融合夜光遥感数据的多维贫困识别及演化分析

Multidimensional poverty identification and evolution analysis based on the intergration of night-time light imagery

基于遥感手段高效识别贫困地区及掌握其演化机制对于加强扶贫攻坚与乡村振兴统筹衔接等工作具有重要意义。提出了融合夜光遥感数据借助相关系数及层次分析法构建多维贫困指数的方法,以四川省为例,对四川省各县多维贫困指数进行了估算,并对模型进行了精度检验。借助地理信息系统(GIS)空间分析技术与夜光遥感数据时间连续的特点,从时空演化角度对贫困县动态发展状况进行了详细分析。结果表明,2003-2013年四川省多维贫困县比例从46.45%下降到28.42%;变异系数呈下降趋势,证实四川省内部贫困差距缓慢缩小;2003-2013年空间分布演变图中,阿坝藏族羌族自治州、甘孜藏族自治州和凉山彝族自治州的区县大多处于长期多维贫困状态;处于极贫困区与极富裕区多维贫困指数增长幅度不明显,而处于中间地带县区增幅较大;热点分析中表现为热点区与冷点区逐渐呈东西方向抗衡之势。该研究结果可为局部异化贫困形势复杂地区的脱贫政策精准制定提供前瞻依据。

It is of great significance to effectively identify the poverty-stricken areas and appreciate the mechanism of their evolution based on remote sensing to strengthen the coordination between poverty alleviation and rural revitalization.In this paper,a method of constructing multidimensional poverty index by integrating nighttime imagery with the correlation coefficient and analytic hierarchy process is proposed.And at the same time,the accuracy of the model is tested.With the spatial analysis technology of geographic information system(GIS) and the characteristics of temporal continuity of nighttime light imagery,this paper makes a detailed analysis of the dynamic development of poverty-stricken counties from the perspective of spatialtemporal evolution.The results demonstrate that from 2003 to 2013,the proportion of the poverty-stricken counties in Sichuan dropped from 46.45% to 28.42%.The variation coefficient illustrates a decreasing trend,which proves that the poverty gap within Sichuan province is slowly narrowing.In the spatial distribution evolution map from 2003 to 2013,most districts and counties in Ganzi,Aba and Liangshan autonomous prefecture are in long-term multidimensional poverty.The increase of multidimensional poverty index is not miraculous in the extreme poverty and extremely affluent counties,but the increase is miraculous in the middle counties.In the hot spot analysis,the hot spot area and the cold spots gradually show an east-west trend.The results of this study can provide a prospective basis for the accurate formulation of poverty alleviation policies in areas with complicated local alienated poverty situation.