GIS‐evaluation of two slope‐calculation methods regarding their suitability in slope analysis using high‐precision LiDAR digital elevation models
Bibliographic record
Abstract
Abstract Slope is a metric that is essential to describe surface hydrological processes, including overland flow, soil erosion, and sediment transport. Most commercial GIS have built‐in functions to calculate the slope from Digital Elevation Models (DEMs) by means of average neighbourhood methods that are appropriate for coarse‐resolution DEMs. Emergence of high‐resolution DEMs from LiDAR data creates a need to re‐assess the suitability of existing algorithms for calculating slope in hydrological applications. In this study, we investigate the properties of two different slope‐calculation methods: an average‐neighbourhood‐slope (ANS) and a downhill‐slope (DHS) method. Conceptually, the DHS method provides a more intuitive description of surface water‐flow characteristics in an uneven terrain. DEMs of five different types were used to evaluate the methods, namely a 1‐m and 10‐m resolution DEM interpolated from irregular elevation point‐data generated with conventional photogrammetric techniques, and a 1‐m, 5‐m, and 10‐m resolution DEM derived from LiDAR data. The slopes calculated were summarized for the entire watershed, along mapped streams, and within pre‐defined ‘stream buffers’. Slopes generated for the entire watershed with 1‐m resolution LiDAR DEM indicated that the ANS method on an average produced smaller slopes than the DHS method (0·64°). A similar trend was observed in stream buffers, with greatest slope differences (Δ S ) between methods within 20‐m buffers, when the 1‐m LiDAR‐based DEM was used (Δ S = 1·12°). In contrast, the ANS‐calculated slopes along mapped streams were generally larger than those calculated with the DHS method for LiDAR‐based DEMs (Δ S = 0·81°). The results from this study signal the need for caution when estimating slopes along streams from high‐accuracy, LiDAR‐generated DEMs. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".