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With rapid urbanization and industrialization, rural work forces have migrated to cities, leading to remarkable reduction in rural poulation. So large amounts of arable lands have been abandoned in China in recent years. Abandoned arable lands in under development region of China have seriously affected the redline of arable land and national food security, which has become a major practical problem facing urban-rural integration. Multispectral remote sensing has the advantage of wide range and high speed in terms of data acquisition. It has great potential in the study of lands use. A new research approach and technical roadmap were proposed for abandoned land information extraction based on remote sensing, geographic information system, support vector machines and landscape ecological index. The study area, Zilu town, Henan province, China, is a typical underdevelopment region. Four scenes Landsat-8 OLI data from 2013 to 2015 were used to extract abandoned arable land, and its spatialtemporal distribution was analyzed based on landscape metrics. Furthermore, analysis of driving factors was conducted, such as terrain, traffic, irrigation conditions and farming radius in terms of the impact of abandoned arable lands in the study area. The results showed that the accuracy of extracting abandoned arable lands using RS was above 90%. The area of abandoned arable lands was divided into seasonal and perennial abandoned, and the former was more severe. The factors of terrain, traffic, irrigation conditions and farming radius affected the spatial-temporal distribution of abandoned arable lands, and the slope of the terrain had the greatest impact. The results can provide technical support for spatial information extraction of abandoned arable land in underdevelopment region, and can be applied to establishment of regional sustainable development policy.