Measuring Spatial and Vertical Heterogeneity of Grasslands Using Remote Sensing Techniques
Why this work is in the frame
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Bibliographic record
Abstract
Grassland heterogeneity, defined by its components of spatial pattern, vertical structure, and species composition, is one of the most important indicators of prairie habitat. Maintaining grassland under conservation without disturbance may result in homogeneity at multiple spatial scales that could reduce wildlife diversity as a consequence. Therefore, monitoring grassland conditions that contribute to diversity can be critical for wildlife habitat and ecological integrity. Remote sensing, with multi-spatial, multi-spectral, and multi-temporal resolutions plus newly developed analytical techniques, provides a potential tool for measuring grassland heterogeneity under different management regimes quickly, efficiently, and at low cost. The objectives of this study were 1) to evaluate the heterogeneity of grassland under grazing and conservation management practices spatially and vertically, and 2) to investigate the feasibility of using remotely sensed data to measure grassland heterogeneity. The study area was Grasslands National Park of Canada and its surrounding pastures. Field data were collected in the 1999 growing season by measuring the grassland vertical profile in a fixed spatial array. A Landsat Enhanced Thematic Mapper Plus (ETM+) image was acquired for the same year. A grey level co-occurrence matrix (GLCM) texture analysis was applied to the Landsat ETM+ imagery to compare the grasslands under grazing and those under the conservation practice. The results derived from field measurement show that the variation of vertical structures of grasslands differ significantly under grazing and conservation management regimes. Optical remote sensing data could detect the spatial variation of grasslands under these two management practices. Texture analysis is effective at 15 m resolution, which confirmed other studies that grassland heterogeneity is at about 15 meter.
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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 it