Parcel-based classification of agricultural crops via multitemporal Landsat imagery for monitoring habitat availability of western burrowing owls in the Imperial Valley agro-ecosystem
Bibliographic record
Abstract
Agricultural production has a large impact upon the sustainability of wildlife populations. Certain species decline in the presence of intensive agriculture, while others thrive. Thus, quantifying changes in habitat within agricultural systems is paramount to understanding the underlying ecological mechanisms influencing species' range shifts and may facilitate the development of management strategies for sensitive wildlife species that depend on agricultural systems. Remotely sensed data can provide an efficient means to assess and monitor agricultural crop dynamics across large spatial extents. The objective of this study was to classify field-level agricultural crop types via a time series of Landsat imagery across the 3 620 000 ha agro-ecosystem in the Imperial Valley in California. The primary impetus was to generate data to characterize short-term changes in habitat for the western burrowing owl (Athene cunicularia), a species of concern that has an affinity for agricultural systems. The parcel-based classification method attained overall accuracies of 84% and 69% for level II (3 crop groups) and level III (31 crop types) agricultural crops, respectively. Given the large number of crop categories classified, these accuracies were quite high, especially when compared with existing crop type classifications across the region (e.g., the National Cropland Data Layer). These results suggest that the crop type classifications presented herein could potentially be used to evaluate owl demographic and space use parameters in light of shifting crop patterns across the study area.
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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 | 0.001 |
| 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".