Using Landsat Time-Series and LiDAR to Inform Aboveground Forest Biomass Baselines in Northern Minnesota, USA
Bibliographic record
Abstract
The publicly accessible archive of Landsat imagery and increasing regional-scale LiDAR acquisitions offer an opportunity to periodically estimate aboveground forest biomass (AGB) from 1990 to the present to align with the reporting needs of National Greenhouse Gas Inventories (NGHGIs). This study integrated Landsat time-series data, a state-wide LiDAR dataset, and a recent cycle of the national forest inventory (NFI) records in Minnesota, USA, to obtain a spatially explicit inventory of AGB across a large region of space and time back to the 1990 baseline used by the US NGHGI. Pixel-level polynomial models were fit to 6 time-series metrics of Landsat data to obtain fitted predictors that were ultimately coupled with the NFI data in a nonparametric modeling framework to map temporal AGB baselines. Eighteen candidate models, formulated using different combinations of LiDAR and Landsat metrics, revealed that the model using both Landsat and LiDAR metrics consistently performed better than the alternative models. The RMSE of the model using both Landsat and LiDAR was 27.2 Mg ha−1, against 31.39 Mg ha−1 for the model using only LiDAR metrics. We conclude that the fitted Landsat-based model (RMSE = 47.64 Mg ha−1) provides acceptable accuracy for the 1990-baseline mapping of AGB.
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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 | 0.000 | 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) | 0.000 | 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".