Socioecologically informed use of remote sensing data to predict rural household poverty

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  • Gary R. Watmough, Univ Edinburgh, University of Edinburgh, Sch Geosci
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  • Charlotte L. J. Marcinko, University of Southampton
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  • Clare Sullivan, Columbia Univ, Columbia University, Ctr Int Earth Sci Informat Network
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  • Kevin Tschirhart, Columbia Univ, Columbia University, Ctr Int Earth Sci Informat Network
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  • Patrick K. Mutuo, International Institute of Tropical Agriculture (IITA)
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  • Cheryl A. Palm, Univ Florida, University of Florida, State University System of Florida, Agr & Biol Engn Dept
  • ,
  • Jens-Christian Svenning

Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.

TidsskriftProceedings of the National Academy of Sciences of the United States of America
Sider (fra-til)1213-1218
Antal sider6
StatusUdgivet - 22 jan. 2019

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