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Record W2921754301 · doi:10.11575/prism/31916

Remote Sensing of Wildland Fire-induced Risk Assessment Framework

2017· article· en· W2921754301 on OpenAlexfundaboutno aff
Quazi K. Hassan, M. Razu Ahmed, Khan Rubayet Rahaman

Bibliographic record

VenueOpen MIND · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersPublic Safety CanadaDigitalGlobe FoundationNational Foundation for Infectious Diseases
KeywordsRemote sensingRisk assessmentEnvironmental scienceComputer scienceEnvironmental resource managementRisk analysis (engineering)BusinessGeographyComputer security

Abstract

fetched live from OpenAlex

Wildland fire is one of the critical natural hazards that pose a significant threat to the communities located in the vicinity of forested/vegetated areas. In this report, our overall goal was to use very high spatial resolution (0.5-2.4m) satellite images to develop wildland fire-induced risk framework. We considered two extreme fire events, such as the 2016 HRF over Fort McMurray, and 2011 Lesser Slave Lake fire in Alberta. 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 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 National Fire Information Database (NFID); and found strong linear relationships (i.e., r2-value of 0.97 with a slope of 0.97 for the 2016 HRF over Fort McMurray; and 378 from satellite image vs. 407 from 378 from satellite image vs. 407 from NFID system for the 2011 Lesser Slave Lake fire). Upon delineating the WUI and its associated buffer zones at 10m, 30m, 50m, 70m and 100m distances; we found existence of vegetation within the 30m 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 30m and 70m buffers from the WUI in case of Fort McMurray area, 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. We found that there were still some communities that had the existence of vegetation within the buffer zones; thus such vegetation should be removed and monitored regularly in order to reduce the wildland fire-induced risks.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.321
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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