Identification of poverty based on nighttime light remote sensing data: A case study on contiguous special poverty-stricken areas in Liupan Mountains
Dan SHEN1,2,Liang ZHOU1,2,3(),Peian WANG1,2
1.College of Mapping and Geographic Information, Lanzhou Jiaotong University, Lanzhou 730070, China 2.Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China 3.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
In the process of targeted poverty alleviation, the problems that traditional data statistic aperture is not unified and that nighttime light data for identifying poverty is studied in a short time usually exist. With Liupan Mountain as an example, the average light index and multidimensional poverty index (MPIstatistical) indices were constructed by using the method of invariant target area and gray relational model with the help of night light and socio-economic statistics. Poverty estimation models were constructed through average light index and MPIstatistics. MPIestimation was generated and used to explore long-term sequence of poverty identification. Some conclusions have been reached: the accuracy of poverty results based on nighttime light image was higher, which can reflect the real poverty degree of the region, and the relative error ranges between 3.14% and 3.52%. The MPI estimated averages of the contiguous special poverty areas respectively are 0.346, 0.353, 0.353, 0.357 and 0.358 in many years. The level of poverty has been reduced year by year. Between 2000 and 2012, there were 3946 counties with extremely poor conditions and 2021 counties with highly poor conditions. The Moran’s I index from 2000 to 2015 respectively were 0.49, 0.45, 0.47, 0.49 and 0.43, indicating that the poverty level in 78 counties exhibits obvious agglomeration. The pattern of poverty is presented with the spatial evolution trend of “relatively less poverty in the eastern and western regions and relatively heavier poverty in the northern and southern regions”.
沈丹,周亮,王培安. 基于夜间灯光数据的六盘山连片特困区贫困度识别[J]. 国土资源遥感, 2019, 31(2): 157-163.
Dan SHEN,Liang ZHOU,Peian WANG. Identification of poverty based on nighttime light remote sensing data: A case study on contiguous special poverty-stricken areas in Liupan Mountains. Remote Sensing for Land & Resources, 2019, 31(2): 157-163.
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