Mapping Above- and Below-Ground Biomass Components in Subtropical Forests Using Small-Footprint LiDAR
Bibliographic record
Abstract
In order to better assess the spatial variability in subtropical forest biomass, the goal of our study was to use small-footprint, discrete-return Light Detection and Ranging (LiDAR) data to accurately estimate and map above- and below-ground biomass components of subtropical forests. Foliage, branch, trunk, root, above-ground and total biomass of 53 plots (30 × 30 m) were modeled using a range of LiDAR-derived metrics, with individual models built for each of the three dominant forest types using stepwise multi-regression analysis. A regular grid covered the entire study site with cell size 30 × 30 m corresponding to the same size of the plots; it was generated for mapping each biomass component. Overall, results indicate that biomass estimation was more accurate in coniferous forests, compared with the mixed and broadleaved plots. The coefficient of determination (R2) for individual models was significantly enhanced compared with an overall generic, or common, model. Using independent stand-level data from ground inventory, our results indicated that overall the model fit was significant for most of the biomass components, with relationships close to a 1:1 line, thereby indicating no significant bias. This research illustrates the potential for LiDAR as a technology to assess subtropical forest carbon accurately and to provide a better understanding of how forest ecosystems function in this region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".