Towards global spaceborne lidar biomass: Developing and applying boreal forest biomass models for ICESat-2 laser altimetry data
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Bibliographic record
Abstract
Space-based laser altimetry has revolutionized our capacity to characterize terrestrial ecosystems through the direct observation of vegetation structure and the terrain beneath it. Data from NASA's ICESat-2 mission provide the first comprehensive look at canopy structure for boreal forests from space-based lidar. The objective of this research was to create ICESat-2 aboveground biomass density (AGBD) models for the global entirety of boreal forests at a 30 m spatial resolution and apply those models to ICESat-2 data from the 2019–2021 period. Although limited in dense canopy, ICESat-2 is the only space-based laser altimeter capable of mapping vegetation in northern latitudes. Along each ICESat-2 orbit track, ground and vegetation height is captured with additional modeling required to characterize biomass. By implementing a similar methodology of estimating AGBD as GEDI, ICESat-2 AGBD estimates can complement GEDI's estimates for a full global accounting of aboveground carbon. Using a suite of field measurements with contemporaneous airborne lidar data over boreal forests, ICESat-2 photons were simulated over many field sites and the impact of two methods of computing relative height (RH) metrics on AGBD at a 30 m along-track spatial resolution were tested; with and without ground photons. AGBD models were developed specifically for ICESat-2 segments having land cover as either Evergreen Needleleaf or Deciduous Broadleaf Trees, whereas a generalized boreal-wide AGBD model was developed for ICESat-2 segments whose land cover was neither. Applying our AGBD models to a set of over 19 million ICESat-2 observations yielded a 30 m along-track AGBD product for the pan-boreal. The ability demonstrated herein to calculate ICESat-2 biomass estimates at a 30 m spatial resolution provides the scientific underpinning for a full, spatially explicit, global accounting of aboveground biomass.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 it