Multispectral and LiDAR-Derived Vegetation Indicators of Water Table Dynamics in Forested Wetlands
Bibliographic record
Abstract
We examined the potential to use remotely sensed measures of vegetation composition and structure, including spaceborne multispectral and airborne light detection and ranging (LiDAR) variables, as indicators of forested wetland hydrology. First, we identified statistically significant associations between a suite of remote sensing variables (multispectral and LiDAR-derived vegetation indices) and field-measured metrics of wetland vegetation structure and composition. We then used redundancy analysis (RDA) to evaluate the relationships between remotely sensed vegetation indices and growing season water table dynamics in 13 north-temperate forested wetlands in southwestern Quebec, Canada. Our results show that ratio measures of near-infrared (NIR) and red/green band combination (SR-Simple Ratio, NDVI-Normalized Difference Vegetation Index and NDVIG-Normalized Difference Green Vegetation Index) are associated with many hydrometric variables describing the water table position (WTP), water level fluctuations and hydroperiod. Individual band brightness measures are associated with a smaller subset of these hydrometric variables. However, the incorporation of LiDAR derivatives with image variables in multivariate RDA did not significantly contribute to the explained variance in hydrometric variables. Overall, our results demonstrate that image-derived vegetation indices, more so than LiDAR-derived metrics of vegetation structure, can be used as indicators of water table dynamics in forested wetlands.
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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 | 0.001 |
| 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 | 0.000 | 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".