Analysis of Landscape Pattern Spatial Scale in Middle and Upper Reaches of Meijiang River Basin

  • Yuchan Chen
  • Zhengdong ZhangEmail author
  • Chuanxun Yang
  • Yang Yang
  • Chen Zhang
  • Liusheng Han
  • Ji YangEmail author
  • Xiangyu Han
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1228)


The scale research of landscape pattern is an important basis for the study of spatiotemporal evolution of landscape pattern and the scientific and reasonable allocation of landscape pattern. This paper takes the middle and upper reaches of Meijiang river as the research area. Combined with spatial grain size analysis and extent size analysis, the spatial scale effect was studied to select the optimal research scale in this research area. The results show that most of the appropriate grain size of landscape pattern indexes are in range of 90–150 m, and the optimal grain size scale is 150 m in Middle and Upper Reaches of Meijiang River Basin. At the grain size scale of 150 m, the spatial self-correlation of landscape pattern index over 50% is the highest at the extent size of 300 m, and the optimal extent size scale is 300 m in this basin. This paper determines the optimal scale of landscape pattern in the middle and upper reaches of Meijiang river basin, which is of great significance to the ecological balance and sustainable development of the basin.


Landscape pattern spatial scale Grain scale effect Extent scale effect 



The authors would like to thank the Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06D336), Guangdong Provincial Science and Technology Program (2017B010117008), Guangzhou Science and Technology Program (201806010106, 201902010033), the National Natural Science Foundation of China (41976189, 41976190), the GDAS’s Project of Science and Technology Development (2016GDASRC-0211, 2018GDASCX-0403, 2019GDASYL-0301001, 2017GDASCX-0101, 2018GDASCX-0101), and Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0301) for providing financial support.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yuchan Chen
    • 1
    • 2
    • 3
  • Zhengdong Zhang
    • 2
    Email author
  • Chuanxun Yang
    • 1
    • 4
  • Yang Yang
    • 2
  • Chen Zhang
    • 1
    • 3
  • Liusheng Han
    • 1
  • Ji Yang
    • 3
    Email author
  • Xiangyu Han
    • 5
  1. 1.Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangzhouChina
  2. 2.School of GeographySouth China Normal UniversityGuangzhouChina
  3. 3.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)GuangzhouChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.State Grid Chengdu Electric Supply CompanyChengduChina

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