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Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province China

机译:基于LCM模型的卫星遥感数据土地利用/覆被变化驱动因素及未来预测-以甘肃省为例

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摘要

Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, and sustainable development. Detecting and predicting LUCC, a dynamic process, and its driving factors will help in formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection. In this study, taking Gansu Province as a case study example, we explored the LUCC pattern and its driving mechanism from 1980 to 2018, and predicted land use and cover in 2030 using the integrated LCM (Logistic-Cellular Automata-Markov chain) model and data from satellite remote sensing. The results suggest that the LUCC pattern was more reasonable in the second stage (2005 to 2018) compared with that in the first stage (1980 to 2005). This was because a large area of green lands was protected by ecological engineering in the second stage. From 1980 to 2018, in general, natural factors were the main force influencing changes in land use and cover in Gansu, while the effects of socioeconomic factors were not significant because of the slow development of economy. Landscape indices analysis indicated that predicted land use and cover in 2030 under the ecological protection scenario would be more favorable than under the historical trend scenario. Besides, results from the present study suggested that LUCC in arid and semiarid area could be well detected by the LCM model. This study would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis in arid and semiarid regions.
机译:土地利用和覆被变化(LUCC)是影响全球环境,气候变化和可持续发展的重要问题。对LUCC,动态过程及其驱动因素的检测和预测将有助于制定适合当地条件的有效土地使用和规划政策,从而支持当地的社会经济发展和全球环境保护。在本研究中,以甘肃省为例,探讨了1980年至2018年的LUCC模式及其驱动机制,并使用Logistic-Cellular Automata-Markov Chain(Logistic-Cellular Automata-Markov Chain)模型和2030年预测了2030年的土地利用和覆盖来自卫星遥感的数据。结果表明,与第一阶段(1980年至2005年)相比,第二阶段(2005年至2018年)的LUCC模式更为合理。这是因为第二阶段的生态工程保护了大片绿地。从1980年到2018年,总体而言,自然因素是影响甘肃土地利用和覆被变化的主要力量,而社会经济因素的影响并不显着,因为经济发展缓慢。景观指数分析表明,在生态保护情景下,2030年的预测土地利用和覆盖将比历史趋势情景下的预测更为有利。此外,本研究的结果表明,通过LCM模型可以很好地检测干旱和半干旱地区的LUCC。这项研究有望为将来的土地利用规划和管理提供理论指导,并为干旱和半干旱地区的土地利用覆盖变化分析提供新的方法学参考。

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