| Soil-salinization is one of the most important environmental issues which are faced inmany regions in the world, which affects economy development there. At present, not only thearea is expanding, but also the degree is more and more serious. Soil-salinization modelingand prediction is one of the research hotspot. The prediction methods integrating with RemoteSensing, Geographical Information System and Experts System are used extensively.However, the application of their integration in practice is not ideal, espatially with GIS. Thefunction of GIS in storage, analysis, management are not displayed well while integrating.Therefore, either real time visual model and prediction or data update is difficult. CA model is a dynamic system, which is composed of discrete and limited cells and canevolve geography phenomena basing on rules that are regulated according to the feature ofsystem. CA model has been proved to be powerful in modeling and predictingspatial-temporal evolution of complex systems. GeoCA can describe not only staticgeography but also dynamic one. By integrating organically with GIS, GeoCA mocel cansimulate many kinds of geography procession vividly under real-time and visual environment. Soil-salinization has the feature of its own and its spatial-temporal evolution fits on CAmodel. In this dissertation, GeoCA-Salinization, which mechanism accords to CA theory andwhich is a new theory about CA model is established. Analyzing soil-salinization systemfeatures in Changling County, the forming factors and the influencing factors are made certainand the formula of soil-salinization of Changling County is established. Based on the frame ofCA model, the spatial data and the attribution data are dealed with and the database aboutthem is established in GIS system. The CA model and the database of GIS being integrated,the simulating and the predicting of soil-salinization in Changling County are finished. The method of soil-salinization prediction and modeling is a new one. Integrating CAmodel and GIS, the soil-salinization modeling and prediction conquer the limitation ofdynamic analysis of GIS, while taking use of its powerful function of storage and edition. Theapplying of GeoCA-Salinization will provide decision-maker a decision-making insoil-salinization prevention and cure and it will also provide a new means on economicpersistence development. Required by research and statement, there are five parts in this dissertation. The firstchapter is about the academic and practical signification of soil-salinization spatial-temporalmodeling and predicting which is based on GeoCA model and integrated with GIS. Theresearch development and status about soil-salinization model and prediction and the applyingof CA model in geography area are stated. This chapter also accounts the data source, researchmethod and related technology route. In the second chapter, the study area is introduced andthe features of soil-salinization system of Changling County in Jilin province are alsoanalyzed and soil-salinization factors of forming and affecting are confirmed. In the thirdchapter, GeoCA-Salinization is modeled and spatial data and attribute data are dealed with.Salinity soil in Changling County in 1980 and 2020 are modeled and predicted separately inthe forth chapter. The last chapter is about the result and the discussion.
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