Zoning land for agricultural protection by the integration of remote sensing, GIS, and cellular automata

Abstract

Zoning strategic agricultural land for protection has become important in reducing agricultural land loss in rapidly growing areas. In this paper, a constrained CA model based on the integration of remote sensing, GIS, and cellular automata (CA) techniques was developed to overcome the limitations of the existing methods commonly used by planners in zoning land for agricultural protection. Remote sensing data were used to calculate the normalized difference vegetation index (NDVI) which was the initial map used for the model. The factors of land suitability and geometry were embedded in the model to facilitate the rational allocation of land for agricultural protection. The CA model was implemented within a geographic information system which provided useful constraint information and modeling environment. "Grey cells" were defined in the CA model to improve modeling accuracy. The model has been tested in the Pearl River Delta, one of the fastest growing areas in China.