Land Use Change Prediction Method Based on CA-Markov Model Under Cloud Computing Environment

Traditional land-use change prediction methods are usually implemented by serial algorithms or semi-manual methods and they were often inefficient. This paper develops a parallel land-use change prediction method based on cloud lomputing (Cloud-CMLP), the map reduce programming model is used to parallelize and extend the cellular automata(CA)-Markov model. Taking Hangzhou as a study area, the experiments are conducted as follows: ① Efficiency tests are conducted to compare the core algorithms of Cloud-CMLP under the different number of data. ② The Cloud-CMLP method is used to simulate the land-use change in 2013, and the simulated results are compared with the 2013 remote sensing image classification results to verify the validity of Cloud-CMLP method. ③ The land-use change in 2020 is predicted and analyzed by using Cloud-CMLP, and the predicted results show that the land area of urban construction is rising rapidly and mainly come from the conversion of agricultural land.