Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model

Dexiong Teng; Jiaojun Zhu; Jin Chen; Tian Gao; Fengyuan Yu; Yirong Sun; Lizhong Yu; Yuan Zhu; Jinxin Zhang; Xinhua Zhou

doi:10.1016/j.srs.2025.100265

Science of Remote Sensing (Dec 2025)

Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model

Dexiong Teng,
Jiaojun Zhu,
Jin Chen,
Tian Gao,
Fengyuan Yu,
Yirong Sun,
Lizhong Yu,
Yuan Zhu,
Jinxin Zhang,
Xinhua Zhou

Affiliations

Dexiong Teng: CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Qingyuan Forest CERN, National Observation and Research Station, Liaoning Province, Shenyang, 110016, China; CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
Jiaojun Zhu: CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Qingyuan Forest CERN, National Observation and Research Station, Liaoning Province, Shenyang, 110016, China; CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Corresponding author. Institute of Applied Ecology, Chinese Academy of Sciences, USA.
Jin Chen: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
Tian Gao: CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Qingyuan Forest CERN, National Observation and Research Station, Liaoning Province, Shenyang, 110016, China; CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
Fengyuan Yu: CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Qingyuan Forest CERN, National Observation and Research Station, Liaoning Province, Shenyang, 110016, China; CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
Yirong Sun: CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Qingyuan Forest CERN, National Observation and Research Station, Liaoning Province, Shenyang, 110016, China; CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
Lizhong Yu: CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Qingyuan Forest CERN, National Observation and Research Station, Liaoning Province, Shenyang, 110016, China; CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
Yuan Zhu: CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Qingyuan Forest CERN, National Observation and Research Station, Liaoning Province, Shenyang, 110016, China; CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
Jinxin Zhang: CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Qingyuan Forest CERN, National Observation and Research Station, Liaoning Province, Shenyang, 110016, China; CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China
Xinhua Zhou: CAS-CSI Joint Laboratory of Research and Development for Monitoring Forest Fluxes of Trace Gases and Isotope Elements, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China; Campbell Scientific Incorporation, Logan, UT, 84321, USA

DOI: https://doi.org/10.1016/j.srs.2025.100265
Journal volume & issue: Vol. 12
p. 100265

Abstract

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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 (Df), entropy of terrain elements (H), rugosity (R), volume filling ratio (V), and slope (α) as TCI=(Df+sinα)H−1/R+V. The results showed a substantial variability in Df, H, R, and V with elevations and terrain unit sizes, which was related to feature specific and scale dependent. The terrain features (Df, 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 (R2 ≥ 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.

Published in Science of Remote Sensing

ISSN: 2666-0172 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Geography. Anthropology. Recreation: Physical geography; Science
Website: https://www.sciencedirect.com/journal/science-of-remote-sensing

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