Tracking Forest Recovery: Early Indicators of Forest Regeneration Following the 2017 Elephant Hill Wildfire in British Columbia, Canada
Bibliographic record
Abstract
Wildfire is an increasingly prominent disturbance across forested landscapes in British Columbia, yet ecological responses and long-term vegetation recovery remain complex and spatially variable. This study investigates post-fire vegetation recovery following the 2017 Elephant Hill wildfire using Landsat-derived remote sensing data from 2015 to 2022. By analyzing the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI) we assess vegetation change across four time periods: pre-fire, immediate post-fire, two years post-fire, and five years post-fire. Vegetation recovery was defined as achieving at least 80% of pre-fire index values by 2022. Findings indicate that substantial portions of the burn scar remained below it five years after the fire. Slower recovery was often associated with higher initial burn severity, elevation, and climatic conditions. The use of remote sensing offers a unique advantage by enabling consistent, landscape-scale monitoring especially in remote or inaccessible areas while Landsat’s 30-meter spatial resolution facilitates the detection of fine-scale recovery patterns across heterogeneous terrain. These results underscore the importance of multi-temporal satellite imagery in evaluating ecosystem resilience and identifying areas that may benefit from targeted restoration efforts in the context of more frequent and severe wildfires.
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 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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".