Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America
Bibliographic record
Abstract
We report estimates of the amount, distribution, and uncertainty of aboveground biomass (AGB) of the different ecoregions and forest land cover classes within the North American boreal forest, analyze the factors driving the error estimates, and compare our estimates with other reported values. A three-phase sampling strategy was used (i) to tie ground plot AGB to airborne profiling lidar metrics and (ii) to link the airborne estimates of AGB to ICESat-GLAS lidar measurements such that (iii) GLAS could be used as a regional sampling tool. We estimated the AGB of the North American boreal forest at 21.8 Pg, with relative error of 1.9% based on 256 GLAS orbits (229 086 pulses). The distribution of AGB was 46.6% for western Canada, 43.7% for eastern Canada, and 9.7% for Alaska. With a single exception, relative errors were under 4% for the three regions and for the major cover types and under 10% at the ecoregion level. The uncertainties of the estimates were calculated using a variance estimator that accounted for only sampling error, i.e., the variability among GLAS orbital estimates, and airborne to spaceborne regression error, i.e., the uncertainty of the model coefficients. Work is ongoing to develop robust statistical techniques for integrating other sources of error such as ground to air regression error and allometric error. Small ecoregions with limited east–west extents tended to have fewer GLAS orbits and a greater percent sampling error. AGB densities derived from GLAS agreed closely with the estimates derived from both forest inventories (<17%) and a MODIS-based interpolation technique (<26%) for more southern, well-inventoried ecoregions, whereas differences were much greater for unmanaged northern and (or) mountainous ecoregions.
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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 | 0.002 | 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 | 0.001 |
| 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".