Simulation of Urban Expansion and Farmland Loss in China by Integrating Cellular Automata and Random Forest
Yao Yao, Xiaoping Liu, Dachuan Zhang, Zhaotang Liang, Yatao Zhang

TL;DR
This study develops a high-resolution, region-specific cellular automata model integrated with random forest algorithms to accurately simulate China's urban expansion and farmland loss from 2000 to 2030, revealing key land use trends.
Contribution
It introduces a novel RFA-based CA model that captures complex urban conversion rules across different economic regions at a 30-meter resolution.
Findings
Farmland loss is primarily driven by rapid urbanization since 2000.
The model achieves high accuracy in land use change simulation.
China can preserve 1.20 million km² of farmland without crossing critical thresholds.
Abstract
China has encountered serious land loss problems along with urban expansion due to rapid urbanization. Without considering complicated spatiotemporal heterogeneity, previous studies could not extract urban transition rules at large scale well. This study proposed a random forest algorithm (RFA) based cellular automata (CA) model to simulate China's urban expansion and farmland loss in a fine scale from 2000 to 2030. The objectives of this study are to 1) mine urban conversion rules in different homogeneous economic development regions, and 2) simulate China's urban expansion process and farmland loss at high spatial resolution (30 meters). Firstly, we clustered several homogeneous economic development regions among China according to official statistical data. Secondly, we constructed a RFA-based CA model to mine complex urban conversion rules and carried out simulation of urban…
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Taxonomy
TopicsLand Use and Ecosystem Services · Land Rights and Reforms · Environmental Changes in China
