Research on the spatiotemporal evolution of land use landscape pattern in a county area based on CA-Markov model

The study of the landscape pattern of land use has important practical significance for land use planning and constructing ecological cities. CA, a dynamic modeling approach, has been widely used to simulate future land use change. This study simulated and predicted the land use landscape pattern of Mianzhu City using the CAMarkov model. The spatiotemporal changes and evolution characteristics of the land use landscape pattern from 2008 to 2026 were analyzed qualitatively and quantitatively. Using land use data covering 2008, 2014, and 2020, the road factor parameter range was revised to 50 m, and the CA-Markov model was optimized. In terms of the composition of land types, forest and farmland account for more than 75% of the total area; construction and water areas increase significantly over time. The number of patches (NP) and patch density (PD), which reflect the degree of fragmentation, of landscapes from 2008 to 2020 were higher than 17,500 and 14, respectively. With higher fragmentation, lower agglomeration, and higher landscape diversity and uniformity, various indexes are predicted to have high values in 2026, indicating a significant decrease in fragmentation. In summary, strategies such as planning system, landscape pattern optimization, model modification, and land use patterns under the concept of low-carbon development are proposed. The findings will provide reference for promoting the construction of ecological cities.

Automated summary

The study of landscape pattern of land use is of great practical significance for land use planning and the construction of ecological cities. In this study, the CAMarkov model was used to simulate and predict the land use landscape pattern of Mianzhu City, and the spatiotemporal changes and evolution characteristics from 2008 to 2026 were analyzed. The findings show that construction and water areas have significantly increased over time, while forest and farmland still account for a large proportion of the total area. The predicted indexes indicate a significant decrease in fragmentation and an increase in agglomeration, diversity, and uniformity of the landscape pattern.