Forest structure, roads and soil moisture provide realistic predictions of fire spread in modern Swedish landscape
Bibliographic record
Abstract
• Vegetation properties can be used to predict forest fire susceptibility in Sweden. • High Scots pine stock increases fire susceptibility; deciduous species’ decrease it. • Cellular automata model can realistically predict fire risks in Swedish forests. Recent increases in fire activity in Sweden call for the quantification of forest fire susceptibility, in order to develop management strategies to mitigate fire risk. Using the data from 100 large Swedish forest fires (>10 ha), mapped from sentinel-2 images from 2016 to 2022, we explored the predictive power of vegetation properties in estimating relative likelihood of fires within a landscape using logistic regression. To model spatially explicit fire susceptibility within a given landscape, we used the outcome of logistic regression as an input into a cellular automata model (CA model), which simulates fire spread in a 2D grid. The CA was model calibrated on three fires that occurred between 2016 and 2022, then verified on six 2023 fires and featured a mean sensitivity of 0.74 and specificity of 0.79. The logistic regression model had an accuracy of 54 %, showing increased fire susceptibility from high Scots pine volume ( p -value = 0.02), and decreased fire susceptibility from high volumes of deciduous trees and wet soil. Realistic outcomes of the CA model and reliance of our approach on publicly available data with nation-wide coverage of vegetation cover in Sweden allows for the development of an automated protocol of fire susceptibility assessment at the operational level and its integration in existing decision support systems. This would allow forest owners to obtain estimates of forest fire susceptibility for different forest management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".