Monitoring biological heterogeneity in a northern mixed prairie using hierarchical remote sensing methods
Bibliographic record
Abstract
Heterogeneity, the degree of dissimilarity, is one of the most important and widely applicable concepts in ecology.It is highly related to ecosystem conditions and features wildlife habitat.Grasslands have been described as inherently heterogeneous because their composition and productivity are highly variable across multiple scales.Therefore, biological heterogeneity can be an indicator of ecosystem health.The mixed prairie in Canada, characterized by its semiarid environment, sparse canopy, and plant litter, offers a challenging region for environmental research using remote sensing techniques.This thesis dwells with the plant canopy heterogeneity of the mixed prairie ecosystem in the Grasslands National Park (GNP) and surrounding pastures by combining field biological parameters (e.g., grass cover, leaf area index, and biomass), field collected hyperspectral data, and hierarchical resolution satellite imagery.The thesis scrutinized four aspects of heterogeneity study: the importance of scale in grassland research, relationships between biological parameters and remotely collected data, methodology of measuring biological heterogeneity, and the influence of climatic variation on grasslands biological heterogeneity.First, the importance of scale is examined by applying the semivariogram analysis on field collected hyperspectral and biophysical data.Results indicate that 15 -20 m should be the appropriate resolution when variations of biological parameters and canopy reflectance are sampled.Therefore, it is reasonable to use RADARSAT-1, Landsat TM, and SPOT images, whose resolutions are around 20 m, to assess the variation of biological heterogeneity.Second, the efficiency of vegetation indices derived from SPOT 4 and Landsat 5 TM images in monitoring the northern mixed prairie health was examined using Pearson's correlation and stepwise also like to thank other members of my advisory committee, Dr. Yuguang Bai, Dr. Scott Bell, Dr. Lawrence
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 | 0.000 | 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 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".