The impact of leaf-wood separation algorithms on aboveground biomass estimation from terrestrial laser scanning
Bibliographic record
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 ±2 %. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".