Mapping species-specific aboveground forest biomass for Guangxi using wall-to-wall airborne laser scanning data
Bibliographic record
Abstract
Estimates of aboveground forest biomass (AGB) and carbon fluxes, especially on a species-specific basis, over an extensive area are crucial in decision-making in forestry. This study generated the species-specific AGB map with a resolution of 20 m using three data sources (field measurement, wall-to-wall airborne laser scanning (ALS) data, and aerial digital orthophoto map (DOM)). Field measurement and corresponding ALS data (point cloud density ≥ 2.0/m 2 ) were collected from 1086 sample plots located across four arbor forest types (Chinese fir (Cunninghamia lanceolata) , Eucalyptus (Eucalyptus grandis × Eucalyptus urophylla) , Masson pine (Pinus massoniana) , and other mixed broadleaved forests) in Guangxi province, China, between 2017 and 2019. Firstly, we developed the species-specific AGB models based on field measurement and ALS data using four modeling algorithms (linear regression (LR), support vector regression (SVR), random forest (RF), generalized additive modeling (GAM)). GAM models performed the best, with a relative RMSE ranged from 13.6 % to 35.3 %. Secondly, forest type assigned to each grid cell in the rasterized wall-to-wall ALS data was derived from DOM-derived forest-type distribution map. Finally, a species-specific AGB map with a resolution of 20 m was generated by inputting forest type and ALS metrics for each grid cell in the rasterized wall-to-wall ALS data into the species-specific GAM models. Our AGB map could be useful to formulate the effective strategies of forest management. In the future, one can monitor the changes of AGB or carbon storage in Guangxi using our AGB map.
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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 | 0.001 | 0.001 |
| 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".