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中国农学通报 ›› 2019, Vol. 35 ›› Issue (17): 158-164.doi: 10.11924/j.issn.1000-6850.casb18010169

• 三农研究 • 上一篇    

基于遥感影像的县域贫困度评估方法

丁丽华, 王瑞燕, 冯昊, 袁秀杰   

  1. 山东省泰安市山东农业大学北校区资源与环境学院
  • 收稿日期:2018-01-31 修回日期:2019-05-23 接受日期:2018-05-15 出版日期:2019-06-25 发布日期:2019-06-25
  • 通讯作者: 王瑞燕
  • 基金资助:
    国家自然科学S(41271235);山东农业大学创新团队项目(SYL2017XTTD02);山东农业大学青年教师成长计划经费和青年创新基金共同资助S(41401239);山东农业大学盐碱地改良利用项目(2014017);山东省教育厅项目(J11LC19)

Evaluation method of county poverty degree based onremote sensing image

  • Received:2018-01-31 Revised:2019-05-23 Accepted:2018-05-15 Online:2019-06-25 Published:2019-06-25

摘要: 贫困是当今世界普遍面临的问题之一,阻碍着中国的经济发展、社会稳定以及环境保护。近年来,中国的脱贫工作取得了显著成效,但长期以来对贫困区认定缺乏科学、合理的识别方法,扶贫资金和项目指向不准等问题仍较为突出。因此,贫困区域的有效瞄准和识别对新时期扶贫开发具有重大意义。本文以山东省30经济贫困县和30经济强县中的12个县为样本。首先通过对贫困县的县年鉴查询,确定以农民人均纯收入作为贫困度标准,对比年鉴数据筛选判别贫困县的指标,不同年份进行对比,选取距海洋距离、人均粮食面积、公路里程和人均新增建设用地面积为遥感指标,确定指标权重,然后构建贫困度遥感评估模型。结果表明,模型决定系数为0.5934,两者极显著相关。基于数据分析和遥感影像对比得出的贫困县的评价标准以及指标评估分析得到的贫困度县域均与现有贫困县有很好的对应,与山东省贫困县分布现状基本吻合,综合考虑了贫困现状及其潜在可能性,评估更加全面和深入。因此基于遥感进行县域贫困度识别评价具有一定的可行性。该研究可为县域贫困度评价和动态监测提供参考。

关键词: 沉水植物, 沉水植物, 苦草属, 环境修复, 研究进展, 工程应用

Abstract: Poverty is one of the common problems facing the world, hindering the Chinese economic development, social stability and environmental protection. In recent years, poverty alleviation Chinese achieved remarkable results, but for a long time in poor areas that the lack of scientific and reasonable identification method, poverty alleviation funds and the project is no problem is still more prominent. Therefore in poor areas, effective targeting and identification of poverty alleviation and development in the new period is of great significance. This paper takes 12 counties 30 economic poverty counties in Shandong province and the county economy in 30 as a sample. Firstly, through to the impoverished counties County Almanac inquiry, to determine the per capita net income of farmers as the poverty standard, Compare yearbooks to screen the indicators for poor counties and compare them in different years, selected from the marine distance, the highway mileage and the per capita grain area, new construction land area remote sensing index, determine the index weight, and then construct the evaluation model of remote sensing of poverty. The results show that the model decision coefficient is 0.5934, both significantly correlated. Evaluation of impoverished county that data analysis and remote sensing image contrast standard and evaluation analysis of poverty county were obtained correspond well with the existing poverty county based on consistent with distribution of the poor county of Shandong Province, the comprehensive consideration of poverty status and potential evaluation, a more comprehensive and in-depth.Therefore, the identification and evaluation of county poverty degree based on remote sensing is feasible. This study can provide reference for the evaluation and dynamic monitoring of the county poverty degree.

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