Assessing satellite-derived fire patches with functional diversity trait methods
Bibliographic record
Abstract
Fire disturbance is a significant component of the climate system. Analysis of satellite-derived burned areas has allowed the identification of fire patches and their morphology as a new resource for tracking fire spread to improve fire models used to assess the impact of fires on climate and the carbon cycle . A critical parameter of the flood-fill algorithm used to create fire patches is the cut-off (in days) below which it aggregates two contiguous burned pixels to the same fire patch. However, the current level of validation is insufficient to understand the effect of the cut-off values and sensor resolutions on the subsequent fire-patch morphology. The FRY v1.0 database of functional fire-patch traits (e.g., size, elongation, and direction) emanates from the analyses of two global burned-area products derived from MODIS and MERIS sensors with different spatial and temporal resolutions and with cut-off values of 3, 5, 9, and 14 days. To evaluate whether the FRY products are accurately identifying the spatial features of fire patches and what are the most realistic cut-off values to use in different sub-regions of North America, we propose a new functional diversity trait-based approach, which compares the satellite-derived fire patches to forest service perimeters as reference data. This paper shows the accuracy of the FRY fire patches ≥300 ha in North America during 2005–2011. Our analysis demonstrates that fire patches with a high cut-off of 14 days and those derived from the MODIS sensor, with their high temporal resolution, better identify the fire diversity in North America. In conclusion, our statistical framework can be used for assessing satellite-derived fire patches. Furthermore, the temporal resolution of satellite sensors is the most important factor in identifying fire patches — thus space agencies should consider it when planning the future development of cost-effective climate observation systems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".