Evaluating the sensitivity of wetlands to climate change with remote sensing techniques
Bibliographic record
Abstract
Abstract Wetlands are valuable ecosystems that provide many valuable services, yet many of these important ecosystems are at risk because of current trends in climate change. The Prairie Pothole Region (PPR) in the upper‐midwest of the United States and south‐central Canada, characterized by glacially sculpted landscapes and abundant wetlands, is one such vulnerable region. According to regional/global climate model predictions, drought occurrence will increase in the PPR region through the 21st century and thus will probably cause the amount of water in wetlands to decline. Water surface area (WSA) of Kidder County, ND, from 1984–2011 was measured by classifying TM/ETM+(Landsat Thematic Mapper / Enhanced Thematic Mapper Plus) images through the modified normalized difference water index. We then developed a linear model based on the WSA of these wetlands and historical climate data and used this to determine the wetland sensitivity to climate change and predict future wetlands WSA in the PPR. Our model based on Palmer drought severity index (PDSI) of the current year (PDSI t − 0 ) and of the previous two years (PDSI t − 2 ) can explain 79% of the annual wetland WSA variance, suggesting a high sensitivity of wetlands to drought/climate change. We also predicted the PPR wetlands WSA in the 21st century under A1B scenario (a mid‐carbon emission scenario) using simulated PDSI based on Intergovernmental Panel on Climate Change AR4 22‐model ensemble climate. According to our prediction, the WSA of the PPR wetlands will decrease to less than half of the baseline WSA (defined as the mean wetlands WSA of the 2000s) by the mid of the 21st century, and to less than one‐third by the 2080s, and will then slightly increase in the 2090s. This considerable future wetland loss caused only by climate change provides important implication to future wetland management and climate adaptation policy. Copyright © 2012 John Wiley & Sons, Ltd.
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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".