ENVIRONMENTAL ASSESSMENT

Environmental Management

, Volume 36, Issue 4, pp 576-591

Modeling Change-Pattern-Value Dynamics on Land Use: An Integrated GIS and Artificial Neural Networks Approach

  • Erfu DaiAffiliated withInstitute of Geographical Sciences and Natural Resource Research (IGSNRR), Chinese Academy of Sciences Email author 
  • , Shaohong WuAffiliated withInstitute of Geographical Sciences and Natural Resource Research (IGSNRR), Chinese Academy of Sciences
  • , Wenzhong ShiAffiliated withAdvanced Research Centre for Spatial Information TechnologyDepartment of Land Surveying and Geo-Infomatics, The Hong Kong Polytechnic University
  • , Chui-kwan CheungAffiliated withAdvanced Research Centre for Spatial Information TechnologyDepartment of Land Surveying and Geo-Infomatics, The Hong Kong Polytechnic University
  • , Ahmed ShakerAffiliated withAdvanced Research Centre for Spatial Information TechnologyDepartment of Land Surveying and Geo-Infomatics, The Hong Kong Polytechnic University

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

Abstract

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.

Keywords

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