Welcome to Chinese Agricultural Science Bulletin,

Chinese Agricultural Science Bulletin ›› 2020, Vol. 36 ›› Issue (31): 77-85.doi: 10.11924/j.issn.1000-6850.casb20190900690

Special Issue: 农业地理 耕地保护

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Spatial and Temporal Variability Characteristics and Driving Mechanism of Land-use in Songliao River Basin from 1990 to 2015

Wu Mingwan(), Zang Chuanfu(), Fu Jiayi   

  1. School of Geography and Science, South China Normal University, Guangzhou 510631
  • Received:2019-09-27 Revised:2020-04-13 Online:2020-11-05 Published:2020-11-20
  • Contact: Zang Chuanfu E-mail:15815335398@163.com;chuanfuzang@163.com

Abstract:

To explain the spatial and temporal change characteristic and driving mechanism of land-use in Songliao River Basin from 1990 to 2015, the authors used ArcGIS space analysis, land-use type transfer matrix and SPSS correlation analysis and factor analysis, analyzed the change of land-use with the statistics of social economic and climate. The results showed that the land-use types in the Songliao River Basin changed significantly from 1990 to 2015. The land-use area followed the order of woodland> cultivated land> grassland> unused land> water area> urban and rural construction land, and the change of land-use area followed the order of cultivated land> grassland> woodland> unused land> water area> urban and rural construction land. The mutual transformation was obvious between cultivated land, woodland, grassland and unused land in 1990 to 2000, the construction land was expanding rapidly. The woodland and high-coverage grassland decreased by 12.6 and 17.4 thousand km2 respectively, but the dry land increased by 22.5 thousand km2. The main driving factors of land-use change were economic development and population growth. This study quantified the driving factors on the foundation of basin area, which could provide theoretical guidance for land-use resource management and formulation of ecological restoration plans.

Key words: Songliao River Basin, land-use change, driving factor, land-use type transfer matrix, principal component analysis

CLC Number: