Monitoring shrub encroachment and its influencing factors in Canadian grasslands with remote sensing
Bibliographic record
Abstract
Worldwide, natural grasslands are threatened by the expansion of unwanted woody plant species. Woody plant encroachment (WPE) has become the second most important process that leads to grassland loss in the Great Plains Biome, affecting the food industry, the economy, and the environment. For grassland management practices to be effective, accurate monitoring of grassland health is important. Remote sensing (RS) can achieve this by offering large-scale coverage, near-real time monitoring, cost-efficiency, consistency, and enhanced visualization. From the literature, I found that there is no universal remotely sensed WPE monitoring framework available. Therefore, the objectives of this dissertation are i) to explore RS approaches for appropriate assessment of shrub encroachment in grasslands, ii) to examine the patterns and trends of shrub encroachment, and iii) to investigate the integration of RS approaches into grassland management for shrub encroachment control and grassland health. Study areas include native prairie regions in the Moist Mixed and Cypress Upland grassland ecoregions of Saskatchewan (SK). Field data (vegetation cover, plant area index, soil moisture, vegetation reflectance, and biomass) were used to identify the optimal season and spectral regions to estimate shrub cover in grasslands and spectrally discriminate common shrub species of SK. Aerial imagery was used to map regional shrub cover and generalized least square models (GLM) were used to identify topo-edaphic and anthropogenic factors that relate to existing shrub cover. Our data showed that spring was the best season to distinguish shrubs from grass while each season had a different spectral region more correlated to shrub cover. Summer was the best season to spectrally discriminate western snowberry from wolfwillow. With an object-based approach to classify shrub cover from aerial images, we obtained an overall accuracy between 91%-95%. Overall, we found that shrub cover is spectrally not detectable when its cover is between 10%-25% of an image pixel. Our GLM model results showed that road closeness, medium-high grazing, and haying absence were significantly positively related with shrub cover. Furthermore, loam, flat, upland areas further away from waterbodies and wetlands favor higher shrub cover. This research can be the stepping stone for achieving long-term resilience and sustainability of native grassland species and their habitats by better understanding the interaction of local factors on WPE expansion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".