Use of Landsat TM and ETM+ to describe intra-season change in vegetation, with consideration for wildlife management
Bibliographic record
Abstract
Many studies have used seasonal differences in multi-temporal Normalized Difference Vegetation Index (NDVI) values to help explain movements of large mammal species such as barren-ground caribou (Rangifer tarandus greenlandicus). These studies, however, have typically relied upon coarse-resolution NDVI information (i.e., 250-1000m). The Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) onboard the Landsat satellites capture 30-m multi-spectral data, but because of the limited satellite overpass schedule, these data are less frequently available and consequently more likely to be contaminated by clouds. I assessed the success of several models containing multiple terrain inputs and vegetation information (derived by maximum likelihood classification of TM data with overall accuracy 77%) to predict NDVI in clouded areas and to map uniform NDVI surfaces. Using these data, I employed change detection techniques to derive the phenological differences of vegetation between images from four months during the growing season of 2001 and related these to seasonal changes for 11 vegetation types in the Greater Besa-Prophet Area of the Muskwa-Kechika Management area in northern British Columbia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".