Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest

Seyedeh Kosar Hamidi, Eric K. Zenner, Mahmoud Bayat, Asghar Fallah

Research output: Contribution to journalArticlepeer-review

Abstract

Abstract: Key message: We modeled 10-year netstand volume growth with four machine learning (ML) methods, i.e., artificialneural networks (ANN), support vector machines (SVM), random forests (RF), andnearest neighbor analysis (NN), and with linear regression analysis.Incorporating interactions of multiple variables, the ML methods ANN and SVMpredicted nonlinear system behavior and unraveled complex relations withgreater accuracy than regression analysis. Context: Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies. Aims: The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models. Methods: In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis. Results: ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%). Conclusion: These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management.

Original languageEnglish (US)
Article number4
JournalAnnals of Forest Science
Volume78
Issue number1
DOIs
StatePublished - Mar 2021

All Science Journal Classification (ASJC) codes

  • Forestry
  • Ecology

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