Sensitivity of the Landsat enhanced wetness difference index (EWDI) to temporal resolution
Bibliographic record
Abstract
AbstractWe investigated the sensitivity of the Landsat enhanced wetness difference index (EWDI) to temporal resolution in detecting forest harvesting and silvicultural activities in the Fundy Model Forest in southern New Brunswick. The severity of disturbance was underestimated in the multiyear EWDI because of regrowth and natural variability. In certain instances the severity of disturbance was overestimated with an annual EWDI; for example, a herbicide application in a young stand could have an annual wetness difference approximately equal to that of a forest clearcut. In general, we found annual EWDI differences to be more accurate than multiyear differences in detecting change. The sensitivity of satellite image change detection techniques to temporal resolution must be considered when national or international protocols are developed to provide input to forest monitoring or carbon accounting efforts.Nous avons étudié la sensibilité de l'indice EWDI (« enhanced wetness difference index ») de Landsat à la résolution temporelle dans la détection des activités de coupe forestière et sylvicoles dans la forêt modèle de Fundy, dans le sud du Nouveau-Brunswick. Le niveau de perturbation a été sous-estimé dans les données EWDI multi-années en raison de la repousse et de la variabilité naturelle. Dans certains cas, le niveau de perturbation était surestimé par la valeur annuelle de EWDI; par exemple, une application d'herbicide dans un jeune peuplement pouvait montrer une différence annuelle d'humidité sensiblement égale à celle d'une coupe à blanc. En général, nous avons trouvé que les différences annuelles de EWDI étaient plus précises que les différences multi-années pour la détection du changement. La sensibilité des techniques de détection du changement par satellite à la résolution temporelle doit être prise en considération lorsque l'on développe des protocoles nationaux ou internationaux afin de fournir des intrants aux activités de suivi de la forêt ou de comptabilité du carbone.[Traduit par la Rédaction]
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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 | 0.000 |
| 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".