Land Use/Cover Change Prediction Based on a New Hybrid Logistic-Multicriteria Evaluation-Cellular Automata-Markov Model Taking Hefei, China as an Example
Yecheng He,
Weicheng Wu,
Xinyuan Xie,
Xinxin Ke,
Yifei Song,
Cuimin Zhou,
Wenjing Li,
Yuan Li,
Rong Jing,
Peixia Song,
Linqian Fu,
Chunlian Mao,
Meng Xie,
Sicheng Li,
Aohui Li,
Xiaoping Song,
Aiqing Chen
Affiliations
Yecheng He
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Weicheng Wu
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Xinyuan Xie
School of Architecture and Urban Planning, Nanjing University, Nanjing 210000, China
Xinxin Ke
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Yifei Song
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Cuimin Zhou
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Wenjing Li
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Yuan Li
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Rong Jing
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Peixia Song
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Linqian Fu
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Chunlian Mao
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Meng Xie
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Sicheng Li
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Aohui Li
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Xiaoping Song
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Aiqing Chen
Key Lab of Digital Land and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Land use/cover change (LUCC) detection and modeling play an important role in global environmental change research, in particular, policy-making to mitigate climate change, support land spatial planning, and achieve sustainable development. For the time being, a couple of hybrid models, such as cellular automata–Markov (CM), logistic–cellular automata-Markov (LCM), multicriteria evaluation (MCE), and multicriteria evaluation–cellular automata–Markov (MCM), are available. However, their disadvantages lie in either dependence on expert knowledge, ignoring the constraining factors, or without consideration of driving factors. For this purpose, we proposed in this paper a new hybrid model, the logistic–multicriteria evaluation–cellular automata–Markov (LMCM) model, that uses the fully standardized logistic regression coefficients as impact weights of the driving factors to represent their importance on each land use type in order to avoid these defects but is able to better predict the future land use pattern with higher accuracy taking Hefei, China as a study area. Based on field investigation, Landsat images dated 2010, 2015, and 2020, together with digital elevation model (DEM) data, were harnessed for land use/cover (LUC) mapping using a supervised classification approach, which was achieved with high overall accuracy (AC) and reliability (AC > 95%). LUC changes in the periods 2010–2015 and 2015–2020 were hence detected using a post-classification differencing approach. Based on the LUC patterns of the study area in 2010 and 2015, the one of 2020 was simulated by the LMCM, CM, LCM, and MCM models under the same conditions and then compared with the classified LUC map of 2020. The results show that the LMCM model performs better than the other three models with a higher simulation accuracy, i.e., 1.72–5.4%, 2.14–6.63%, and 2.78–9.33% higher than the CM, LCM, and MCM models, respectively. For this reason, we used the LMCM model to simulate and predict the LUC pattern of the study area in 2025. It is expected that the results of the simulation may provide scientific support for spatial planning of territory in Hefei, and the LMCM model can be applied to other areas in China and the world for similar purposes.
