Evaluating the use of hyperspectral imagery to calculate raster-based wetland vegetation condition indicator
Bibliographic record
Abstract
Abstract Field observations and measurements of wetland plants have traditionally been used to monitor and evaluate wetland condition; however, there has been increasing use of remote sensing applications for rapid evaluations of wetland productivity and change. Combining key aspects of field- and remote sensing-based wetland evaluation methods can provide more efficient or improved biological indices. This exploratory study set out to develop a raster-based Wetland Vegetation Condition Indicator system that used airborne hyperspectral imagery-derived data to estimate plant-community quality (via wetland classification and Coefficient of Conservatism) and vegetation biomass (estimated using the Normalized Difference Vegetation Index). The Wetland Vegetation Condition Indicator system was developed for three Lake Ontario wetland areas and compared to a field-based floristic quality index and a dominant-plant based Floristic quality indexdom. The indicator system serves as a proof-of-concept that capitalized on the spatial and spectral attributes of high-resolution imagery to quantify and characterize the quality and quantity of wetland vegetation. A Pearson correlation analysis showed moderate r values of 0.59 and 0.62 for floristic quality index and floristic quality indexdom, respectively, compared to the indicator method. The differences are potentially due to the spatial resolution of the imagery and the indicator method only accounting for the dominant plants within each assessment unit (pixel), therefore disregarding understory plants or those with low abundance. However, the multi-metric Wetland Vegetation Condition Indicator approach shows promise as an indicator of wetland condition by using remotely sensed data, which could be useful for more efficient landscape-scale assessments of wetland health, resilience, and recovery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".