Modeling Change-Pattern-Value dynamics on land use: An integrated GIS and Artificial Neural Networks approach

Erfu Dai, Shaohong Wu, Wen Zhong Shi, Chui Kwan Cheung, Ahmed Shaker

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)


The use of spatial methods to detect and characterize changes in land use has been attracting increasing attention from researchers. The objectives of this article were to formulate the dynamics of land use on the temporal and spatial dimensions from the perspectives of the Change-Pattern-Value (CPV) and driving mechanism, based on multitemporal remote sensing data and socioeconomic data. The Artificial Neural Networks were used to identify the factors driving changes in land use. The Pearl River Delta Region of southeast China, which was experiencing rapid economic growth and widespread land conversion, has been selected as the study region. The results show that from 1985 to 2000 in the study region (1) the most prominent characteristics of change in land use were the expansion of the urban land at the expense of farmland, forests, and grasslands, (2) the land-use pattern was being optimized during this period, (3) in an analysis of value, built-up land can yield a return of more than 30 times that of farmland, water area, and forests lands, and (4) rapid economic development, growth in population, and the development of an infrastructure were major driving factors behind ecological land loss and the nonecological land expansion.
Original languageEnglish
Pages (from-to)576-591
Number of pages16
JournalEnvironmental Management
Issue number4
Publication statusPublished - 1 Oct 2005


  • Artificial Neural Networks
  • Change in land use
  • Change-Pattern-Value (CPV) analysis
  • Driving factors for land-use change
  • GIS
  • Pearl River Delta Region
  • Remote sensing

ASJC Scopus subject areas

  • Global and Planetary Change
  • Ecology
  • Pollution


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