Use of GEDI Signal and Environmental Parameters to Improve Canopy Height Estimation over Tropical Forest Ecosystems in Mayotte Island
Bibliographic record
Abstract
Canopy height is a fundamental parameter for describing forest ecosystems.GEDI is a spaceborne LiDAR system that was designed to measure vegetation's vertical structure at a global scale.This study evaluates the accuracy of GEDI-derived canopy height estimates over complex tropical forests in Mayotte Island (Overseas France) characterized by moderate height and biomass levels as well as a relatively steep terrain.The influence of GEDI signal and environmental parameters (canopy height, beam sensitivity and slope) on height estimates was assessed.Linear as well as non-linear approaches were implemented using the GEDI L2A product to estimate canopy height.Empirical models were trained on reference data derived from airborne LiDAR scanning.The results showed that using regression models built on multiple GEDI metrics yielded improved accuracies compared to a direct estimation from a single GEDI height metric.Canopy height, beam sensitivity and terrain slope were found to have a significant impact on the height metrics derived from GEDI waveforms.Conversely, both linear and non-linear regression models produced unbiased and stable estimates. RÉSUMÉLa hauteur de la canopée est un paramètre fondamental pour décrire les écosystèmes forestiers.GEDI est un système LiDAR spatial conçu pour mesurer la structure verticale de la végétation à l'échelle mondiale.Cette étude évalue la précision des estimations de la hauteur de la canopée à partir de GEDI sur des forêts tropicales complexes de l'île de Mayotte (France d'outre-mer) caractérisées par des hauteurs et des niveaux de biomasse modérés ainsi que par un terrain relativement escarpé.L'influence du signal GEDI et des paramètres environnementaux (hauteur de la canopée, sensibilité du faisceau laser et pente du terrain) sur les estimations de hauteur a été évaluée.Des approches linéaires et non-linéaires ont été mises en œuvre en utilisant le produit GEDI L2A pour estimer la hauteur de la canopée.Des modèles empiriques ont été entraînés sur des données de référence issues d'acquisitions par LiDAR aéroporté.Les résultats ont montré que l'utilization de modèles de régression construits à partir de plusieurs métriques GEDI permettait d'améliorer la précision par rapport à une estimation directe à partir d'une seule métrique de hauteur GEDI.La hauteur de la canopée, la sensibilité du faisceau et la pente ont eu un impact significatif sur les métriques de hauteur dérivées des formes d'onde GEDI.Par ailleurs, les modèles de régression linéaire et non-linéaire ont produit des estimations stables et sans biais.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".