Influence of Climatic Seasonality on a Survey of Land Use and Cover in the Semi-arid Region
Bibliographic record
Abstract
The dynamics of land use and land cover in watersheds of the Brazilian semi-arid region is not only influenced by human action, but also by the climatic seasonality of the region. Knowledge of the relationship between surveys of land use and land cover using geotechnology and the climatic seasonality of semi-arid regions is necessary. The aim of this study was to map and classify land use and cover in the watershed of the Orós reservoir (WSOR) with the help of geotechnology, and to identify the influence exerted by the climate on variations in the area of each class. The survey of land use and cover was carried out by means of the MAXVER method of classification of images from 2003, 2005, 2008 and 2013 from the LANDSAT 5 and LANDSAT 8 satellites. The areas of each class displayed dynamics influenced not only by human action but also by such factors as climate, topography and plant physiology. Years with high rainfall favoured classes such as thin scrub and dense scrub, with the opposite being seen in years considered as dry, when there was a considerable increase in areas of the anthropogenic class. Changes in the areas are caused by alterations in the deciduous vegetation; with leaf-fall during the dry season, these areas come to have the spectral response of areas with similar characteristics to the anthropogenic class. More-elevated regions favoured the presence of the dense-scrub class due to the microclimate and to the greater difficulty such areas present to human action.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".