Quantitative analysis and spatial distribution of landform spatial structure on Loess Plateau

The Loess Plateau is the largest gully geomorphic region in the world, characterized by the most intense soil erosion in a typical loess-covered area. Previous studies have focused on the terrain and texture of this region; however, there have been no systematic studies on the gully spatial structur...

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Veröffentlicht in:Progress in physical geography 2023-08, Vol.47 (4), p.541-569
Hauptverfasser: Lin, Siwei, Chen, Nan, Qianqian, Zhzou, Feng, Qiu, Jing, Xie, Meng, Qi, Yugui, Fan, Zihao, Yang, Weibin, Lin, JiaYin, Deng, Ping, Tu
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Sprache:eng
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Zusammenfassung:The Loess Plateau is the largest gully geomorphic region in the world, characterized by the most intense soil erosion in a typical loess-covered area. Previous studies have focused on the terrain and texture of this region; however, there have been no systematic studies on the gully spatial structure of the Loess Plateau. Therefore, the present study investigated the characteristics and spatial distribution of landform spatial structure over the Loess Plateau. Specifically, gully weighted complex networks (GWCNs) were applied to simulate the gully spatial structure of loess landforms, and a series of quantitative indices were introduced to delineate these. Using 57 geomorphological units uniformly distributed across the Loess Plateau as test areas and six typical loess landforms as sample areas, GWCNs were constructed, and using these GWCNs, the spatial structure and internal mechanisms of typical loess landforms were explored. From a series of fresh insights, such as the regional scale-free distribution, homologous structure, tightness, community effect, connectivity, stability, and complexity, regular variations in quantitative indices delineate the spatial distribution of the characteristics of landform spatial structure over the plateau. Moreover, the spatial distribution of complex network indices exhibited strong spatial coupling with loess landforms. Overall, GWCNs could be effectively used for landform recognition and performed well. In conclusion, these experimental results suggest that introducing complex networks into landform studies can offer novel insights into landform quantitative analyses. The present work is of great significance, as it proposes a new methodology for describing the spatial structure and terrain features of landforms in quantitative analyses and furthers our understanding of landform genesis.
ISSN:0309-1333
1477-0296
DOI:10.1177/03091333221134192