Sensitivity of watershed attributes to spatial resolution and interpolation method of LiDAR DEMs in three distinct landscapes
Bibliographic record
Abstract
Abstract This study investigates scaling relationships of watershed area and stream networks delineated from LiDAR DEMs. The delineations are tested against spatial resolution, including 1, 5, 10, 25, and 50 m, and interpolation method, including Inverse Distance Weighting (IDW), Moving Average (MA), Universal Kriging (UK), Natural Neighbor (NN), and Triangular Irregular Networks (TIN). Study sites include Mosquito Creek, Scotty Creek, and Thomas Brook, representing landscapes with high, low, and moderate change in elevation, respectively. Results show scale‐dependent irregularities in watershed area due to spatial resolution at Thomas Brook and Mosquito Creek. The highest sensitivity of watershed area to spatial resolution occurred at Scotty Creek, due to high incidence of LiDAR sensor measurement error and subtle changes in elevation. Length of drainage networks did not show a scaling relationship with spatial resolution, due to algorithmic complications of the stream initiation threshold. Stream lengths of main channels at Thomas Brook and Mosquito Creek displayed systematic increases in length with increasing spatial resolution, described through an average fractal dimension of 1.059. The scaling relationship between stream length and DEM resolution allows estimation of stream lengths from low‐resolution DEMs in the absence of high‐resolution DEMs. Single stream validation at Thomas Brook showed the 1 m DEM produced the lowest length error and highest spatial accuracy, at 3.7% and 71.3%, respectively. Single stream validation at Mosquito Creek showed the 25 m DEM produced the lowest length error, and the 1 m DEM the highest spatial accuracy, at 0.6% and 61.0%, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".