谷歌浏览器插件
订阅小程序
在清言上使用

Tracking Spatio-Temporal Dynamics of Greenhouse-Led Cultivated Land and its Drivers in Shandong Province, China

FRONTIERS IN ENVIRONMENTAL SCIENCE(2022)

引用 0|浏览7
暂无评分
摘要
Rapid urbanization and economic development have led the diversified food production and consumption. In this context, as a highly efficient and intensive cultivated land use form, Greenhouse-led cultivated land (GCL) has continuously increased in recent decades worldwide. Previously works have documented the irrational expansion of GCL in challenging the ecological environment and sustainable agricultural development. However, these studies either have been short-term and point-based studies or have not revealed the long-term causes, process and patterns in a large-scale. In this study, long-term annual remote sensing-based and statistical data were used to investigate the spatiotemporal dynamics of GCL and its drivers in Shandong province, China from 1989 to 2018. The results showed that: 1) GCL in Shandong was toward continuous clustering dominated by medium-low and medium densities, showing the same trend as the increase of its total area; 2) GCL with a cumulative duration of more than 15 years and a demolition frequency of less than 0.2 were mainly distributed in the industrial clustering regions and roughly formed a circular expansion pattern around the central mountainous area with the most expansion period appeared in the mid-2010's; 3) Budget expenditure for rural development, local retail sales and average earnings of local farmers were the most important local driving factors of the GCL expansion in Shandong. 4) The competition of external vegetable supply and the consumption demand from Beijing were the main external driving forces of the expansion of GCL in Shandong. These findings can enhance the comprehensive understanding of typical component of "Human-Nature" interaction and support the sustainable development of regional agriculture.
更多
查看译文
关键词
greenhouse-led cultivated land, spatiotemporal dynamics, driving factors, long-term period, provincial scale
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要