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China has encountered serious land loss problems along with urban expansiondue to rapid urbanization. Without considering complicated spatiotemporalheterogeneity, previous studies could not extract urban transition rules atlarge scale well. This study proposed a random forest algorithm (RFA) basedcellular automata (CA) model to simulate China's urban expansion and farmlandloss in a fine scale from 2000 to 2030. The objectives of this study are to 1)mine urban conversion rules in different homogeneous economic developmentregions, and 2) simulate China's urban expansion process and farmland loss athigh spatial resolution (30 meters). Firstly, we clustered several homogeneouseconomic development regions among China according to official statisticaldata. Secondly, we constructed a RFA-based CA model to mine complex urbanconversion rules and carried out simulation of urban expansion and farmlandloss at each homo-region. The proposed model was implemented on Tianhe-1supercomputer located in Guangzhou, China. The accuracy evaluation demonstratesthat the simulation result of proposed RFA-based CA model is more in agreementwith actual land use change. This study proves that the primary factor offarmland loss in China is rapid urbanization from 2000, and the farmland lossrate is expected to slow down gradually and will stabilize from 2010 to 2030.It shows that China is able to preserve the 1.20 million km farmland withoutcrossing the "red line" within the next 20 years, but the situation remainssevere.

Simulation of Urban Expansion and Farmland Loss in China by Integrating Cellular Automata and Random Forest