Upscaling reflectance information of lichens and mosses using a singularity index: a case study of the Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
Abstract. Assessing moisture contents of lichens and mosses using ground-based high spectral resolution spectrometers (400–2500 nm) offers immense opportunities for a comprehensive monitoring of peatland moisture status by satellite/airborne imagery. This information may be valuable for present and future carbon balance modeling. Previous studies are based upon point measurements of vegetation moisture content and water table position, and therefore a detailed moisture status of entire northern peatlands is not available. Consequently, upscaling ground and remotely sensed data to the desired spatial resolutions is inevitable. This study continues our previous investigation of the impact of various moisture conditions of common sub-Arctic lichen and moss species (i.e., Cladina stellaris, Cladina rangiferina, Dicranum elongatum, and Tomenthypnum nitens) upon the spectral signatures obtained in the Hudson Bay Lowlands, Canada. Upscaling reflectance measurements of the above species were conducted in the field, and reflectance analysis using a singularity index was made, since this study serves as a basis for future aircraft/satellite research. An attempt to upscale current and new spectral reflectance indices developed in our previous studies was made as well. Our findings indicate that the spectral index C. rangiferina is to a lesser amount influenced by scale since it has a small R2 values between the log of the index and the log of the resolution, reduced slopes between the log of the index and the log of the resolution, and similar slopes between log reflectance and log resolution (α) of two wavelengths employed by the index. Future study should focus on concurrent monitoring of moisture variations in lichens and mosses both in situ and from satellite and airborne images, as well as analysis of fractal models in relations to the upscaling experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".