Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model
Bibliographic record
Abstract
Terrain complexity for describing the heterogenicity of terrains plays a key role in many disciplines, including geographic information science, atmospheric boundary layer meteorology, and ecology. However, due to the intrinsic relationships between terrain structure and the size or scale of the terrains, quantifying the terrain complexity faces the challenges in adequately capturing the intricate three-dimensional and multiscale features. Here, we developed a novel terrain complexity index ( TCI ) based on digital elevation models (DEMs), integrating fractal dimension ( D f ), entropy of terrain elements ( H ), rugosity ( R ), volume filling ratio ( V ), and slope ( α ) as T C I = ( D f + sin α ) H − 1 / R + V . The results showed a substantial variability in D f , H , R , and V with elevations and terrain unit sizes, which was related to feature specific and scale dependent. The terrain features ( D f , H , R , and V ) increased with the terrain unit size and tended to approach a constant value as the terrain unit size grew larger. It was found that the minimum terrain unit size for these terrain features increased with decreasing DEM resolutions (from 0.5 m to 120 m, ten levels), being well expressed as a power function of the DEM resolution ( R 2 ≥ 0.97). The minimum terrain unit size was uniquely determined using the change point detection. For example, the minimum terrain unit sizes were 140 m × 140 m and 7.56 km × 7.56 km at 0.5 m and 120 m DEM resolutions, respectively. These terrain features, based on the 30 m resolution DEM, explained 7–21 % of the variance in annual soil water erosion (ASWE) and 9–24 % of vascular plant diversity. The TCI exhibited superior predictive capabilities, outperforming individual terrain features by 2–10 % for both ASWE and vascular plant diversity. Our TCI emerges as an effective metric for quantifying the intricate three-dimensional structures of mountainous terrains, providing new insights into its influence on mountainous ecosystem structure and function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".