A Random Forest-Based CA-Markov Model to Examine the Dynamics of Land Use/Cover Change Aided with Remote Sensing and GIS

Abstract

Understanding the land use/cover change (LUCC) in watersheds is essential for sustainable development. The CA-Markov model has been proven to be an effective method for land use modeling because of its simplicity and potential for evolution. However, it is difficult to apply this method to meet the requirement of land use planning and management since it fails to consider the driving forces of LUCC. To evaluate the factors that influence LUCC comprehensively, we developed and implemented a machine learning-based CA-Markov model to understand the dynamics of LUCC in a coastal watershed in Southeast China, the Minjiang River Watershed (MRW). The proposed method performed well for each land use category, with average AUC values of 0.999 and 0.916 for the training and testing periods, respectively, for suitable images. The overall accuracy for LUCC was 0.971. The urbanization process in the MRW was speeding up recently. Urban area increased by 2.22% of the total area during 2015–2020, and most of that was from conversion of woodland and agricultural land. Additionally, the proposed method provided a much deeper understating of the forces driving the LUCC on a regional scale. Population and gross domestic product (GDP) were the major factors influencing the distribution of urbanized land in the MRW. In contrast, woodland distribution was highly related to topographic factors in the MRW. Scenario analysis was also employed to identify patterns of LUCC under different scenarios. The results showed that the process of urbanization may become more complex with increasing population and GDP and that land use evolution may be more sustainable with scientific spatial plans which consider facilities for people and ecological protection. The proposed method quantifies the LUCC in changing environmental settings and can serve as a helpful tool for sustainable watershed management.