Enhanced Grassland Biomass Estimation Using Vegetation Indices and Biomass Proxy: A Comparative Study of Parametric and Non-parametric Models in Manitoba’s Prairie Ecozone
Bibliographic record
Abstract
Grasslands, covering approximately 40% of Earth's terrestrial surface, play a pivotal role in the economy and climate change. Grasslands are among the most vital ecosystems concerning carbon sinks and species-at-risk. Above-ground biomass (AGB) is an important indicator of grasslands’ state and productivity. Monitoring grassland AGB using remote sensing data provides extensive, continuous and reliable information for decision-makers. This study aimed to enhance grassland biomass predictions using vegetation indices (VIs) and Biomass Proxy (BP) in the Prairie Ecozone of Manitoba. A total of 96 field biomass samples, collected between 2021 and 2023, were used. Both parametric and non-parametric machine learning models were evaluated to determine the most effective method for AGB estimation. The results showed that among the parametric models, the BP-based exponential model achieved the highest accuracy with R2= 0.61 and RMSE = 31.34 g/m2. The Random Forest (RF) model outperformed all others with R2= 0.75 and RMSE = 30.16 g/m2, while the Support Vector Regression (SVR) model with a polynomial kernel achieved R2= 0.58. These results suggest that non-parametric models, particularly RF, are more effective in capturing the complex relationships between remote sensing features and grassland biomass
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".