  Cellular Automata (CA) are widely used to model the dynamics within complex
land use and land cover (LULC) systems. Past CA model research has focused on
improving the technical modeling procedures, and only a few studies have sought
to improve our understanding of the nonlinear relationships that underlie LULC
change. Many CA models lack the ability to simulate the detailed patch
evolution of multiple land use types. This study introduces a patch-generating
land use simulation (PLUS) model that integrates a land expansion analysis
strategy and a CA model based on multi-type random patch seeds. These were used
to understand the drivers of land expansion and to investigate the landscape
dynamics in Wuhan, China. The proposed model achieved a higher simulation
accuracy and more similar landscape pattern metrics to the true landscape than
other CA models tested. The land expansion analysis strategy also uncovered
some underlying transition rules, such as that grassland is most likely to be
found where it is not strongly impacted by human activities, and that deciduous
forest areas tend to grow adjacent to arterial roads. We also projected the
structure of land use under different optimizing scenarios for 2035 by
combining the proposed model with multi-objective programming. The results
indicate that the proposed model can help policymakers to manage future land
use dynamics and so to realize more sustainable land use patterns for future
development. Software for PLUS has been made available at
https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model
