Identifying long-term burned forests in the rugged terrain of Southwest China:A novel method based on remote sensing and ecological mechanisms
Bibliographic record
Abstract
Burned forests were detected using remote sensing techniques. Yet, identifying long-term burned forests in the mountains, especially at a large spatial extent, remains a great challenge due to a lack of long-term high-resolution remote sensing data or the unsatisfactory performance of the moderate-resolution remotely sensed data in complex mountain landscapes. Efficient, robust, and cost-effective methods are urgently called for. In this study, we developed a novel method combining remote sensing and ecological mechanisms (short for RSEM-M) with a paired-pixel approach to identify the long-term burned forests in Southwest China, where the terrain is rugged. In mapping burned forests in 2010, the overall accuracy, producer accuracy, user accuracy, Kappa coefficient, and field validation accuracy were 92.27 %, 95.86 %, 88.36 %, 0.85, and 81.16 %, respectively. Compared to the standard procedure and FireCCI51 and MCD64A1 fire products, the RSEM-M shows higher accuracy and consistency in identifying burned forests of different sizes, with fewer invalid pixels, better spatial continuity, and more accurate boundary delineation. Then, using the RSEM-M, we mapped burned forests in Southwest China from 2002 to 2017. The cumulative areas were 37.69 × 10 4 ha. The RSEM-M improved the efficiency and accuracy of long-term burned forest identifications in mountainous landscapes at a large spatial extent.
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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 | 0.000 |
| 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 | 0.001 | 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".