Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression
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Aguilar Torres, Fernando José; Nemmaoui, Abderrahim; Aguilar Torres, Manuel Ángel; Peñalver, AlbertoFecha
2021-10-29Resumen
Most of the allometric models used to estimate tree aboveground biomass rely on tree diameter at breast height (DBH). However, it is difficult to measure DBH from airborne remote sensors, and is common to draw upon traditional least squares linear regression models to relate DBH with dendrometric variables measured from airborne sensors, such as tree height (H) and crown diameter (CD). This study explores the usefulness of ensemble-type supervised machine learning regression algorithms, such as random forest regression (RFR), categorical boosting (CatBoost), gradient boosting (GBoost), or AdaBoost regression (AdaBoost), as an alternative to linear regression (LR) for modelling the allometric relationships DBH = Φ(H) and DBH = Ψ(H, CD). The original dataset was made up of 2272 teak trees (Tectona grandis Linn. F.) belonging to three different plantations located in Ecuador. All teak trees were digitally reconstructed from terrestrial laser scanning point clouds. The results showed that ...
Palabra/s clave
terrestrial laser scanning
allometricmodels
machine learning regression
teak plantations
forest inventory