融合灯光强度和斑块空间分布特征的贫困区域识别模型构建——以山西省为例

贫困区域识别对于国家实施精准扶贫方略具有重要作用。基于山西省2013~2017年NPP-VIIRS夜间灯光数据，提取灯光总强度、平均灯光强度、灯光斑块面积、最大斑块灯光强度、灯光斑块聚集度等参数，应用方差分析方法检验贫困县与非贫困县的参数差异；以2013年NPP-VIIRS数据构建贫困区域识别模型，并应用于2014~2017年的贫困县识别。结果表明：模型的综合识别准确率为71.43%~77.31%，贫困县识别精度较高，为79.31%~86.21%，非贫困县识别精度为59.02%~73.77%。除了灯光强度参数，模型中包含灯光斑块空间分布特征参数能够提高总体精度。进一步分析贫困概率与GDP关系、不同类型县的贫困概率年际变化，可以认为：夜间灯光数据能够用于贫困区域识别和退出评估，融合灯光强度与灯光斑块空间分布特征有助于提高贫困区域识别精度。

Recognition Model of Poverty Areas Combining Light Intensity and Patch Spatial Distribution Characteristics: A Case Study of Shanxi Province

The poverty area recognition is the key to formulate national poverty alleviation policies. Based on satellite-based nighttime light data （NPP-VIIRS data） of 119 counties in Shanxi Province from 2013 to 2017， the statistical significance of differences between poverty counties and other counties was tested by variance analysis in terms of total light intensity， average light intensity， maximum patch light intensity， total patch area， and patch agglomeration. The recognition model of poverty areas was then developed using the NPP-VIIRS data of 2013 and applied to recognize poverty counties in 2014~2017. The results showed that the recognition accuracy of the model for poverty counties is relatively high， ranging from 79.31% to 86.21%. For non-poverty counties， the recognition accuracy is relatively lower， ranging from 59.02% to 73.77%. The comprehensive recognition accuracy is between 71.43% and 77.31%. Besides parameters of light intensity， including parameters related to landscape characteristics of lighted patches helps to improve model accuracy. In addition， we analyzed the relationship between poverty probability and GDP， the reasons of the counties with incorrect cognition， and annual variation of the poverty probability for 58 poverty counties and 15 counties out of poverty. The results not only confirmed the applicability of nighttime light data in the poverty counties recognition and assessment of the counties out of poverty， but also highlighted the important role of landscape characteristics of lighted patches， which were not included in the existing studies.