Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, Helsinki, Finland.
Institute for Atmospheric and Earth System Research/Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland.
Ann Bot. 2021 Oct 27;128(6):737-752. doi: 10.1093/aob/mcab037.
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.
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.
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.
The results showed that the TSMtls method proposed in this study holds promise for extension to more species and larger areas.
枝条生物量和其他属性对于估计林分的碳预算和描述树冠结构非常重要。由于破坏性测量既耗时又费力，因此已经使用地面激光扫描 (TLS) 作为解决方案来快速、非破坏性地估计枝条生物量。然而，由于遮挡和其他缺陷，仅从 TLS 数据中提取枝条信息具有挑战性，尤其是对于估计针叶树的单个枝条属性。
本研究提出了一种方法，即 TSMtls，通过结合树木结构模型和 TLS 数据来非破坏性和准确地估计单个枝条的生物量。TSMtls 方法从 TLS 数据构建树干锥度曲线，然后使用树木结构模型确定轮生水平上每个枝条的数量、基部面积和生物量。我们从 122 棵破坏性测量的欧洲赤松 (Pinus sylvestris) 树上估计了树木结构模型参数，并在六棵首先进行 TLS 扫描然后进行破坏性测量的欧洲赤松树上测试了该方法。此外，我们还使用其他基于 TLS 的方法估计了枝条生物量以进行比较。
TSMtls 得出的树级枝条生物量估计与破坏性测量结果最吻合 [均方根误差的变异系数 (CV-RMSE) = 9.66%和一致性相关系数 (CCC) = 0.99]，优于其他基于 TLS 的方法 (CV-RMSE 12.97-57.45%和 CCC 0.43-0.98)。TSMtls 生成的轮生水平的单个枝条属性估计结果比直接从 TLS 数据生成的结果更准确。
结果表明，本研究提出的 TSMtls 方法有望扩展到更多物种和更大区域。
