Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images
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
Forest ecosystems are exposed increasingly to a variety of human activities and accentuated by climate change. With its Mediterranean climate, Northern Morocco is very hot, which exposes forests to widespread fires. This work aims at the delineation of wildfires and the spectral characterization of burnt vegetation as well as the characterization of the fire severity in the North of Morocco by using Landsat-8, Sentinel-2 spectral data, and topographic data. The methods used include the derivation of wildfires spectral indices and the computation of topographic parameters (elevation, slope, exposure) from SRTM and PALSAR digital elevation models. Then, the Spectral Angle Mapper (SAM) classification was used to map forest fires' severity. Furthermore, we have compared the severity classes obtained from the SAM method applied to Landsat 8 and Sentinel 2 data, with different spectral indices specialized in detecting wildfires, on the one hand, and topographic data, on the other hand. Results showed that MIRBI and NBR indices allow a better characterization of burned areas than BAI index. For its part, SAM classification provides a fair characterization of the severity classes of burnt forests. It has also been shown that the MIRBI index and sun exposure are strongly correlated with severity classes. The obtained maps show the spatial heterogeneity of burns severity and how they interact with topography. These maps may help land resource managers and fire officials predict areas of potential fire hazards and study vegetation regrowth areas after fires.
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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 | 0.002 |
| 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 it