Modelling above ground biomass for a mixed-tree urban arboretum forest based on a LiDAR-derived canopy height model and field-sampled data
Bibliographic record
Abstract
There is an increasing recognition of the value of forests for carbon storage, and hence interest in estimating Above Ground Biomass (AGB) for individual trees and across forests. Field-based inventories of AGB for hundreds of trees based on variables such as height and Diameter at Breast Height (DBH) can be costly and labor intensive, and hence there is increasing interest in evaluating remote sensing methods as an alternative. In this study, we assessed the suitability of Light Detection and Ranging (LiDAR) derived Canopy Height Model (CHM) based on drone data to estimate tree heights and hence AGB in a mixed species (56 species) even aged (~10 years old) arboretum in subtropical Queensland, Australia. First field-based estimates of AGB were obtained for a stratified random sample of 287 trees across the arboretum based on height, DBH and species wood density. Then CHM values was obtained based on LiDAR data for the whole arboretum and compared with the field data. The CHM values were correlated with field data for individual trees including height (r=0.85), DBH (0.52), and AGB (0.52), using Pearson’s correlation coefficients. Using a linear regression CHM was used to predict the height (R 2 = 0.73, height = 1.9436 + 0.8471*CHM), and AGB (R 2 = 0.43, ln(AGB) = 0.4904 + 0.1335*CHM) for individual trees. Incorporating other remote sensing data, such as Sentinel-2 derived vegetation indices (vegetation index, enhanced vegetation index and leaf area index) did not significantly improve the accuracy of the AGB estimates per tree. The outcome of this work confirms that LiDAR derived CHM is a good predictor of individual tree heights and a moderate predictor of AGB and could therefore be used as a proxy for biomass estimation for individual trees and over urban forests. • Drone-derived LiDAR data used to estimate tree heights and AGB in urban forest. • LiDAR derived canopy height model (CHM) is a good predictor of individual tree heights • CHM is a proxy for biomass estimation for individual trees and over urban forests. • Vegetation indices did not significantly improve the accuracy of AGB estimates per tree
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".