A Combined Approach for Monitoring Monthly Surface Water/Ice Dynamics of Lesser Slave Lake Via Earth Observation Data
Bibliographic record
Abstract
Surface water/ice dynamic monitoring is crucial for many purposes, such as water resource management, agriculture, climate change, drought, and flood forecasting. New advances in remote sensing satellite data have made it possible to monitor the surface water/ice dynamics both spatially and temporally. However, there are many challenges when using these data, such as the availability of valid imagery, cloud contamination issues for Landsat-8, and sensitivity of Sentinel-1 C-band to wind speed, topography, and others. A combined methodology using Landsat-8 and Sentinel-1 Synthetic Aperture Radar (SAR) data was proposed to create monthly change maps at 30 m spatial resolution for the Lesser Slave Lake in Alberta, Canada, for the period 2017-2020. The potentials of multi-spectral indices for Landsat-8, such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified NDWI (MNDWI) as well as the Sentinel-1 SAR backscattering coefficients (VV-VH) and Normalized Difference Polarized Index (NDPI) for separating water/ice from the land were investigated. The results obtained from satellite data with historical discharge and water level measurements for the lake were compared. Furthermore, the results show that the MNDWI and VH are the most effective indices for creating the change maps. The overall accuracies achieved for MNDWI and VH are 92.10% and 68.86% for cold months and 99.88% and 98.49% for warm months, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".