Spatial identification and dynamic analysis of land use functions reveals distinct zones of multiple functions in eastern China.

Sci Total Environ

School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Avenue, Qixia Distinct, Nanjing 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, 163 Xianlin Avenue, Qixia Distinct, Nanjing 210023, China; Natural Resources Research Center, 163 Xianlin Avenue, Qixia Distinct, Nanjing University, Nanjing 210023, China.

Published: November 2018

AI Article Synopsis

  • Land use function (LUF) is key for evaluating land system changes and is important for effective land use planning and management.
  • A new classification system for LUFs, based on spatial planning goals, was tested in Jiangsu Province, analyzing data from 2000 to 2015 across 63 counties.
  • The study revealed significant changes in LUFs due to different county characteristics and identified four distinct LUF zones, suggesting a need for tailored local strategies in future land use policies.

Article Abstract

Land use function (LUF) is a valuable concept that allows for more integrated assessments of land system change. Identifying the changes and relationships of multiple LUFs is pertinent to land use planning and management. Selection and quantification of LUF indicators are critical for LUFs assessment. However, past studies have mostly assessed LUFs using socioeconomic data, which are not suitable for spatial variable quantification. In this study, we proposed a new LUFs classification system based on spatial planning goals, and we applied the system to assess 12 LUFs across 63 counties in Jiangsu Province of eastern China based on multi-source data using geospatial modeling tools combined with statistical analysis of socioeconomic data. We also analyzed the change in LUFs between 2000 and 2015, as well as the interactions among multiple functions. Finally, we identified distinct function zones based on the LUFs assessment in 2000 and 2015 using k-means clustering. The result showed that 12 LUFs displayed significant changes and interactions between 2000 and 2015, which can be explained by differing topography and social-ecological characteristics among counties. Additionally, we found four distinct LUF zones that are spatially agglomerated in similar landscapes and characterize specific LUF relationships in each cluster. In the future, local LUFs and their changes over time should be taken into consideration for land use planning and management, which provide a reference for policy-makers to make decisions that better match local development realities.

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http://dx.doi.org/10.1016/j.scitotenv.2018.05.383DOI Listing

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