Optimizing crown density and volume estimation across two coniferous forest types in southern China via Boruta and Cubist methods

Abstract

Abstract Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources. And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing. Based on GF-2 remote sensing data, sample plot survey data, and forest resource survey data, this study used the Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) and Pinus massoniana Lamb. as research objects to tackle the key challenges in the use of remote sensing technology. The Boruta feature selection technique, together with multiple stepwise and Cubist regression models, was used to estimate crown density and volume in portions of the research area's stands, introducing novel technological methods for estimating stand parameters. The results show that: 1) the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable, which solves the problem of data and the loss of original feature data after dimensionality reduction; 2) using the Cubist method to build the model yields better results than using multiple stepwise regression. The Cubist regression model's coefficient of determination (R2) is all more than 0.67 in the Chinese fir plots and 0.63 in the Pinus massoniana plots. As a result, combining the two methods can increase the estimation accuracy of stand parameters, providing a theoretical foundation and technical support for future study.