Separating Crested Wheatgrass Using Field Hyperspectral Data in the Native Prairie of Southwestern Saskatchewan
Bibliographic record
Abstract
Crested Wheatgrass (Agropyron cristatum) is an introduced invasive grass species in North American grasslands. It was initially seeded to increase the grazing duration, but is now threatening native grassland biodiversity. Effective monitoring of Crested Wheat-grass requires robust remote sensing methods, yet prior studies relying on multispectral satellite data have faced limitations due to spectral similarity with co-occurring vegetation. In this study, we investigated which biophysical and spectral properties (hyperspectral and simulated multispectral) distinguish Crested Wheatgrass from native grasses. We further assessed whether spectral indices, linked to biophysical traits, could distinguish Crested Wheatgrass from native grasses. We collected field data of hyperspectral reflectance, leaf area index (LAI), biomass, and vegetation cover. We found that the differences between Creased Wheatgrass and native grasses are significant for many biophysical properties and spectral features. Grass cover, height, LAI, and biomass (grass, total and dead) are much higher for Crested Wheat grass sites, while bare ground cover is lower compared to native grasses. Hyperspectral data revealed distinct lower reflectance for Crested Wheat grass in visible and shortwave infrared regions (SWIR) compared to native grasses, which might be driven by differences in photosynthetic pigments and moisture content. SWIR spectral indices for hyperspectral data, SWIR and visible spectral indices for simulated Sentinel-2A data, and visible spectral indices for PlanetScope SuperDove data discriminated Crested Wheatgrass from native grasses. These findings advance invasive grass monitoring by shifting the focus from phenology to biophysical properties based detection, supporting management of Crested Wheatgrass and restoration of native grassland ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".