Work

Anomaly Detection and Machine Learning for Stand-Level Growth and Yield Modeling in Hybrid Eucalypt Plantations

Year: 2026

Type: article

Abstract: Accurate prediction of forest growth and yield is essential for strategic planning in intensive plantation management. This study evaluates whether unsupervised anomaly detection can be used as a systematic data-quality layer in stand-level growth and yield modeling, after standard consistency checks have been applied. We used a multi-regional continuous forest inventory of hybrid Eucalyptus urophylla × Eucalyptus grandis plantations in Minas Gerais, Brazil (6,553 measurements from 1,749 permanent plots in three regions). Four unsupervised methods, Isolation Forest, One-Class SVM, Local Outlier Factor and a dense autoencoder, were applied within each region to identify multivariate anomalies in stand age, volume, basal area and dominant height. Using Isolation Forest with a contamination rate of 0.10, approximately 10% of records were flagged as anomalous, but this corresponded to the complete removal of 2–4% of plots, while most plots either retained all measurements or lost only a subset of them. We then calibrated nine nonlinear machine learning models to predict stand volume at the second measurement (V₂) under two scenarios: using the full dataset and using the anomaly-filtered dataset. Gradient Boosting and other tree ensembles achieved the best performance in both cases (R 2 ≈ 0.86–0.88, relative RMSE ≈ 13–15%), and differences in accuracy between full and filtered datasets were small (changes in R 2 < 0.03 and in relative RMSE < 1 percentage point). A sensitivity analysis across contamination levels from 0.05 to 0.20 confirmed that a 10% threshold offers a practical compromise, removing a consistent set of multivariate extremes while preserving plot coverage and predictive performance.

Source: Forest Science

DOI: 10.1007/s44391-026-00062-y

Authors: Gianmarco Goycochea Casas, Zool Hilmi Ismail, Pedro Henrique Fontes dos Santos, Leonardo Ippolito Rodrigues, Tassius Menezes Araújo, Helio Garcia Leite

Institutions: Universidade Federal de Viçosa, University of Technology Malaysia

Language: English

Topic: Forest ecology and management

Subfield: Nature and Landscape Conservation

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger

Open Access status: hybrid

Estimated APC paid: $2,690