Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model
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 ter...
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Elsevier
2025-12-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000719 |
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| author | Dexiong Teng Jiaojun Zhu Jin Chen Tian Gao Fengyuan Yu Yirong Sun Lizhong Yu Yuan Zhu Jinxin Zhang Xinhua Zhou |
| author_facet | Dexiong Teng Jiaojun Zhu Jin Chen Tian Gao Fengyuan Yu Yirong Sun Lizhong Yu Yuan Zhu Jinxin Zhang Xinhua Zhou |
| author_sort | Dexiong Teng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-76fc01cbeee0450197e005a921340d5d |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-76fc01cbeee0450197e005a921340d5d2025-08-20T04:00:43ZengElsevierScience of Remote Sensing2666-01722025-12-011210026510.1016/j.srs.2025.100265Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation modelDexiong Teng0Jiaojun Zhu1Jin Chen2Tian Gao3Fengyuan Yu4Yirong Sun5Lizhong Yu6Yuan Zhu7Jinxin Zhang8Xinhua Zhou9CAS 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, ChinaCAS 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.State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, ChinaCAS 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, ChinaCAS 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, ChinaCAS 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, ChinaCAS 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, ChinaCAS 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, ChinaCAS 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, ChinaCAS-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, USATerrain 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.http://www.sciencedirect.com/science/article/pii/S2666017225000719Terrain complexityScale effectFractal dimensionShannon wiener indexRugosityVolume filling ratio |
| spellingShingle | Dexiong Teng Jiaojun Zhu Jin Chen Tian Gao Fengyuan Yu Yirong Sun Lizhong Yu Yuan Zhu Jinxin Zhang Xinhua Zhou Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model Science of Remote Sensing Terrain complexity Scale effect Fractal dimension Shannon wiener index Rugosity Volume filling ratio |
| title | Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model |
| title_full | Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model |
| title_fullStr | Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model |
| title_full_unstemmed | Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model |
| title_short | Terrain complexity index: a novel metric for estimating multiscale three-dimensional terrain structure of montane areas based on digital elevation model |
| title_sort | terrain complexity index a novel metric for estimating multiscale three dimensional terrain structure of montane areas based on digital elevation model |
| topic | Terrain complexity Scale effect Fractal dimension Shannon wiener index Rugosity Volume filling ratio |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000719 |
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