A Crown Contour Envelope Model of Chinese Fir Based on Random Forest and Mathematical Modeling

Abstract

The tree crown is an important part of a tree and is closely related to forest growth status, forest canopy density, and other forest growth indicators. Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is an important tree species in southern China. A three-dimensional (3D) visualization assistant decision-making system of plantations could be improved through the construction of crown contour envelope models (CCEMs), which could aid plantation production. The goal of this study was to establish CCEMs, based on random forest and mathematical modeling, and to compare them. First, the regression equation of a tree crown was calculated using the least squares method. Then, forest characteristic factors were screened using methods based on mutual information, recursive feature elimination, least absolute shrink and selection operator, and random forest, and the random forest model was established based on the different screening results. The accuracy of the random forest model was higher than that of the mathematical modeling. The best performing model based on mathematical modeling was the quartic polynomial with the largest crown radius as the variable (R-squared (R2) = 0.8614 and root mean square error (RMSE) = 0.2657). Among the random forest regression models, the regression model constructed using mutual information as the feature screening method was the most accurate (R2 = 0.886, RMSE = 0.2406), which was two percentage points higher than mathematical modeling. Compared with mathematical modeling, the random forest model can reflect the differences among trees and aid 3D visualization of a Chinese fir plantation.

Deep learning for crown profile modelling of Pinus yunnanensis secondary forests in Southwest China

“…Previous studies have shown that machine learning has broad application in crown profile modeling. These machine learning includes MLP, SVR, RF, AdaBoost, GBDT and XGBoost (Tian et al, 2021;Chen et al, 2022). Among them, the ensemble learning algorithms can deal with complex nonlinear relationship and show strong prediction ability when predicting the crown profile (Chen et al, 2022).The purpose of this paper is to find the applicable model for crown profile prediction by comparing the ensemble and deep learning algorithms based on the same data format.…”

“…With the rapid development of machine learning artificial intelligence, some machine learning algorithms have the characteristics of high accuracy and good robustness for the data with nonlinear features (Singh et al, 2016;Dong et al, 2021), which has subsequently been applied to crown profile modeling. Tian et al (2021) established crown profile model for Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) based on random forest algorithm, the accuracy of the random forest model was higher than that of the mathematical model.…”

Crown Profile Modeling and Prediction Based on Ensemble Learning

“…Artificial intelligence procedures have been increasingly adopted to overcome problems related to a lack of statistical assumptions [57]. Machine learning methods have been introduced into the study of crown profile [58], and can be used to establish the relationship between crown width at any point in the crown and tree factors in the absence of continuous data, as well as showing a strong nonlinear problem learning ability and the flexibility to analyze longitudinal data. Thus, machine learning is an effective technique in improving the crown profile prediction accuracy.…”

Knots searching and location of Chinese Fir (Cunninghamia lanceolata(lamb.) hook) constructional lumber from Fujian province, China: using contour fitting and extraction method

“…Constructional lumber(CL) had been used as a predominant building material due to its higher ratio of weight to strength and renewability [3][4][5]. There are more and more researches on building materials for modern wood structures, especially lumber, among which the performance of lumber has attracted the most attention [6].Chinese Fir (Cunninghamia lanceolata)(CF) is Taxodiaceae, Cunninghamia is a kind of evergreen tree [7]. Fujian Chinese Fir(FCF), with a height of 30m and a DBH of 2.5 ~ 3.0m, is a unique fast-growing timber tree species in China.…”