Cellular automata-based spatial multi-criteria land suitability simulation for irrigated agriculture

Abstract

Cellular automata (CA) models are increasingly used to simulate various dynamic courses, e.g. urban spatial growth, forest fire spread and soil desertification. CA can express space structures and patterns of complex systems, which are difficult to perform only with mathematical equations. In this study, a new CA-based spatial multi-criteria evaluation (MCE) methodology was developed to conduct land suitability simulation (LSS). The approach incorporated MATLAB to build the analytical hierarchy procedure (AHP) for criteria weighting. The method is implemented as a tool, called AHP–CA–GIS, using C# .NET computer language in ArcGIS environment. It has adjustable parameter values which allow users to rectify model inputs for deriving different scenarios. It is spatial-based, flexible, low-cost and robust, as well as suitable for long-term evaluation. It has increased the scope of GIS application in MCE and makes the application practical for decision-making. The AHP–CA–GIS model has been applied to simulate an evaluation of irrigated cropland suitability in the Macintyre Brook catchment of southern Queensland, Australia. Five suitability scenarios were generated. The resultant land suitability map was compared with present land use. The analysis has clearly revealed the potential for irrigation expansion in the catchment. It has also represented the possible suitability of spatial distribution in the long run. This, in turn, can help the decision-makers optimise land allocation and make better land-use planning decisions.