Simulating Future LUCC by Coupling Climate Change and Human Effects Based on Multi-Phase Remote Sensing Data

Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems LUCC. Subtropical forests have great potential for sequestration, yet their future dynamics under natural human influences are unclear. Zhejiang Province China is distribution area subtropical forests. For management, it significance explore dynamic changes Zhejiang. As a popular LUCC spatial simulation model, cellular automata (CA) model coupled with machine learning quantitative demand models such as system (SD) can achieve effective simulation. Therefore, we first integrated back propagation neural network (BPNN), CA, SD BPNN_CA_SD (BCS) then designed slow development scenario (SD_Scenario), harmonious (HD_Scenario), baseline (BD_Scenario), fast (FD_Scenario), combining climate disturbance. Thirdly, obtained land-use patterns from 2014 2084 multiple scenarios, finally, analyzed temporal discussed future. The results showed following: (1) overall accuracy was approximately 0.8, kappa coefficient 0.75, figure merit (FOM) value over 28% when using BCS predict LUCC, indicating that could consistent accurately. (2) evolution different scenarios varied, growth bamboo decline coniferous FD_Scenario being prominent among changes. Compared 2014, will increase by 37%, while decrease 25%. (3) Comparing forests, SD_Scenario found be beneficial ecology. These provide decision-making reference planning sustainable Province.