Driving an ecosystem simulation model with spatial estimates of LAI
Bibliographic record
Abstract
Ground and remotely sensed data were collected between 1994 and 1998 as part of the BOReal Ecosystem-Atmosphere Study (BOREAS) and Boreal Ecosystem Research and Monitoring Sites (BERMS) initiatives in northern Canada. A pilot investigation 'optimised' the FOREST-BGC (Bio Geochemical Cycling) ESM to obtain accurate estimates of NPP for a number of BOREAS sites and ascertained model sensitivity to input variables. Subsequently, FOREST-BGC was 'automated' using remotely sensed estimates of leaf area index (LAI) from the Landsat Thematic Mapper (TM) sensor, in order to produce a 20 km2 map of NPP. The pilot study emphasised the need for accurate spatial estimates of LAI. Consequently, three refinements were investigated with the aim of maximising the accuracy with which remotely sensed data could be used to estimate boreal forest LAI: First, the joint issues of scale and the choice of an optimum sampling unit for forested landscapes were investigated through the development of a new technique for the partitioning of remotely sensed images into relatively homogeneous 'areal sampling units' (ASU). Second, three alternative methods for producing spatially-extensive estimates of LAI were explored: Aspatial regression, cokriging and conditional simulation. Third, the potential of using radiation acquired by the Advanced Very High Resolution Radiometer satellite sensor was assessed by investigating the relationship between LAI and several spectral vegetation indices. The final phase of this research explored the impact of future climates on the carbon budget of the boreal forest. It was concluded that driving FOREST-BGC with accurate spatial estimates of LAI derived from remotely sensed data is a powerful tool with which to gain a quantitative understanding of current and future biogeochemical cycling through the boreal forest ecosystem.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".