Abstract
Abstract Understanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.
Highlights
Land is one of the indispensable basic resources in human life and production
The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient
The weak correlation between kappa coefficient, landscape pattern and economic factors enhanced that the cellular automata (CA)-Markov model is a powerful tool to predict the change of land use on county scale
Summary
Land is one of the indispensable basic resources in human life and production. Land use is a process in which people use and renovate land by selecting different natural attributes to meet people’s needs for life and society. The land carries human activities in different time and space, creating an extremely complex and varied land use pattern. The pattern of land use, in turn, will have a positive or negative impact on human activities. The land-use/land-cover (LULC) change may affect climate, ecosystem processes and biodiversity [1]. Understanding and modeling of LULC change is of great significance to environmental protection and land use planning [2]. A full understanding of the interaction between the driving forces of LULC change and a complete simulation is a prerequisite for accurately predicting future land use changes
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