Satellite‐derived ecosystems classification: image segmentation by ecological region for improved classification accuracy, a boreal case study
Bibliographic record
Abstract
An unsupervised image classification technique employing image segmentation by ecological regions is evaluated using percentage accuracies and tau coefficients against an unsegmented two‐stage classification. k‐fold cross‐validation is used to partition the field data into training and testing sets. A Z‐test of the tau statistic and its variance is used to test for a significant increase in classification accuracy when using image segmentation. Results show a significant increase in classification accuracy (α = 0.05, one‐tailed) over two‐stage approaches (Z = 2.49, Z crit = 1.65 p = 0.0063). This supports our hypothesis that spectral variance within information classes can be explained, in part, by ecological region. Multi‐group discriminant analysis is performed using jack pine (Pinus banksiana) plant community spectral data, grouped by ecological region. Results show significant spectral differences in a single information class within different ecological regions, which support the image segmentation approach to classification. The minimum mappable unit (MMU) is discussed in the context of Landsat Thematic Mapper (TM). The plant association, or ecosite, is presented as the MMU and the physical and ecological properties are discussed in relation to their spectral properties. The results suggest refinements in data collection and image analysis for remotely sensed data in boreal environments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".