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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 157-163     DOI: 10.6046/gtzyyg.2019.02.22
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
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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”.

Keywords DMSP-OLS/NPP-VIIRS      poverty index      targeted poverty alleviation      poverty identification      Liupan Mountains     
:  TP79  
Corresponding Authors: Liang ZHOU     E-mail:
Issue Date: 23 May 2019
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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[J]. Remote Sensing for Land & Resources, 2019, 31(2): 157-163.
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Fig.1  Location of contiguous destitute district of Liupan Mountain Area
Fig.2  Data processing flow
指标 权重 意义
农民人均纯收入 0.044 9 反映农民的收入水平
城镇居民人均可支配收入 0.023 5 反映居民的收入水平
高程 0.011 8 反映区域的生活环境
人口密度 0.165 6 反映区域的人口密集程度
人均GDP 0.076 6 反映区域的经济发展水平
城镇化率 0.083 5 反映区域的经济发展水平
投资总额 0.233 8 反映改善人民物质生活的条件
人均财政收入 0.110 7 反映提供基础公共设施与服务的能力
经济密度 0.249 6 反映区域单位面积上经济活动的效率和土地利用的密集程度
Tab.1  Weight of each indicator
差距 类别 2000年 2004年 2008年 2012年 2015年
0 个数/个 51 56 51 50 54
比例/% 65.38 71.79 65.38 64.10 69.23
±1 个数/个 23 21 25 24 21
比例/% 29.49 26.92 32.05 30.76 26.92
±2 个数/个 4 1 2 4 3
比例/% 5.13 1.28 2.56 5.13 3.85
Tab.2  Comparison of MPIstatisticaland ALI from 2000 to 2015
Fig.3  Regression results between MPIstatistical and ALI from 2000 to 2015
Fig.4  Total pixel value of DN and MPIestimated based on night lighting data from 2000 to 2015
贫困程度 2000年 2004年 2008年 2012年 2015年
个数/个 比例/% 个数/个 比例/% 个数/个 比例/% 个数/个 比例/% 个数/个 比例/%
极贫困 46 58.97 44 56.41 45 57.69 39 50.00 45 57.69
高度贫困 20 25.64 21 26.92 20 25.64 21 26.92 16 20.51
中度贫困 4 5.13 4 5.13 4 5.13 9 11.54 8 10.26
轻度贫困 4 5.13 4 5.13 3 3.85 3 3.85 4 5.13
非贫困 4 5.13 5 6.41 6 7.69 6 7.69 5 6.41
Tab.3  Comparison of poverty levels of contiguous destitute district of Liupan Mountain Area
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