Wetland Mapping: Application of Supervised Classification Using Random Forest in Wetland Prediction
Bibliographic record
Abstract
In response to the necessity for enhanced wetland inventories, this study aimed to assess effectiveness of employing high spatial resolution SPOT imagery (1.5m resolution by Planet Lab) for classifying and detecting wetlands in Atlin, British Columbia, Canada. Utilizing the Random Forest Classifier, featured for its capability in handling high-dimensional spatial data, the research aims to contribute to the local understanding of wetland status through advanced raster analysis. The application of the Random Forest Classifier yielded an overall classification accuracy of 86%, underscoring the method's applicability for wetland delineation in Atlin. The generated wetland map, featuring a 10m spatial resolution, integrates topographic, vegetative, and textural indices, presenting a valuable tool for assessing the variable importance in wetland classification. Despite its high accuracy, the study acknowledges the irreplaceable value of field assessments for comprehensive wetland evaluation by ecological uniqueness of wetlands. This research not only demonstrates the potential of high-resolution SPOT imagery in environmental monitoring but also encourages further application of machine learning techniques in the preservation and management of critical wetland ecosystems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".