becoming increasingly common resulting in the destruction not only of extensive forest areas but, quite often, of small-sized and scattered forest lands, thereby adversely affecting both the productivity and the safety of workers employed in the harvest of windblown trees. In the present study, an attempt is made to identify and record areas in the northeastern forests of Greece covered by mixed stands of conifers and broadleaves that experienced massive windthrow following local storms. Our results reveal that where Pinus sylvestris was mixed either with Quercus sp. or with Fagus sylvatica, it had been substantially protected, while in plots where it stood on its own it had been extensively uprooted. On the other hand, Picea abies, even if it was mixed with Fagus sylvatica and Pinus sylvestris, had been blown down to a large extent. Based on tree-level data, local topographic features, forest characteristics, and the mechanical properties of green wood, a reliable model, to be used for the prediction of similar disturbances in the future, has been selected after a thorough comparative study of the most well-known intelligent Machine Learning (ML) algorithms. Specifically, Random Forest Classifier, k-Neighbors Classifier, Decision Tree Classifier, Light Gradient Boosting Machine, Gradient Boosting Classifier, Ada Boost Classifier, Ridge Classifier, Linear Discriminant Analysis, Logistic Regression, Naive Bayes, SVM – Linear Kernel, and Quadratic Discriminant Analysis were evaluated and compared using six performance measures (confusion matrix, accuracy, precision, recall, F1-score, and ROC).

KEYWORDS:

• Wind damage
• wind disturbance
• Pinus sylvestris
• Picea abies
• machine learning
• Random Forest

Acknowledgements

The authors wish to express their sincere gratitude for their cooperation to the staff of the Forest Service of Xanthi. This synergy contributed to a great extent to the implementation of the current research project. We hope that the research will contribute to the existing body of knowledge and raise awareness among forest managers. The authors also wish to thank Mrs Malivitsi Zoe for editing the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Declarations

• The authors have no relevant financial or non-financial interests to disclose.

• The authors have no competing interests to declare that are relevant to the content of this article.

• All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

• The authors have no financial or proprietary interests in any material discussed in this article.