Evaluating landsat time series of above-ground biomass to monitor boreal forest recovery following different types of disturbance
Bibliographic record
Abstract
Global changes are exerting uncertain effects on boreal forest dynamics, highlighting the need for accurate methods to monitor above-ground biomass (AGB). Numerous studies have developed AGB estimation models based on Landsat reflectance and then applied these models across time series. However, only few studies provide an accuracy assessment of the resulting AGB time series using field plot data, limiting the ability to evaluate their applicability and highlight potential constraints. We developed AGB maps using a two-phase approach combining wall-to-wall airborne LiDAR point cloud data, winter and summer Landsat composites time series and Unet convolutional neural networks. This 36 years AGB time series (1985–2020) was evaluated using over 3,000 permanent sample plots with periodic measurement. The accuracy assessment of the produced maps indicates an R2 of 0.50 and RMSE of 33 t/ha. Analyses of dynamics after severe disturbance (harvesting and fires) reveal a close similarity in trajectories between permanent sample plots and predicted AGB values (Pearson correlation of 0.93). The maps enabled identifying stands where recovery failed, highlighting potential of this approach for monitoring disturbance recovery. When comparing our method to NBR spectral recovery, we found that Unet-based AGB provided a more realistic approach for monitoring structural recovery.
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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 | 0.001 |
| 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) | 0.000 | 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".