Identifying Burnt Areas in Forests and Land Fire Using Multitemporal Normalized Burn Ratio (NBR) Index on Sentinel-2 Satellite Imagery
Bibliographic record
Abstract
Forest and land fires are global disasters that pose a serious threat to society, particularly during the dry season.Sumatra Island, located in Indonesia, is one of the areas prone to forest and land fires.It causes thick smoke every year which has implications for health and flight activities.It also threatens the 18,931 endemic plant species on the Sumatra Island so this needs to be monitored continuously for mitigation and recovery.This study aims to identify areas of past forest and land fires to support it.In this study, burnt areas were identified by utilizing Normalized Burn Ration (NBR) index on Sentinel-2 Satellite imagery using thresholding method.It is calculated as the ratio between the near-infrared (NIR) and short-wave infrared (SWIR) wavelengths.Both are very sensitive to vegetation and bare land so NBR is very potential for identification of burnt areas using multitemporal techniques.The results of this study indicated that the highest accuracy was achieved using the µ-2σ threshold model of dNBR, with the highest accuracy rate of 81.56% for the February 2019 fires.The results of this study suggest that the thresholding method using the NBR index can be utilized to identify burnt areas effectively.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".