Disentangling physical and biological drivers of optical signals for improved monitoring of evergreen needleleaf photosynthesis
Bibliographic record
Abstract
The largest source of uncertainty in global climate models is terrestrial carbon cycle feed- backs. One of the most important but most poorly understood vegetation types in the global carbon cycle is evergreen needleleaf forests (ENFs). To address this challenge, a growing appreciation for the stress physiology of photosynthesis has inspired emerging techniques to detect ENF photosynthetic activity with optical signals. This includes the use of solar- induced chlorophyll fluorescence (SIF), a small light signal emitted by plants during the photosynthetic process. SIF has shown a marked improvement over traditional reflectance- based vegetation indices in tracking ENF photosynthesis. However, SIF, as well as other optical signals, in ENF are complicated by photon-plant interactions over complex canopy structures (physical) and unique adaptations to deal with the seasonal stress of winter while retaining their needles (biological).In this dissertation, we identify the physical and biological drivers of optical signals in ENF and connect remote sensing observations with physiological processes to improve monitoring of evergreen needleleaf photosynthesis. In Chapter 2, we provide a broad overview for non-specialists of the biological basis for using optical signals to track evergreen needleleaf photosynthesis. We then explore these topics in more detail by using tower-based remote sensing data across four ENF sites which span the climatic gradient experienced by ENF (details in Chapter 3). In Chapters 4 and 5 we zoom in to a single site in Canada and explore the temporal dynamics of different optical metrics and their biological underpinnings. In Chapter 6 we then show how to combine multiple metrics across multiple sites to improve predictions of forest carbon uptake.Ultimately this work advances our understanding of ENF photosynthesis and our ability to predict the fate of ENFs in a changing climate. Future work will help scale and integrate the understandings gleaned in this dissertation to satellite and modeling frameworks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".