Remote Sensing of Ecosystem Structure—Part 2: Initial Findings of Ecosystem Functioning through Intra- and Inter-Annual Comparisons with Earth Observation Data
Bibliographic record
Abstract
This study examines the response of a cold-regions deltaic wetland ecosystem in northwestern Canada to two separate and differing seasonal wetting cycles. The goal of this paper was to examine the nature of reflected electromagnetic energy measured by earth observation (EO) satellites, and to assess whether seasonal wetland hydroperiod and episodic flooding events impact the information retrieved by the Sentinel-2 sensors. The year 2018 represents a year characterized by a large spring freshet and ice-jam flooding, while 2019 represents a year characterized more by summer open-water flooding. We applied the Modified Normalized Difference Wetness Index (MNDWI) to address the effects of the wetting cycles. The response of the vegetative cover was tracked using the fraction of the absorbed photosynthetically active radiation (fAPAR) and the Leaf Area Index (LAI). All three indices were viewed through the lens of cover classes as derived through a previously published study by the authors. The study provides a framework for designing longer-term studies where multiple intra- and inter-annual hydrological cycles can be accessed via EO data. Future studies will enable the examination of lag times inherent in the response to the various water sources applied to spectral response and incorporate this EO approach into a monitoring framework.
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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 | 0.001 | 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".