Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region
Bibliographic record
Abstract
Abstract Global forests are increasingly threatened by disturbance events such as wildfire. Spaceborne Synthetic Aperture Radar (SAR) missions at L‐ (or P‐) band, such as the upcoming NASA ISRO SAR (NISAR), have great potential to advance global mapping of above‐ground biomass (AGB). AGB mapping with SAR is challenging due to lack of available L‐ or P‐ band data, and because SAR data are sensitive to confounding factors such as hydrology and terrain. This study uses recently collected AGB validation site data (AGBV) to produce a 1 ha biomass map about the Great Slave Lake in Canada using SAR data, and reports on NISAR's anticipated performance. This study addresses errors inherent to the representativeness of AGBVs with coarser grid/landscape scale processes by evaluating model performance as data are aggregated over increasingly larger areas (AOAs). Air and spaceborne SAR data were found to be interoperable after processing them according to analysis ready data specifications, improving data availability. Owing to poor model performance at two AGBVs, root‐mean‐square errors (RMSEs) were ∼60 Mg/ha, irrespective of AOA. When instead using NISAR's more lenient assessment criteria, RMSEs decreased to 32, 15, and 21 Mg/ha for the small (∼0.1 ha), medium (∼3.5 ha), and large (∼14 ha) AOA. Thus, AGB mapping in this region appears to benefit significantly from coarser data aggregations than to be used by NISAR's. This approach is practical for identifying a suitable scale of correspondence between the AGBV and SAR data and the landscape‐scale processes and can substantially improve AGB mapping accuracy.
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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 | 0.001 | 0.000 |
| 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 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".