Spectral unmixing of multiple lichen species and underlying substrate
Bibliographic record
Abstract
Cryptogamic covers are a wide range of photoautotrophic plants which synthesize their own food while using sunlight as an energy source. Globally, cryptogrammic covers (such as cyanobacteria, algae, fungi, lichens, and bryophytes) annually uptake about 7% of the net primary production of terrestrial vegetation and account for about half of annual biological terrestrial nitrogen fixation. On the basis of these contributions to global carbon and nitrogen cycling, it is crucial to be able to accurately monitor seasonal and regional patterns of cryptogamic cover distribution and abundance. However, lichen-encrusted rock seldom comprises 100% of the ground cover within a pixel of remote-sensed imagery, and thereby arise challenges in lichen mapping and monitoring. Here we explore spectroscopic methods and spectral mixture analysis (SMA) to overcome the challenges of reflectance spectroscopy-based optical remote sensing detection and characterization of crustose lichen species. One suite of discrete wavelengths (λ1 = {400, 470, 520, 570, 680, 800, 1080, 1120, 1200, 1300, 1470, 1670, 1750, 2132, 2198, 2232 nm}) and two wavelength regions (λ2 = {λ: 800 nm ≤ λ ≤ 1300 nm} and λ3 = {λ: 2000 nm ≤ λ ≤ 2400 nm}) were investigated for their ability to discriminate between substrate and different lichen species. We found that the spectral region 800–1300 nm performed best at lichen-substrate differentiation and interspecial lichen differentiation. Furthermore, measures of central tendency from multiple wavelength regions are superior to most individual wavelength regions, particularly for lichen-rock unmixing.
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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".