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- W4387307574 abstract "The unreasonable development of land use and use of landscape patterns are the primary causes of the unsustainable growth of tourism cities. Dawa District, a well-known tourist destination in China, was chosen as the research object in order to scientifically evaluate and simulate the dynamic changes in land use. Landsat remote sensing images of the Dawa District from 2011, 2016, and 2021 were processed, using the development of regional tourism in 2016 as the dividing point. Using Arcgis10.3, ENVI5.3, and Fragstats tools, GIS spatial analysis and landscape index analysis were used to explore the spatiotemporal dynamic characteristics of land use and landscape patterns. The CA-Markov model was used to simulate and predict the land use landscape pattern in 2026. The results show that the rapid expansion of built-up areas in 2011–2021 and the increasing marginal complexity and fragmentation of landscape patterns are the main evolution trends. In 2016, emphasis was placed on sustainable land resources, and the area of forested water gradually recovered. However, the patch density (PD) and number of patches (NP) indices reflecting fragmentation still showed a 1.5–2 fold increase. It is expected that in 2026, the dual pressure of urban development and ecological sustainable construction will enhance connectivity and aggregation, and the ecological environment will gradually recover. At the same time, the competition for various types of land will become increasingly prominent, and agricultural activities will be affected. In summary, optimization strategies are proposed from the perspectives of government, land, landscape, and industry, aiming to promote the sustainable development of land and landscape resources in tourist cities." @default.
- W4387307574 created "2023-10-04" @default.
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- W4387307574 date "2023-10-03" @default.
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- W4387307574 title "Spatiotemporal Dynamic Analysis and Simulation Prediction of Land Use and Landscape Patterns from the Perspective of Sustainable Development in Tourist Cities" @default.
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- W4387307574 doi "https://doi.org/10.3390/su151914450" @default.
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