Estimation de la hauteur des peuplements forestiers par diffusiomètre radar
Bibliographic record
Abstract
This paper presents the results of an analysis of radar airborne scatterometer data obtained over an Austrian black pine (Pinus nigra Arnold ssp. nigricans Host.) forest in Lozère, France. This system provides vertical sounding inside canopy from which can be derived tree height estimations. The objective of this paper is to analyse the validity of these measurements and, then, to study the interest of this kind of sensors for better forest management. The mean and top height estimations give very good results with a relative error of about 6%. The radar measures the back scatter coefficient of the vegetation from which can be estimated other parameters such as stem volume or basal area. The radar backscatter coefficient is found to be sensitive to the biomass in a low to intermediate range of values (0-200 m3/ha), particularly when the canopy backscattering is separated from the ground echo. The estimation of stem volume using a regression with both tree height estimations and backscatter measurements gives satisfying results. Finally, we discuss the interest of this kind of sensor data for an improved and detailed forest management giving an example of application on the evaluation of the variability of the local site index within a stand.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".