An evaluation of DEMs derived from LiDAR and photogrammetry for wetland mapping
Bibliographic record
Abstract
The Ontario Ministry of Natural Resources (OMNR) and Ducks Unlimited Canada (DUC) have been engaged in developing an efficient and accurate methodology for inventorying wetlands. Their progress in this area has demonstrated that Digital Elevation Models (DEMs) are crucial input for wetland identification and boundary delineation. The provincial DEM, however, has known precision limitations in areas of minimal topographic relief that cause considerable mapping error. This study explored whether wetland mapping derived from bare-earth light detection and ranging (LiDAR) data would overcome the limitations of the provincial DEM. An automated wetland mapping approach was applied to the 2 elevation datasets and the results were compared using 2 methods of validation. One hundred aerial-photo-interpreted sample plots were used to quantitatively measure the ability of each source to separate upland from wetland. An overlay of wetland maps created from the 2 DEM sources was then qualitatively assessed to further clarify the magnitude of discrepancy between the 2 mapping sources. The study concluded that LiDAR showed a significant improvement at p = 0.05 over the provincial DEM for mapping wetlands, improving overall mapping accuracy from 76% to 84%. However, an overlay analysis and qualitative assessment showed the magnitude of this reported improvement is greater than was quantified by the accuracy assessment and that an assessment scheme with different sample units may further elucidate this discrepancy. Key words: LiDAR, DEM, wetland, mapping
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".