Detection of non-stand replacing disturbances (NSR) using Harmonized Landsat-Sentinel-2 time series
Bibliographic record
Abstract
Non-stand replacing disturbances (NSRs) are events that do not result in complete removal of trees and generally occur at a low intensity over an extended period of time (e.g., insect infestation), or at spatially variable intensities over short time intervals (e.g., windthrow). These disturbances alter the quality and quantity of forest biomass, impacting timber supply and ecosystem services, making them critical to monitor over space and time. The increased accessibility of high frequency revisit, moderate spatial resolution satellite imagery, has led to a subsequent increase in algorithms designed to detect sub-annual change in forested landscapes across broad spatial scales. One such algorithm, the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) has shown promise with sub-annual change detection in temperate forested environments. Here, we evaluate the sensitivity of BEAST to detect NSRs across a range of severity levels and disturbance agents in Central British Columbia (BC), Canada. Moderate resolution satellite time series data were utilized by BEAST to produce rasters of change probability, which were compared to the occurrence, severity, and timing of disturbances as mapped by the annual British Columbia Aerial Overview Survey (BC AOS). Differences in the distributions of BEAST probabilities between agents and levels of severity were then compared to undisturbed pixels. In order to determine the applicability of the algorithm for updating forest inventories, BEAST probability distributions of major NSRs (> 5 % of total AOS disturbed area) were compared between consecutive years of disturbances. Cumulatively, all levels of disturbances had higher and statistically significant (p < 0.05) mean BEAST change probabilities compared with historically undisturbed areas. Additionally, 16 disturbance agents observed in the area had higher statistically significant (p < 0.05) probabilities. All major NSRs showed an upwards and statistically significant (p < 0.05) progression of BEAST probabilities over time corresponding to increases in BC AOS mapped area. The sensitivity of BEAST change probabilities to a wide range of NSR disturbance agents at varying intensities suggests promising opportunities for earlier detection of NSRs to inform continuously updating forest inventories and potentially inform adaptation and mitigation actions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".