Leaf Area Index Estimation in a Heterogeneous Grassland Using Optical, SAR, and DEM Data
Bibliographic record
Abstract
Different types of remote sensing data, including optical, synthetic aperture radar (SAR), and digital elevation model (DEM), have been used to investigate diverse vegetation properties in grasslands. However, it is not known if integrating these data can improve investigation accuracy and how much of a contribution that each of these data can make to the investigation. In this research, WorldView-2, Sentinel-1, and DEM data are used to estimate vegetation leaf area index (LAI) in a heterogeneous grassland in Canada. From the 3 types of data, 121 optical, 13 SAR, and 7 DEM variables were extracted. Four combinations of the variables, including optical, optical + SAR, optical + DEM, and optical + SAR + DEM, were designed and used to evaluate the contribution of each type of data to the LAI estimation. Four random forest models using these 4 combinations of variables were established to estimate LAI. Results show that the first model built with only optical variables achieved a good accuracy (R2 = 0.630, RMSE = 0.701) that is comparable to other studies. Integrating SAR and DEM variables with optical variables improved LAI estimation accuracy, but not substantially. SAR data’s marginal contribution is likely a result of the heterogeneous nature of the grassland ecosystem.
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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".