Assessing the impact of N-fertilization on biochemical composition and biomass of a Douglas-fir canopy—A remote sensing approach
Bibliographic record
Abstract
Vegetation biochemistry is a critical driver of the forest carbon and water cycle and the fluxes between the land surface and the atmosphere. As result, monitoring biochemistry is a key to improving our estimates of the terrestrial carbon and energy budget. While destructive sampling techniques have been widely applied to determine nutrient content in foliage, scaling of these measurements to the stand and landscape is challenging. As an alternative to traditional field-based approaches, optical remote sensing is a powerful technique for sampling biochemical constituents in a spatially continuous fashion. Remote sensing of biochemical constituents is based on the understanding that leaf biochemistry is closely linked to absorption and reflectance properties in characteristic, often spectrally narrow, wavebands. Spectral absorption features can be identified to characterize and quantify biochemical properties at the leaf, stand and landscape level. At the same time, Light Detection and Ranging (LiDAR) remote sensing can allow inference about the impact of leaf biochemistry on tree growth and canopy structure. In this study, we report the effect of nitrogen-fertilization of a Douglas-fir dominated forest on Vancouver Island, British Columbia, Canada using active and passive remote sensing techniques. Leaf pigment concentrations were estimated from inversion of a canopy reflectance model (PROSAIL) and canopy nitrogen (N) was inferred from an airborne imaging spectrometer (AVIRIS). The impact of leaf biochemistry on canopy structure and tree growth was then investigated using a temporal sequence of LiDAR data acquired two years before, and after, the fertilization treatment. Results indicate that while fertilization had a significant impact on canopy pigment concentrations, it did not impact canopy nitrogen. A notable increase in tree growth was found for younger stands of less than 15 m of height, but not for more mature stand with taller trees. Fertilization had no immediate impact on canopy density measured from LiDAR derived leaf area and canopy volume. The use of advanced remote sensing tools and techniques such as those demonstrated in this study can be a powerful addition to ongoing efforts to model carbon and water fluxes throughout the landscape.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".