Hydrogeomorphic edge detection and delineation of landscape functional units from lidar digital elevation models
Bibliographic record
Abstract
A new method is introduced to delineate hydrogeomorphic elements from light detection and ranging (lidar) digital elevation models. Landscape segmentation is achieved using an edge‐detection procedure to identify boundaries defining rapid changes in the tan αd index of landscape drainage potential. These boundaries define homogenous, functional landscape units that can be classified according to different topographically derived indices such as mean of the expected hydraulic gradient (approximated by tan αd), mean topographic wetness index, and mean ratio of flow path lengths to flow path gradients (L/G). Two case studies are presented in which the new method was applied (1) to map forested wetlands and nonwetland saturation‐prone depressional areas and improve regression models of dissolved organic carbon source areas in the Muskoka‐Haliburton region of south central Ontario and (2) to spatially characterize near‐surface soil moisture patterns along a complex, upland hillslope catena in a small experimental watershed in northwestern Ontario. Both case studies point to the critical role of local drainage conditions and slope geometry in dictating spatial patterns of terrain wetness in complex Boreal Shield landscapes. The results cast uncertainty on the role of upslope contributing area as a first‐order control on terrain wetness in this environment. Nevertheless, the results highlight the strong potential of digital terrain analysis to improve conceptualization of hydrological processes in the Boreal Shield, and they are conceptually consistent with the emerging paradigm of runoff generation in this region. We propose hydrogeomorphic edge detection and classification as a way to improve the characterization of landscape functional units in Boreal Shield watersheds for process‐oriented and model‐based ecohydrological research.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".