Estimating Fractional Cover of Grassland Components from Two Satellite Remote Sensing Sensors
Bibliographic record
Abstract
In this study, the fractional cover (f) of three grassland components (photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and background (B)) were estimated using Landsat 5 and CHRIS/Proba sensors. In 2009, a field campaign was carried out at three sites on the mixed prairie of southern Alberta, Canada to collect in situ measurements of fractional cover. Landsat 5 and CHRIS/Proba images were acquired near the same time as the ground measurements. The fPV was found to be closely related to the Modified Transformed Vegetation Indexes 1 and 2 (MTVI1, MTVI2; R 2 0.72 and 0.76) calculated from Landsat imagery. Narrow band versions of these and two other narrow band indices, the Red-edge Index (RE) and the Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI)), derived from nadir CHRIS imagery were also reasonable predictors of fPV. The estimates of non-photosynthetic vegetation were poor using these indices. A soil adjusted vegetation index, the Normalized Difference Senescent Vegetation Index derived from Landsat 5 produced a reasonable relationship with NPV ground cover (R 2 0.70; RMSE 3.52%). Estimation of fB from 100-(fPV+fNPV) consequently gave a similar reasonable relationship (R 2 of 0.71 ~ 0.82 and RMSE of 5.57~7.06%). The results showed that fPV and fNPV of mixed prairie rangeland could be estimated with an RMSE of 4-6 % using Landsat-derived vegetation indices. Such estimates of f could become a critical input to more comprehensive estimation of grassland biomass and growth rates in Alberta rangelands. Key words: grassland ecosystem, remote sensing, fractional cover, vegetation, background 1
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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 | 0.001 | 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".