Constructing a biomass model of Larix olgensis primary branches based on TLS

TANG Yiren, JIA Weiwei, WANG Fan, SUN Yuman, ZHANG Ying

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (2) : 130-140.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (2) : 130-140. DOI: 10.12302/j.issn.1000-2006.202204037

Constructing a biomass model of Larix olgensis primary branches based on TLS

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Abstract

【Objective】 To explore the feasibility of using terrestrial laser scanning (TLS) point cloud data to estimate branch biomass and find the optimum model to predict the branch biomass of Larix olgensis. 【Method】 The characteristic factors (branch length (LBL), branch chord length (LBCL), branch diameter (dB), branch angle (AB), branch arch height (HBAH), basal area of branches (SBAB), and relative depth within the crown (dRDINC)) of 733 first-order branches extracted using point cloud data of 26 artificial larch plants and the corresponding measured data from Mengjiagang Forestry Farm were used as data sources to establish first-level branch biomass base models at the branch level. Additionally, the feasibility of using TLS data to establish branch biomass models by comparing the differences between base models were analyzed. Finally, the prediction effects of the base model, mixed-effects model, and random forest model were compared separately using TLS data. 【Result】 The independent variables selected in the base model were SBAB and LBCL. Using TLS data to build the branch biomass base model had a better prediction accuracy. Comparing the results of the three models showed that the random forest model showed the best results both on the training and test sets, in the following order: random forest model > mixed-effects model > base model. The R2 of the random forest model improved by 1.32% and 4.89%, and the RMSE decreased by 11.23% and 13.60%, respectively, compared to the mixed model and the base model. 【Conclusion】 The results of this paper proved that the branch biomass can be estimated accurately based on TLS using the random forest algorithm, which not only laid a practical foundation for the application of the random forest algorithm on stand growth models, but also provided an important reference value for the application of TLS in the study of crown structure.

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Larix olgensis / point cloud data / branch characterization factor / branch biomass / mixed-effects model / random forest

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TANG Yiren , JIA Weiwei , WANG Fan , et al . Constructing a biomass model of Larix olgensis primary branches based on TLS[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2023, 47(2): 130-140 https://doi.org/10.12302/j.issn.1000-2006.202204037

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