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author:

Lin, Siwei (Lin, Siwei.) [1] | Xie, Jing (Xie, Jing.) [2] | Deng, Jiayin (Deng, Jiayin.) [3] | Qi, Meng (Qi, Meng.) [4] | Chen, Nan (Chen, Nan.) [5]

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Abstract:

Landform classification, which is a key topic of geography, is of great significance to a wide range of fields including human construction, geological structure research, environmental governance, etc. Previous studies of landform classification generally paid attention to the topographic or texture information, whilst the watershed spatial structure has not been used. This study developed a new landform classification method based on watershed geospatial structure. Via abstracting the landform into the internal and marginal structure, we adopted the gully weighted complex network (GWCN) and watershed boundary profile (WBP) to simulate the watershed geospatial structure. Introducing various indices to quantitatively depict the watershed geospatial structure, we conducted the landform classification on the Northern Shaanxi of Loess Plateau with a watershed-based strategy and established the classification map. The classified landform distribution has significant spatial aggregation and clear regional boundaries. Classification accuracy reached 89% and the kappa coefficient reached 0.87%. Besides, the proposed method has a positive response to some similar and complex landforms. In general, the present study first utilized the watershed geospatial structure to conduct landform classification and is an efficient landform classification method with well accuracy and universality, offering additional insights for landform classification and mapping. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Keyword:

Classification (of information) Complex networks Landforms Mapping Sediments Textures Watersheds

Community:

  • [ 1 ] [Lin, Siwei]Key Laboratory of Spatial Data Mining Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, China
  • [ 2 ] [Lin, Siwei]The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China
  • [ 3 ] [Xie, Jing]Key Laboratory of Spatial Data Mining Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, China
  • [ 4 ] [Xie, Jing]The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China
  • [ 5 ] [Deng, Jiayin]State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • [ 6 ] [Deng, Jiayin]University of Chinese Academy of Sciences, Beijing, China
  • [ 7 ] [Qi, Meng]Key Laboratory of Spatial Data Mining Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, China
  • [ 8 ] [Qi, Meng]The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China
  • [ 9 ] [Chen, Nan]Key Laboratory of Spatial Data Mining Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, China
  • [ 10 ] [Chen, Nan]The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China

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Source :

International Journal of Digital Earth

ISSN: 1753-8947

Year: 2022

Issue: 1

Volume: 15

Page: 1125-1148

5 . 1

JCR@2022

3 . 7 0 0

JCR@2023

ESI HC Threshold:51

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 17

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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