Spectral remote sensing reveals forest structural characteristics resilient to spruce budworm infestations
Bibliographic record
Abstract
• Spectral indexes derived from Landsat can capture forest resilience to infestations. • Pre-disturbance forest structure modulates resilience to spruce budworm infestations. • Infestation impact is lower in shorter, sparser, and lower basal area stands. • Recover is faster in greenness, followed by structure and vegetation water content. • Resilience quantification can be operationalized to inform management strategies. Forests are increasingly influence by natural disturbance, which are intensifying under climate change, threatening the sustainability of natural resources. Resilience is a central component in these dynamics, defined as the ability of forests to recover from a disturbance and return to a pre-disturbed state. Recent conceptual frameworks describe resilience as a combination of disturbance impact and recovery rate offering a more unified and quantitative approach. However, these frameworks often do not consider how forest characteristics and their spatio-temporal variability shape resilience outcomes. In this study, we leveraged three decades of Landsat time series data to quantify forest resilience to spruce budworm (SBW) infestations, a major defoliator of fir and spruce forests in Quebec, Canada. We applied a two-stage clustering analysis to investigate how pre-disturbance forest structures, including canopy height, cover, basal area, cumulative defoliation severity, and age influence resilience. Resilience was quantified via impact and recovery rate of three key spectral indexes: Green Normalized Vegetation Index (GNDVI, greenness), Moisture Stress Index (MSI, vegetation water content), and Normalized Burn Ratio (NBR, structural characteristics) following the 2006 outbreak. Our results revealed that stands with lower basal area, sparser, and shorter canopies were more resilience to SBW over time. Greenness recovered most rapidly, followed by structural development and vegetation water content. Ultimately, stands showed greater resilient in greenness and structural development than vegetation water content throughout the infestation. These findings advance empirical understanding of forest resilience and can inform management strategies to maintain and enhance Canada’s boreal forest resilience to increasing SBW pressure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".