Disturbance recognition in the boreal forest using radar and Landsat-7
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractAs part of a Siberian mapping project supported by the National Aeronautics and Space Administration (NASA), this study evaluated the capabilities of radars flown on the European Remote Sensing Satellite (ERS), Japanese Earth Resources Satellite (JERS), and Radarsat spacecraft and an optical sensor enhanced thematic mapper plus (ETM+) on-board Landsat-7 to detect fire scars, logging, and insect damage in the boreal forest. Using images from each sensor individually and combined, an assessment of the utility of using these sensors was developed. Transformed divergence analysis revealed that Landsat ETM+ images were the single best data type for this purpose. However, the combined use of the three radar and optical sensors did improve the results of discriminating these disturbances.Réalisée dans le cadre d'un projet de cartographie de la Sibérie financé par la NASA, cette étude a évalué le potentiel des radars à bord des satellites ERS, JERS et Radarsat et d'un capteur optique, le capteur ETM+ à bord de Landsat-7, pour la détection des cicatrices d'incendies, des coupes forestières et des dommages liés aux insectes dans la forêt boréale. Basé sur l'utilization d'images de chacun de ces capteurs, individuellement ou en combinaison, nous avons réalisé une évaluation de l'utilité de ces capteurs. Une analyse de divergence transformée a révélé que les images Landsat ETM+ constituaient, sur une base individuelle, le meilleur type de données pour cet objectif. Toutefois, l'utilization combinée des trois capteurs radar et du capteur optique a permis d'améliorer les résultats de la détermination de ces perturbations.[Traduit par la Rédaction]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it