Estimation of forest structure metrics relevant to hydrologic modeling using coordinate transformation of airborne laser scanning data
Bibliographic record
Abstract
Abstract. Accurate characterizations of the complex and heterogeneous forest architecture are necessary to parameterize physically-based hydrologic models that simulate precipitation interception, energy fluxes and water dynamics. While hemispherical photography has become a popular method to obtain a number of forest canopy structure metrics relevant to these processes, image acquisition is field-intensive and therefore difficult to apply across the landscape. In contrast, airborne laser scanning (ALS) is a remote sensing technique increasingly used to acquire detailed information on the spatial structure of forest canopies over large, continuous areas. This study presents a novel methodology to calibrate ALS data with in-situ optical hemispherical camera images to obtain traditional forest structure and solar radiation metrics. The approach minimizes geometrical differences between these two techniques by transforming the Cartesian coordinates of ALS data to generate synthetic images with a polar projection directly comparable to optical photography. We demonstrate how these new coordinate-transformed ALS metrics, along with additional standard ALS variables, can be used as predictors in multiple linear regression to estimate forest structure and solar radiation indices at any individual location within the extent of an ALS transect. This approach is expected to substantially reduce fieldwork costs, broaden sampling design possibilities, and improve the spatial representation of forest structure metrics directly relevant to parameterize hydrologic models.
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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 | 0.002 |
| 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 | 0.001 | 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 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".