High accuracy Land Use Land Cover (LULC) maps for detecting agricultural drought effects in rainfed agro-ecosystems in central Mexico
Bibliographic record
Abstract
Abstract Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km 2 ) using Landsat ETM+ images (multispectral resolution of 30 m and 15 m panchromatic), and to process very accurate Land Use Land Cover (LULC) classification to determine with great precision the effects of drought in specific classes. The study area was the Tortugas-Tepezata sub watershed (Moctezuma River), located in the state of Hidalgo in central Mexico. The LULC classification was processed using a new method based on available ancillary information plus analysis of three single date satellite images. The newly developed LULC methodology developed produced overall accuracies ranging from 87.88% to 92.42%. Spectral indices for vegetation and soil/vegetation moisture were used to detect anomalies in vegetation development caused by drought; furthermore, the area of water bodies was measured and compared to detect changes in water availability for irrigated crops. The proposed methodology has the potential to be used as a tool to identify, in detail, the effects of drought in rainfed agricultural lands in developing regions, and it can also be used as a mechanism to prevent and provide relief in the event of droughts.
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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 | 0.002 | 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".