Forest cover type classification at Mt. Asama using ALOS fully Polarimetric PALSAR data, in Nagano prefecture, central Japan
Bibliographic record
Abstract
PALSAR (Phased Array L-band Synthetic Aperture Radar) was installed on the Japanese ALOS (Advanced Land Observing Satellite) that was launched on 24 January 2006. This is the first satellite regularly operated with fully polarimetric SAR in the world, and is considered useful for monitoring forest and biomass, as well as topography and land use. Forest cover types and stand volume classifications were produced for Mt. Asama using unsupervised and supervised classifications from the backscatter matrix using polarimetric PALSAR data. The Mt. Asama region was selected for study because of its gentle geographic features and large number of representative Japanese larch (Larix kaempferi), red pine (Pinus densifolia), and sub-alpine conifer (Abies mariesii and Tsuga diversifolia) forests. The unsupervised classification data allowed the forest region to be distinguished from fields, rice fields, pastures, residential areas, roads, and bare ground. We also produced forest cover type and volume classification images using a supervised classifier with GIS and field data, with a classification accuracy of 11.0-56.5%. The accuracy of highest volume classes was 301-450 m3/ha (56.5%) and 451-800 m3/ha (49.6%). This may have been because the influence of geographic features such as slope azimuth and ridges were larger than that of forest cover types. Therefore, polarimetric PALSAR data are suitable for monitoring deforestation and detecting changes in forest cover types in a homogenous, large forested region without complex mountainous geographic features.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".