A Remote Sensing-Based Method to Assess Water Level Fluctuations in Wetlands in Southern Brazil
Bibliographic record
Abstract
The characterization of water level fluctuations is crucial to explain the hydrological processes that contribute to the maintenance of the structure and function of wetlands. The aim of this study was to develop a method based on remote sensing to characterize and map the water level variation patterns, evapotranspiration, discharge, and rainfall over wetlands in the Gravataí River basin, Rio Grande do Sul (RS), Brazil. For this purpose, ground-based measurements of rainfall, water discharge, and evapotranspiration together with satellite data were used to identify the apparent water level based on the normalized difference water index (NDWI). Our results showed that the variation of the water level followed the rainfall, water discharge, and evapotranspiration seasonal patterns in the region. The NDWI showed similar values to the ground-based data collected 10 days prior to satellite image acquisition. The proposed technique allows for quantifying the pattern of flood pulses, which play an important role for establishing the connectivity between different compartments of wetlands in the study area. We conclude that our methodology based on the use of satellite data and ground measurements was a useful proposition to analyze the water level variation patterns in an area of great importance in terms of environmental degradation and use of agriculture. The information obtained may be used as inputs in hydrologic models, allowing researchers to evaluate the impact, at both local and regional scales, caused by advance of agriculture into natural environments such as wetlands.
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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 | 0.002 | 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".