Spatial quantification of vegetation density from terrestrial laser scanner data for characterization of 3D forest structure at plot level
Bibliographic record
Abstract
Precise description of forest 3D structure at plot level is required for sustainable ecosystem management. However, a detailed structure description from traditional field measurements is tedious. We propose an innovative method to quantify in 3D the spatial distribution of forest structure from terrestrial lidar data. The method rests on the hypothesis that the normalized number of laser returns within a given volume element is proportional to the density of vegetation material inside this volume. The developed model is based on analysis made inside Svoxels (spherical voxels) to compute a spatialized vegetation density index. The model was tested on two different scans of the same plot. The resulting vegetation density index well represents the vegetation structure as observed within the lidar point cloud. Quantitative analyses confirmed a global consistency of the results within and between scans. However, we observed a slight bias in the computed density indexes. It might be mainly explained by occlusions, which cause 1) a slight decrease of the density index with distance and 2) local differences in density index between scans.. Future work will focus on improving our algorithm and correcting biases. These results are promising for the development of quantitative measures of the 3D forest structure.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".