Mapping coastal Great Lakes wetlands and adjacent land use through hybrid optical-infrared and radar image classification techniques
Bibliographic record
Abstract
In the U.S., the National Wetland Inventory (NWI) is the most contiguous and current wetland map available, yet it lacks information on lands adjacent to wetlands and the distribution of invasive plants. Existing Canadian maps are comprised of a mosaic of mapping techniques, sources, and resolutions. A consistent baseline map is needed to monitor change in coastal ecosystems. Short falls in long-term monitoring is in part caused by reliance on dated, static, and inconsistent maps. Use of SOLEC or GLEI indicators is impeded by limitations of current maps, impacting the ability to monitor and detect effects from significant wetlands stressors; urban development and invasive plant species. Current work is underway to produce an international and contemporary baseline map for the Great Lakes Basin. Due to the complexity of wetland ecosystems, detection of species and extent as well as adjacent land use can be accomplished using sensor fusion approach. Synthetic Aperture Radar (SAR) is sensitive to flood condition as well as structure and biomass. Optical sensors, such as Landsat TM, are complementary in the classification and monitoring of wetland ecosystems. Previous research demonstrated the capability of ALOS PALSAR L-band data for detecting and mapping invasive Phragmites australis. The international wetlands map is being produced from a fusion of PALSAR and Landsat data and aims at detection of large stands of problematic plant species such Phragmites australis and Typha spp. A Random Forests classifier is used to create a land cover map through the integration of field and air photo interpreted data with underling sensor fusion data. The Lake Michigan map is complete and is being evaluated for accuracy through randomly selected field and air photo interpreted validation data. The basin wide maps will provide the first ever international Great Lakes coastal land cover map suitable for coastal wetland assessment and management.
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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 | 0.000 |
| 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 | 0.001 | 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".