Lichens and mosses moisture content assessment through high‐spectral resolution remote sensing technology: 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 offers immense opportunities for a comprehensive monitoring of peatland moisture status by satellite/airborne imagery. This study investigates the impact of various moisture conditions of common subarctic lichen and moss species upon the spectral signatures obtained. The lichens are Cladina stellaris and Cladina rangiferina , and the mosses are Dicranum elongatum and Tomenthypnum nitens . Reflectance and moisture content measurements of these species were made in a laboratory setting, while maintaining the natural moisture conditions of the samples; once the moisture and spectral measurements were complete, the samples were returned to the field and placed in their natural setting, continuously receiving moisture from precipitation and groundwater and losing water through evaporation and drainage. Changes in reflectance of the visible to shortwave infrared (SWIR) range (400–2500 nm) at various moisture contents were examined, as well as the potential of current spectral reflectance indices to evaluate the plants' moisture contents was examined. Results indicate that the SWIR region is useful in identifying variations in plants moisture conditions, while the unique spectral signatures of the lichens and mosses in the visible and near‐infrared range suggest that these species may be detected by satellite and airborne imagery. Of current spectral indices, the normalized difference infrared index (NDII) was most successful in identifying the above plants' moisture content (details are discussed in the paper). Future study should focus on the development of improved moisture content spectral indices, as well as upscaling reflectance data and spectral indices. Copyright © 2010 John Wiley & Sons, Ltd.
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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".