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• W4288455764 abstract "As one of the central cities in the Wuling Mountains, Huaihua is regarded as the main body and core economic development area in the process of Wuling’s future new urbanization, exerting a pivotal influence on the five provincial border areas (Hunan, Hubei, Chongqing, Guizhou and Guangxi). But studies on urban expansion in this city are few, so this paper makes efforts to simulate its urban land expansion by constructing the Cellular Automata model (the Weight-CA model) taking into account differences in land types conversion. Firstly, the paper obtains the time-series Landsat spectral bands (Red, NIR) and NDVI images using the time–space fusion model; then the optimal combination of classification features is screened based on JBh distance and the object-oriented random forest algorithm is applied for land cover classification; the paper finally constructs the Weight-CA model for the simulation and validation of urban land expansion in Huaihua. Study results in the paper have shown that the Landsat spectral bands obtained with the ESTARFM have a relatively high correlation with real Landsat images. The land cover classification based on the optimal classification feature combination and the object-oriented random forest algorithm is high in accuracy, the total of which exceeding 84 % over 4 years. Meanwhile, based on the Weight-CA model, the simulation accuracy of urban land expansion in Huaihua in 2010 and 2015 reached 89.98 % and 89.60 % compared with the real values. It is operationally ready and practical and of strong practical significance to extract land cover types by Landsat and MODIS data fusion, and simulate urban land expansion using the Weight-CA method." @default.
• W4288455764 created "2022-07-29" @default.
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• W4288455764 date "2022-11-01" @default.
• W4288455764 modified "2023-09-26" @default.
• W4288455764 title "Analysis of time–space dynamic simulation on urban expansion in mountainous area based on the weight-CA model" @default.
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• W4288455764 doi "https://doi.org/10.1016/j.asr.2022.07.061" @default.
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