Remote Sensing of Wildland Fire-Induced Risk Assessment at the Community Level
Bibliographic record
Abstract
Wildland fires are some of the critical natural hazards that pose a significant threat to the communities located in the vicinity of forested/vegetated areas. In this paper, our overall objective was to study the structural damages due to the 2016 Horse River Fire (HRF) that happened in Fort McMurray (Alberta, Canada) by employing primarily very high spatial resolution optical satellite data, i.e., WorldView-2. Thus, our activities included the: (i) estimation of the structural damages; and (ii) delineation of the wildland-urban interface (WUI) and its associated buffers at certain intervals, and their utilization in assessing potential risks. Our proposed method of remote sensing-based estimates of the number of structural damages was compared with the ground-based information available from the Planning and Development Recovery Committee Task Force of Regional Municipality of Wood Buffalo (RMWB); and found a strong linear relationship (i.e., r² value of 0.97 with a slope of 0.97). Upon delineating the WUI and its associated buffer zones at 10 m, 30 m, 50 m, 70 m and 100 m distances; we found existence of vegetation within the 30 m buffers from the WUI for all of the damaged structures. In addition, we noticed that the relevant authorities had removed vegetation in some areas between 30 m and 70 m buffers from the WUI, which was proven to be effective in order to protect the structures in the adjacent communities. Furthermore, we mapped the wildland fire-induced vulnerable areas upon considering the WUI and its associated buffers. Our analysis revealed that approximately 30% of the areas within the buffer zones of 10 m and 30 m were vulnerable due to the presence of vegetation; in which, approximately 7% were burned during the 2016 HRF event that led the structural damages. Consequently, we suggest to remove the existing vegetation within these critical zones and also monitor the region at a regular interval in order to reduce the wildland fire-induced risk.
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 | 0.001 | 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 | 0.001 | 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".