Improving sample-based National Forest Inventory estimates of tree cover using Landsat-derived land cover data as auxiliary information
Bibliographic record
Abstract
In Canada, satellite-derived data are available as National Terrestrial Ecosystem Monitoring System (NTEMS) products, which can be used as auxiliary information to improve sample-based National Forest Inventory (NFI) estimates. This study explored statistical approaches of using these satellite-derived data to improve vegetated tree (VT) cover estimate in the Atlantic Maritime Ecozone. First, a model-assisted regression estimator with beta regression (MA beta ) was evaluated using simulated population datasets. Then, the efficiency of the MA beta estimator was compared to a design-based ratio estimator (DB) in two scenarios: one with temporally matched survey and auxiliary data, and another with temporally unmatched survey and auxiliary data. The assisting model was also used to generate model-based bootstrap aggregate annual estimates (Mb BAE ) of VT cover proportion to explore temporal trends and assess sensitivity to disturbances during 2007–2017. Results showed that the MA beta estimator provided nearly unbiased estimates with a coverage rate of about 95% (when n ≥ 100). The MA beta estimates were more precise than the DB estimates, especially when survey and auxiliary data were temporally matched (RE = 3.04 with g-weights, RE = 3.25 without g-weights). Moreover, the MB BAE -based annual estimates of VT cover proportion indicated that stand-replacing disturbances drive VT cover dynamics in the ecozone. The results suggest that incorporating remote sensing based, independently derived, wall-to-wall data products can improve the accuracy and precision of Canada's NFI, aiding in monitoring forest resources at various scales.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".