Evaluation of the fire-fighting potential of forest landscapes of the Baikal natural territory using thermal infrared information data (on the example of the territory of the Baikal-Lena Reserve)
Bibliographic record
Abstract
The article considers the experience of a comparative assessment of the fire potential fire-stop capability of different types of forests in the conditions of southern permafrost zone. The presented methodology is based on the processing of thermal infrared data from the Landsat TM satellite. On this basis, there were obtained surface temperatures for sites with different forest growing conditions and bioproduction characteristics. This approach has been tested on the example of modal landscapes of the Baikal-Lena State Nature Reserve, located in the central ecological zone of the Baikal natural territory. The possibility of using surface temperatures to estimate the fire-fighting role of different types of forests is based on the equation of the heat balance of the earth’s surface. The thermal values obtained from the processing of thermal infrared images reflect the measure of the emission of sensible heat flux by the landscape. Near-surface temperatures vary by forest type. Forest types with the highest fire-fighting role are characterized by a higher moisture exchange potential and the lowest surface temperature values. It has been revealed that forests on permafrost have higher surface temperature values. Most fire vulnerable forest landscapes coincide with valleys of intermountain depressions, lowlands, bottom of bolsons, submontane uplands and smooth hillsides. Those have different degree of water-logged areas with islands of permafrost and low values of phytomass. Those nature complexes are more fire-dangerous in periods of long droughts due to lower transpiration potential and less influence on micro- and mesoclimat as compared with forest on unfrozen rocks. This technique has been tested in contrasting forest-growing areas of the boreal permafrost zone and can be applied in regions of Russia, Canada and the United States that are similar in terms of landscape and geographic characteristics. This approach can also be used to improve the fire safety systems in Russian forest reserves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".