Discriminating Native and Nonnative Grasses in the Dry Mixedgrass Prairie With MODIS NDVI Time Series
Bibliographic record
Abstract
Separating native grasses from tame pastures is an important mapping exercise that assists in the assessment of biodiversity, delineation of species' habitat, and appraisal of rangeland health. However, native grasslands (primarily naturally occurring species) and tame pastures (primarily nonnative grasses planted for hay, pasture, or seed) are spectrally similar and therefore difficult to differentiate with traditional remote sensing techniques and with air-photo interpretation. We used seasonal profiles of the normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments to examine the separability of native grasslands and tame pastures (where both types employ the C3 photosynthetic pathway) in the Dry Mixedgrass natural subregion of Alberta, Canada. The two classes were found to have different rates of spring green up at the pixel level that allowed for separation with a simple linear discriminant function. We achieved an overall accuracy of 73% (n = 100 independent test cases) with the MODIS time series-a statistically significant improvement of the photo-interpretation-based Grassland Vegetation Inventory (52%): the current standard for vegetation information in Alberta's agricultural zone. We also found that the multitemporal method was able to select dates for single-date classifications that provided relatively high classification accuracies (up to 71% overall). In addition to achieving higher levels of overall accuracy than more conventional methods, the MODIS time series produced much more reliable identification of abandoned pastures: formerly planted rangelands that lack many of the visual cues used by photo interpreters.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".