Estimating Fractional Cover of Grassland Components from Two Satellite Remote Sensing Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it