Supervised wetland classification using high spatial resolution optical, SAR, and LiDAR imagery
Bibliographic record
Abstract
Wetlands are among the most valuable natural resources, being highly beneficial to both the environment and humans. Therefore, it is very important to map and monitor wetlands. Although various remote sensing datasets, including optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR) imagery, have been widely applied to classify wetlands, it is still required to discuss the advantages/limitations of each of these datasets and suggest the best remote sensing methodology for wetland mapping. Thus, the Terra Nova National Park, located in Newfoundland, Canada, was initially selected as the study area to develop a supervised classification method along with object-based image analysis. To this end, different remote sensing-based scenarios were investigated using individual optical, SAR, and LiDAR datasets, as well as their various combinations. In addition, for achieving the highest accuracy, the effects of segmentation scales and the tuning parameters of the random forest (RF) classifier were examined. The results showed that a combination of optical, SAR, and LiDAR images with the segmentation scale of 150, the RF depth of 20, and the RF minimum sample number of 5 provided the highest classification accuracy with the overall accuracy of 87.2%. Moreover, based on the results, approximately 21% and 79% of the study area are covered by wetlands and nonwetlands, respectively. The proposed methodology shows an optimum scenario for future wetland classification tasks and can assist stakeholders in the effective management of wetlands and establishment of necessary policies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".