Estimating grassland chlorophyll content using remote sensing data at leaf, canopy, and landscape scales
Bibliographic record
Abstract
A small yet promising body of research has been conducted on the use of remote sensing data to retrieve vegetation chlorophyll content for heterogeneous ecosystems at the leaf level; however, the extent to which leaf chlorophyll contents can be estimated from reflectance measurements at the canopy and landscape scales remain uncertain. The goal of this study was to develop and evaluate a species percent cover-based chlorophyll content scaling up procedure that aims to accurately estimate chlorophyll content at canopy or landscape level. Using both field and QuickBird data collected in a heterogeneous tall grassland located in Ontario, Canada, this study calculated vegetation chlorophyll content at canopy and landscape levels, and it correlated chlorophyll data at leaf, canopy, and landscape levels with a red-edge spectral index. Results indicated that the relationships between the red-edge index and vegetation chlorophyll content (e.g., chlorophyll a, chlorophyll b, chlorophyll a + b) were significant at all three scales in the study site. At the landscape level, the species percent cover-based scaling up chlorophyll was slightly better correlated with the red-edge index than the greenness-based chlorophyll that was calculated using the ratio of green area to total area as an empirical coefficient, but it was much better correlated than the site averaging chlorophyll that was directly averaged from leaf level chlorophyll. These results suggest that inclusion of species percent cover in the scaling up procedure is a more appropriate method for canopy or landscape chlorophyll estimation. What we have to keep in mind is that the proposed scaling procedure only takes into account the species composition within a canopy. More canopy information such as standing dead, litter, and soil background should be considered into the scaling tool in the future.
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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 | 0.001 |
| 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".