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

Abstract

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.

Keywords: SDGs; population environment; poverty; remote sensing; socioecological systems.

Fig. 1.

The multilevel approach to linking households and landscape characteristics. Households have individual access to homestead areas (A, B, and C: level 1) and agricultural fields (A1–A3; B1–B3, C1–C3: level 2) surrounding the homestead. These levels should be linked to a single household. Households will also make use of common pool resources (level 3) around the village, which can be linked to multiple households. The wider regional level (level 4) considers infrastructure access. X, Y, and Z indicate fields that are adjacent to multiple households or no households, which would be split using our current method.

Fig. 2.

Tree derived from cross-validation with an overall classification accuracy of 52%. Brackets after Yes/No indicate the number of households (HH) that met the split criteria. Group 1 = poorest, group 2 = middle, and group 3 = wealthiest households correspond to the predicted wealth group using the preceding data splits. G1/G2/G3 indicate the number of households observed in each wealth group at that terminal node. LGP, length of growing season. Level 1, homestead; level 2, agriculture; level 3, common-pool resource area; level 4, wider region for accessibility and length of growing period; bare ag, proportion of bare agricultural land within level 2.

Fig. 3.

The single-level approach to linking satellite and household data often uses a single radial buffer zone. This can be problematic as it results in overlapping regions and multiple pixels being assigned to multiple households when households would not have access to some land parcels such as multiple homesteads.