Comprehensive Landsat-Based Analysis of Long-Term Surface Water Dynamics over Wetlands and Waterbodies in North America
Bibliographic record
Abstract
Wetlands are considered one of the most valuable ecosystems around the world and provide numerous environmental services, including water purification, flood protection, and habitat for a variety of species.Wetlands loss is an increasing trend due to anthropogenic activities and natural processes.As such, spatial knowledge regarding the extent and dynamics of surface water is demanding for wetland conservation and protection.The Landsat program, with five decades of historical Earth observation data, has a unique advantage for monitoring wetland surface water changes and dynamics with 30 m spatial resolution.We monitored 266 Ramsar wetland sites in North America for the past 40 years using the open-access Landsat data within the Google Earth Engine cloud computing platform.Landsat Collection 2 Level-2 surface reflectance products were preprocessed and cloudscreened, and a time series of spectral bands and indices were created.The unsupervised Dynamic Surface Water Extent method classified each image into water classes with different confidence levels.An average overall agreement of 92,97% and an average F-score of 96.31% were achieved in this study.Water occurrence maps, in addition to inundation class and change maps, were created for the entire North America, and quantified spatial information was calculated for Ramsar wetland sites.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".