An artificial neural network model of the landscape pattern in Shanghai metropolitan region, China

Abstract

To characterize the urbanization pattern quantitatively, a study on the mechanisms of the landscape pattern formation could facilitate the understanding on urban landscape patterns and processes, the ecological and socioeconomic consequences of urbanization, as well as the establishment of more effective strategies for landscape management. In this study, we integrated a Geographic Information System (GIS)-based analysis on landscape pattern with an artificial neural network (ANN) to quantitatively characterize the urbanization pattern of the metropolitan area of Shanghai, China, and to establish an ANN model that could preferably simulate the responses of urban landscape pattern to the natural and socioeconomic factors such as residence area, road density, population density, urban development history and the Huangpu River as an element of economic change. Our results showed that the ANN model seems appropriate for studying the nonlinear relationship among the forcing factors of urbanization and the urban landscape patterns, which provided an effective and practical approach for further understanding the mechanisms of the landscape formation pattern and the reciprocal relationship between landscape spatial pattern and ecological process.