@article{01JJ93HEM23Y7W5SFXZMMJ4WB3,
  abstract     = {{Leaf-wood separation plays an important role in estimating aboveground biomass (AGB) of trees from terrestrial laser scanning (TLS) point clouds. Yet, leaf-wood separation studies have predominantly focused on reporting the accuracy of leaf and wood point separation. Assessments of the impact of these algorithms on the subsequent AGB estimations, based on commonly used quantitative structure models (QSMs), have been limited. Therefore, in this study, we quantified the impact of 11 published leaf-wood separation algorithms on QSM-based tree AGB estimation using an independent benchmarking dataset. The benchmarking dataset consists of AGB measured for 20 destructively harvested trees from a mixed temperate forest in Harvard Forest and AGB estimated from QSMs built on manually segmented tree point clouds of 856 broadleaved trees in Wytham Woods under leaf-off conditions. These benchmarking AGB values were compared to the AGB estimated from QSMs built on the leaf- removed point clouds resulting from the different separation algorithms performed on the leaf-on tree point clouds of the same trees. The results of the study indicated that for most of the algorithms, the leaf-removed AGB estimates for both coniferous and broadleaved trees underestimated the AGB (conifers:-17 % to-3 %, broadleaf:-14 % to-2 %) compared to the destructively measured AGB in Harvard Forest. In Wytham Woods, leaf-removed AGB estimates from all separation algorithms consistently underestimated the AGB (-46 % to-24 %) compared to the AGB from the leaf-off point clouds. Most leaf-wood separation algorithms performed better on broadleaved trees than on coniferous trees. Moreover, significant differences were observed among different algorithms in estimating AGB for trees of the same forest type. For coniferous trees, the relative difference (RD) of leaf-removed AGB estimates from QSMs and separation algorithms ranged from-27 % to 16 %, among which the best performing algorithms demonstrated similar optimal performance, with small RD values of approximately-3 % to 2 %. For broadleaved trees, the leaf-removed AGB estimates from QSMs and eight separation algorithms, as well as leaf-off point cloud estimates (approximately at 10 %), were closely in agreement with the harvested benchmark values, among which the best performing algorithms had a RD value approximately within f2 %. Additionally, most separation algorithms could lead to better estimates of trunk biomass than branch biomass, whereas the estimation for branch biomass consistently exhibited varying degrees of underestimation. These findings provide a timely reference for utilizing leaf-wood separation algorithms for QSM-based AGB estimation.}},
  articleno    = {{114581}},
  author       = {{Chen, Shilin and Verbeeck, Hans and Terryn, Louise and Van den Broeck, Wouter and Vicari, Matheus Boni and Disney, Mathias and Origo, Niall and Wang, Di and Xi, Zhouxin and Hopkinson, Chris and Dai, Wenxia and Wang, Meilian and Krishna Moorthy, Sruthi M. and Shao, Jie and Ferrara, Roberto and MacFarlane, David W. and Calders, Kim}},
  issn         = {{0034-4257}},
  journal      = {{REMOTE SENSING OF ENVIRONMENT}},
  keywords     = {{Leaf-wood separation,Aboveground biomass estimation,Quantitative structure model,3D point clouds,Terrestrial laser scanning (TLS),Benchmarking dataset,Q-ForestLab,TREE SPECIES CLASSIFICATION,RADIOMETRIC CALIBRATION,LIDAR,FOREST,SEGMENTATION,MODELS}},
  language     = {{eng}},
  pages        = {{24}},
  title        = {{The impact of leaf-wood separation algorithms on aboveground biomass estimation from terrestrial laser scanning}},
  url          = {{http://doi.org/10.1016/j.rse.2024.114581}},
  volume       = {{318}},
  year         = {{2025}},
}

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