Spatial and temporal modelling of aboveground carbon stocks using Landsat TM and ETM+ for a subboreal forest
Bibliographic record
Abstract
Forest carbon (C) stocks and sequestration have become an important management consideration for countries with large forested regions. A series of Landsat Thematic Mapper (TM) images was obtained for the Aleza Lake Research Forest (ALRF) in subboreal British Columbia. Plot-based aboveground biomass and woody debris C stocks measured in 2003 and 2004 were related through regression analysis to TM and spatially explicit forest cover information. Two empirical models were developed, namely a biomass C regression model (BCRM) and a woody debris C regression model (WDCRM), with r2 values of 0.67 and 0.64, respectively. Uncertainties in C stock estimates were determined using a Monte Carlo uncertainty analysis. In 2003, the total C stocks in biomass and woody debris over the 6034 ha area were 588 ± 7 and 77 ± 2 kt, respectively. During the time period from 1992 to 2003, forest harvesting operations accounted for a loss of 39.9 ± 2.6 kt C, and remaining areas accounted for a gain of 31.3 ± 10.3 kt C. Therefore, there was a small and nonsignificant loss of 8.7 ± 10.6 kt C over this 11 year interval for the ALRF, implying that aboveground carbon stock losses associated with clearcuts were offset by carbon stock gains achieved through forest growth.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".