Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening

Xuan Liu, Ruirui Wang (), Wei Shi, Xiaoyan Wang and Yaoyao Yang
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Xuan Liu: College of Forestry, Beijing Forestry University, Beijing 100083, China
Ruirui Wang: College of Forestry, Beijing Forestry University, Beijing 100083, China
Wei Shi: Beijing Ocean Forestry Technology Co., Ltd., Beijing 100083, China
Xiaoyan Wang: College of Forestry, Beijing Forestry University, Beijing 100083, China
Yaoyao Yang: College of Forestry, Beijing Forestry University, Beijing 100083, China

Sustainability, 2024, vol. 16, issue 10, 1-17

Abstract: The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman’s Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R 2 of 0.85 and an MSE of 10.74 on the training set and an R 2 of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening.

Keywords: LiDAR; feature screening; carbon stock; bagging; random forest; forests; model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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