Estimation of Fire Counts and Fire Radiative Power Using Satellite Optical and Microwave Vegetation Indices With Random Forest Method
Bibliographic record
Abstract
Abstract The satellite microwave emissivity difference vegetation index (EDVI) has been used in previous studies to estimate FCs and FRP using traditional multivariate linear regression models. However, the nonlinear effects and contributions of numerous factors that affect forest fires cannot be disentangled by this model. Using the random forest (RF) model, this study utilized multiple EDVIs and the optical normalized difference vegetation index (NDVI) as key fuel properties to resolve the physical driving mechanisms of forest fires and to estimate the daily FCs and FRP over East Asia. The results showed that the estimated FCs and FRP were in good agreement with satellite observations, with a spatial R of 0.59 for FCs and 0.63 for FRP and a temporal R of 0.80 for FCs and 0.81 for FRP. The integration of EDVIs and NDVI into the RF model was found to improve model performance and generate overall lower systematic errors than the model without vegetation variables. Model performance was better than that in previous studies using multivariate linear regression models. In addition, EDVIs showed greater importance than NDVI. This was largely due to their daily temporal resolution that allowed EDVIs to capture forest fire dynamics in time. The combination of the RF model with satellite microwave and optical observations shows good performance and has great potential for FC and FRP estimations in global fire danger assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".