Multi-scale Remote Assessment of Phenology of Photosynthesis and Productivity of Northern Forests
Bibliographic record
Abstract
Northern trees undergo seasonal regulation of photosynthetic activity in response to environmental conditions. Remote sensing provides a powerful tool to assess photosynthetic phenology at the global scale. However, traditional “greenness” based vegetation indices, like the normalized difference vegetation index (NDVI) which is sensitive to chlorophyll content, are often limited in monitoring the phenology of evergreen conifers due to the retention of green foliage year-round. Therefore, the main goal of my thesis is to evaluate the ability of carotenoid sensitive vegetation indices, photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI), for monitoring the phenology of photosynthesis in evergreen conifers and deciduous trees. This study was conducted at two long-term carbon monitoring forest stands at the Turkey Point Observatory in Ontario, Canada, representing an evergreen forest and a mixed deciduous forest. First, I characterized the energy partitioning of photochemical and photoprotective processes and photosynthetic pigment composition in eastern white pine, red maple and white oak to understand the photosynthetic mechanisms reflected by NDVI, PRI and CCI at the leaf-scale. In deciduous trees, NDVI adequately reflected seasonal variation of photosynthetic activity and pigments. In pine, NDVI was unable to represent phenology. In contrast, PRI and CCI reflected seasonal variation in carotenoid pigments to represent photosynthetic phenology in both deciduous and evergreen trees. I then further evaluated NDVI, PRI and CCI as proxies of photosynthetic parameters at the canopy-scale and validated using leaf-scale measurements. NDVI and PRI were confirmed to be good indicators of the fraction of absorbed photosynthetically active radiation (ƒAPAR) and photosynthetic efficiency (ɛ), respectively. CCI was revealed to be a good indicator of both photosynthesis and ɛ. Finally, I parameterized a light-use efficiency (LUE) model with PRI and CCI as proxies of ɛ and compared model performance with a meteorological-based LUE model and a land surface model. The PRI- and CCI-LUE models demonstrated improved performance for reflecting the timing of phenology of GPP. Together, these results show that PRI and CCI reflect seasonal variation of carotenoid pigments and photoprotection in both evergreen and deciduous trees and are good proxies of photosynthetic activity for parameterizing LUE models to improve the monitoring of photosynthetic phenology.
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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 | 0.000 | 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 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".