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Journal of the Indian Society of Remote Sensing

, Volume 47, Issue 1, pp 153–163 | Cite as

Oasis Landscape Pattern Dynamics in Manas River Watershed Based on Remote Sensing and Spatial Metrics

  • Bin WangEmail author
  • Yanxia Li
  • Siyuan Wang
  • Changzheng Liu
  • Guanhua Zhu
  • Li Liu
Research Article
  • 25 Downloads

Abstract

Constant alterations of oasis landscape pattern, triggered by specific natural and human driving forces, have aroused some heated discussions in the field of regional environmental change research especially in arid areas. In this study, the Normalized Difference Vegetation Index and the Soil Brightness Index have been selected to successfully identify discrete land cover classes derived from a series of Landsat satellite images of Manas River watershed in 1989, 1998, and 2014. FRAGSTATS and principal components analysis were used to recognize independent components of landscape structure. Nine land cover classes combined with ten landscape metrics indicated variations in the composition and configuration of landscape pattern. Results related to historical data were analyzed to identify driving forces, mainly including population growth, agricultural modernization, urbanization, and water resource provision. The results revealed a trend toward less landscape fragmentation, as indicated by a steady decrease in the number of patches as well as an increase in mean patch size and contagion. To be more specific, we found substantial increases in cropland (72.3%) as well as built-up land (43.7%) with larger and more compact patches, while water (64.1%), snow cover (52.0%), and saline–alkali soil (48.5%) dropped sharply. Our findings suggested that population influx and reclamation of saline–alkali soil were two major driving forces critical to those landscape pattern changes.

Keywords

Oasis landscape pattern Manas river watershed spatial metrics Urbanization Driving forces 

Notes

Acknowledgements

This material was funded by a Xinjiang Production and Construction Corps (XPCC) Scientific and Technological Project (Grant No. 2015AD018).

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Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  • Bin Wang
    • 1
    Email author
  • Yanxia Li
    • 1
  • Siyuan Wang
    • 2
  • Changzheng Liu
    • 3
  • Guanhua Zhu
    • 1
  • Li Liu
    • 1
  1. 1.School of Civil EngineeringBeijing Jiaotong UniversityBeijingChina
  2. 2.Chinese Academy of Sciences, Institute of Remote Sensing ApplicationsBeijingChina
  3. 3.College of Information Science and TechnologyShihezi UniversityShiheziChina

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