Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study
Bibliographic record
Abstract
Forest fires play a crucial role in resetting boreal ecosystems and steering ecological succession dynamics. However, the escalating impacts of global climate change are anticipated to increase the frequency, intensity, and size of wildfires, leading to significant economic, ecological, and social consequences. To effectively address fire risk and optimize post-fire management strategies, close monitoring, assessment, and understanding of the spatial heterogeneity of wildfires and their impacts are essential. Remote sensing, with its extensive historical records, provides a cost-effective means to examine wildfires. This study focuses on a significant wildfire event that occurred in May 2016 that made a substantial impact on Fort McMurray, Alberta, Canada. Using the Google Earth Engine (GEE) Platform, Landsat images time series covering pre- and post-fire (2015 to 2023), and land cover maps, we delineated the fire’s extent and conducted a comprehensive assessment of variations in burn severity and subsequent vegetation recovery. The Differenced Normalized Burn Ratio (dNBR) was calculated from Landsat images to measure burn extent, burn severity, and burn spatial variability. The Normalized Difference Vegetation Index (NDVI) was used for post-fire vegetation recovery analysis. Our findings reveal that 53.5% of the burn area experienced fire damage. Swamps and forests experienced the most intense burns (dNBR of 0.55 for swamps and 0.41 for forests) due to denser vegetation and biomass. Grasslands had moderate burn severity (dNBR of 0.281). In contrast, bogs, marshes, and fens showed lower dNBR values (0.15, 0.12, and −0.003), indicating low to no burns, likely due to their wetter conditions acting as natural firebreaks. NDVI changes indicate varying rates of vegetation recovery post-wildfire across different land cover types. In fen and marsh areas, NDVI was initially at 0.66 and 0.65 in 2015, dropped slightly in 2016, but rebounded by 2017, showing resilience. Swamps’ NDVI declined from 0.69 in 2015 to 0.46 in 2016, recovering to 0.72 by 2020. Grasslands’ NDVI dropped from 0.81 to 0.64 in 2016, recovering quickly to 0.80 by 2020. Forests’ NDVI decreased from 0.72 to 0.51 in 2016, with a gradual recovery to 0.67 by 2023, suggesting a slower recovery process. While NDVI values indicate a fast vegetation recovery for most land cover types, a deeper analysis suggests a transitional phase where past forests are now dominated by other vegetation types. The findings suggest that fire management strategies must integrate both immediate response and long-term recovery plans to ensure robust fire prevention and adequate rehabilitation resources for affected areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".