A new method to estimate branch biomass from terrestrial laser scanning data by bridging tree structure models. (7th March 2021)

• Record Type: Journal Article
• Authors: Hu, Man; Pitkänen, Timo P; Minunno, Francesco; Tian, Xianglin; Lehtonen, Aleksi; Mäkelä, Annikki
• Is Part Of: Annals of botany. Volume 128:Number 6(2021)
• Page Start: 737
• Page End: 752
• Publication Date: 2021-03-07
• Subjects: Branch biomass -- terrestrial laser scanning -- tree-structure model -- individual branch attributes -- quantitative structure model; Botany -- Periodicals; 580
• DOI: 10.1093/aob/mcab037
• Languages: English
• ISSNs: 0305-7364

Abstract

Abstract: Background and Aims: Branch biomass and other attributes are important for estimating the carbon budget of forest stands and characterizing crown structure. As destructive measuring is time-consuming and labour-intensive, terrestrial laser scanning (TLS) as a solution has been used to estimate branch biomass quickly and non-destructively. However, branch information extraction from TLS data alone is challenging due to occlusion and other defects, especially for estimating individual branch attributes in coniferous trees. Methods: This study presents a method, entitled TSMtls, to estimate individual branch biomass non-destructively and accurately by combining tree structure models and TLS data. The TSMtls method constructs the stem-taper curve from TLS data, then uses tree structure models to determine the number, basal area and biomass of individual branches at whorl level. We estimated the tree structural model parameters from 122 destructively measured Scots pine ( Pinus sylvestris ) trees and tested the method on six Scots pine trees that were first TLS-scanned and later destructively measured. Additionally, we estimated the branch biomass using other TLS-based approaches for comparison. Key Results: Tree-level branch biomass estimates derived from TSMtls showed the best agreement with the destructive measurements [coefficient of variation of root mean square error (CV-RMSE) = 9.66 % and concordance correlation coefficient (CCC) = 0.99], outperforming the other TLS-based approaches (CV-RMSE 12.97–57.45 % and CCC 0.43–0.98 ). Whorl-level individual branch attributes estimates produced from TSMtls showed more accurate results than those produced from TLS data directly. Conclusions: The results showed that the TSMtls method proposed in this study holds promise for extension to more species and larger areas.