A comparison of existing surficial hydrological data layers in a low-relief forested Ontario landscape with those derived from a LiDAR DEM
Bibliographic record
Abstract
The current provincial-extent digital elevation model (DEM) and corresponding hydrological maps for Ontario have been produced using traditional photogrammetry and aerial photograph interpretation. This process is labour-intensive and requires visual interpretation of stereo image pairs. The ground surface and small hydrological features may be inaccurately delineated in areas where vegetation is dense or the ground is otherwise shielded from aerial view. In an effort to improve and automate delineation of hydrological features, we examined the behaviour and final products of the D8 flowrouting algorithm in 2 software environments (TAS and TauDEM for ArcGIS) operating on a high spatial resolution DEM derived using canopy-penetrating light detection and ranging (LiDAR) technology in a pilot study in the Romeo Malette Forest (41.25°N, 81.50°W). Filtered LiDAR data points (5-m spacing) were interpolated using IDW, TIN, and splines, each resulting in a 2.5-m spatial resolution DEM. Results demonstrate improved realism in the characterization of surficial hydrology by LIDAR derived products as compared to applying identical algorithms on existing coarser provincial data. Benefits include the ability to represent streams of lower Strahler order to define crisp watershed boundaries, and the more accurate identification of local depressions that form potentially wet sites. This approach identifies wet sites that should be avoided during forest operations (e.g., skidder traffic) and can provide additional information for trail layout, road planning, and water crossings. By increasing the number of uses of LiDAR, the capital investment in these data becomes increasingly palatable for forest companies interested in obtaining detailed plans of their forest holdings. Key words: LiDAR, DEM, OBM, spatial resolution, interpolation, Strahler stream order, flow routing, topographic wetness
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".