Wetland mapping with LiDAR derivatives, SAR polarimetric decompositions, and LiDAR–SAR fusion using a random forest classifier
Bibliographic record
Abstract
In this paper, we assess the use of Random Forest (RF) for mapping land cover classes within Mer Bleue bog, a large northern peatland in southeastern Ontario near Ottawa, Canada, using Synthetic Aperture Radar (SAR) and airborne Light Detection and Ranging (LiDAR). Not only has RF been shown to improve classification accuracies over more traditional classifiers, but it also provides useful information on the statistical importance of individual input image bands for land cover classification. Our specific objectives in this study were to: (i) assess the robustness of a RF approach to northern peatland classification; (ii) examine variable importance resulting from the RF classifications to identify which imagery types, derivatives, and analysis scales are most useful for mapping different classes of northern peatlands; (iii) assess if fusion of different LiDAR and SAR variables can improve classification accuracies at Mer Bleue; and (iv) assess physical interpretability of the multisensor image types and derivatives with respect to biophysical attributes associated with peatland classes. Our results show that the fusion of SAR with LiDAR imagery and derivatives at this study site did not provide additional classification accuracy over the use of LiDAR derivatives alone. Nevertheless, the RF-based approach presented here has strong potential to improve mapping and imagery classification of wetlands and may also help researchers and practitioners improve information extraction and land cover classification in other application areas benefitting from large volumes of multi-sensor imagery.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".