Characterizing the Spectral-Temporal Signatures of Eastern Hemlock (Tsuga Canadensis) Using Sentinel-2 Satellite Images and Phenology Modelling
Bibliographic record
Abstract
Hemlock woolly adelgid (HWA) is an invasive insect that affects the eastern hemlock population in North America, causing severe die-off and altering ecosystem dynamics. Understanding the distribution of eastern hemlock will improve future HWA management and protection of existing eastern hemlock populations. To determine the degree to which different forest types and species can be distinguished at the stand level with variable densities of eastern hemlock present, a Bayesian phenology model was used to compute seven phenological parameters from four spectral indices derived from Sentinel-2 time series imagery. We tested spectral and phenological parameters derived using this method across three classification levels, including broad forest type, hemlock density, and dominant or co-dominant evergreen species. Using Kruskal-Wallis with post-hoc Dunn’s test, we found that phenological parameters derived from the Inverted Red-Edge Chlorophyll Index and the Soil-Adjusted Vegetation Index provided the highest separability between groups across all three levels of classification. The seasonal minimum greenness and fall inflection day provided the highest degree of separability among hemlock density classes. Seasonal minimum greenness provided the highest degree of separability among evergreen species. Among the nine evergreen dominant or co-dominant species classes tested, hemlock stands were found to be separable from four of the classes. White pine stands and black spruce stands showed the highest degree of overall separability. This study demonstrates the potential for phenological parameters in stand-level evergreen species classification. The combination of Sentinel-2 time series and phenological modeling has the potential to enhance tree species mapping studies at regional scales. • Phenological parameters tested for ability to identify hemlock in mixed stands. • Large phenological differences observed between deciduous and evergreen stands. • Spring inflection day and green-down slope separate evergreen and mixedwood stands. • Seasonal minimum greenness is sensitive to varying hemlock densities. • Hemlock dominant stands differ significantly from black spruce dominant stands.
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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 | 0.000 | 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 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".