High-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China

Abstract

The accurate prediction of poverty is critical to efforts of poverty reduction, and high-resolution remote sensing (HRRS) data have shown great promise for facilitating such prediction. Accordingly, the present study used HRRS with 1 m resolution and 238 households data to evaluate the utility and optimal scale of HRRS data for predicting household poverty in a grassland region of Inner Mongolia, China. The prediction of household poverty was improved by using remote sensing indicators at multiple scales, instead of indicators at a single scale, and a model that combined indicators from four scales (building land, household, neighborhood, and regional) provided the most accurate prediction of household poverty, with testing and training accuracies of 48.57% and 70.83%, respectively. Furthermore, building area was the most efficient indicator of household poverty. When compared to conducting household surveys, the analysis of HRRS data is a cheaper and more time-efficient method for predicting household poverty and, in this case study, it reduced study time and cost by about 75% and 90%, respectively. This study provides the first evaluation of HRRS data for the prediction of household poverty in pastoral areas and thus provides technical support for the identification of poverty in pastoral areas around the world.

Keywords

Weighted relative wealth index / Classification tree / Inner Mongolia grassland / Multi-scale

Declaration of Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We are very grateful to Qingfu Liu, Rihui Cong, Yongzhi Yan, Siqi Liu, and Ling Zhu for their fieldwork. This study was supported by the Key Science and Technology Program of Inner Mongolia (Grant No. ZDZX2018020, 2020GG0007, 2019GG009), Natural Science Foundation of Inner Mongolia (Grant No. 2020MS03068), Research Project of China Institute of Water Resources and Hydropower Research (Grant No. MK2019J02), and Grassland Talents Program of Inner Mongolia (Grant No. CYYC9013).

Supplementary material

Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.geosus.2021.10.002.

References

[1] Abdi, H., Williams, L.J., 2010. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433-459.

[2] Adamu, M., Kirk-Greene, A. H. M., 2018. Pastoralists of the West African savanna: Selected studies presented and discussed at the Fifteenth International African seminar held at Ahmadu Bello University, Nigeria, July 1979, Routledge.

[3] Aditian, A., Kubota, T., Shinohara, Y., 2018. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318, 101-111.

[4] Angelsen, A., Jagger, P., Babigumira, R., Belcher, B., Hogarth, N.J., Bauch, S., Börner, J., Smith-Hall, C., Wunder, S., 2014. Environmental income and rural livelihoods: A global-comparative analysis. World Dev. 64, S12-S28.

[5] Barbier, E.B., Hochard, J.P., 2018. Land degradation and poverty. Nat. Sustain. 1, 623-631.

[6] Barnett, M.J., Jackson-Smith, D., Endter-Wada, J., Haeffner, M., 2020. A multilevel analysis of the drivers of household water consumption in a semi-arid region. Sci. Total Environ. 712, 136489.

[7] Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58, 239-258.

[8] Berchoux, T., Watmough, G.R., Johnson, F.A., Hutton, C.W., Atkinson, P.M., 2020. Collective influence of household and community capitals on agricultural employment as a measure of rural poverty in the Mahanadi Delta, India. Ambio 49, 281-298.

[9] Briske, D.D., Zhao, M., Han, G., Xiu, C., Kemp, D.R., Willms, W., Havstad, K., Kang, L., Wang, Z., Wu, J., Han, X., Bai, Y., 2015. Strategies to alleviate poverty and grassland degradation in Inner Mongolia: Intensification vs production efficiency of livestock systems. J. Environ. Manage. 152, 177-182.

[10] Chen, J., Yang, S., Li, H., Zhang, B., Lv, J., 2013. Research on geographical environment unit division based on the method of natural breaks (Jenks). Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 3, 47-50.

[11] Christiaensen, L., Pan, L., Wang, S., 2013. Pathways out of poverty in lagging regions: Evidence from rural western China. Agric. Econ. 44, 25-44.

[12] Clary, W.P., Beale, D.M., 1983. Pronghorn reactions to winter sheep grazing, plant communities, and topography in the Great Basin. J. Range Manage. 36, 749-752.

[13] Cleve, C., Kelly, M., Kearns, F.R., Moritz, M., 2008. Classification of the wildland-urban interface: A comparison of pixel-and object-based classifications using high-resolution aerial photography. Comput. Environ. Urban Syst. 32, 317-326.

[14] Coudouel, A., Hentschel, J.S., Wodon, Q.T., 2002. Poverty measurement and analysis. In: Klugman, J. (Ed.), A Sourcebook for Poverty Reduction Strategies. World Bank, Washington, D.C., pp. 29-74.

[15] Elvidge, C.D., Sutton, P.C., Ghosh, T., Tuttle, B.T., Baugh, K.E., Bhaduri, B., Bright, E., 2009. A global poverty map derived from satellite data. Comput. Geosci. 35, 1652-1660.

[16] Engstrom, R., Hersh, J., Newhouse, D., 2016. Poverty in HD: What does high resolution satellite imagery reveal about economic welfare? https://thedocs.worldbank.org/en/doc/60741466181743796-0050022016/render/PovertyinHDdraftv2.75.pdf (accessed on 1 September 2019).

[17] Entwisle, B., Walsh, S.J., Rindfuss, R.R., VanWey, L.K., 2005. Population and upland crop production in Nang Rong, Thailand. Popul. Env. 26, 449-470.

[18] Fan, M., Li, Y., Li, W., 2015. Solving one problem by creating a bigger one: The consequences of ecological resettlement for grassland restoration and poverty alleviation in Northwestern China. Land Use Policy 42, 124-130.

[19] Filmer, D., Pritchett, L.H., 2001. Estimating wealth effects without expenditure data —Or tears: An application to educational enrollments in states of India. Demography 38 (1), 115-132.

[20] Frazier, A.E., Bryan, B.A., Buyantuev, A., Chen, L., Echeverria, C., Jia, P., Liu, L., Li, Q., Ouyang, Z., Wu, J., Xiang, W.-N., Yang, J., Yang, L., Zhao, S., 2019. Ecological civilization: Perspectives from landscape ecology and landscape sustainability science. Landscape Ecol. 34, 1-8.

[21] Fu, B., Liu, Y., Lü, Y., He, C., Zeng, Y., Wu, B., 2011. Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China. Ecol. Complex. 8, 284-293.

[22] Gislason, P.O., Benediktsson, J.A., Sveinsson, J.R., 2006. Random forests for land cover classification. Pattern Recogn. Lett. 27, 294-300.

[23] Han, F., Kang, S., Buyantuev, A., Zhang, Q., Niu, J., Yu, D., Ding, Y., Liu, P., Ma, W., 2016. Effects of climate change on primary production in the Inner Mongolia Plateau, China. Int. J. Remote Sens. 37, 5551-5564.

[24] Heger, M., Zens, G., Bangalor, M., 2018. Does the Environment Matter for Poverty Reduction? The Role of Soil Fertility and Vegetation Vigor in Poverty Reduction. Policy Research Working Paper 8537. World Bank, Washington, D.C.

[25] Henderson, J.V., Storeygard, A., Weil, D.N., 2012. Measuring economic growth from outer space. Am. Econ. Rev. 102, 994-1028.

[26] Heringer, G., Thiele, J., do Amaral, C.H., Meira-Neto, J.A.A., Matos, F.A.R., Lehmann, J.R.K., Buttschardt, T.K., Neri, A.V., 2020. Acaciainvasion is facilitated by landscape permeability: The role of habitat degradation and road networks. Appl. Veg. Sci. 23 (4), 598-609.

[27] Howe, L.D., Hargreaves, J.R., Huttly, S.R.A., 2008. Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries. Emerg. Themes Epidemiol. 5, 3.

[28] Hulme, D., 2013. Poverty in development thought:Symptoms or causes…Synthesis or uneasy compromise? In: Currie-Alder, B., Kanbur, R., Medhora, R. (Eds.), International Development: Ideas, Eperience and Prospects. Oxford University Press, Oxford, pp. 81-97.

[29] Jain, A., Nandakumar, K., Ross, A., 2005. Score normalization in multimodal biometric systems. Pattern Recognit. Lett. 38, 2270-2285.

[30] Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S., 2016. Combining satellite imagery and machine learning to predict poverty. Science 353, 790-794.

[31] Kilic, T., Serajuddin, U., Uematsu, H., Yoshida, N., 2017. Costing household surveys for monitoring progress toward ending extreme poverty and boosting shared prosperity. Policy Research Working Paper Series 7951.

[32] Kuhn, M., 2008. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1-26.

[33] Li, W., Huntsinger, L., 2011. China’s grassland contract policy and its impacts on herder ability to benefit in Inner Mongolia: Tragic feedbacks. Ecol. Soc. 16, 14.

[34] Liu, Y., Liu, J., Zhou, Y., 2017. Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies. J. Rural Stud. 52, 66-75.

[35] Liu, Q. F., Zhang, Q., Yan, Y. Z., Zhang, X. F., Niu, J. M., Svenning, J. C., 2020. Ecological restoration is the dominant driver of the recent reversal of desertification in the Mu Us Desert (China). J. Clean Prod. 268, 122241.

[36] McKenzie, D.J., 2005. Measuring inequality with asset indicators. J. Popul. Economics. 18, 229-260.

[37] Michelson, H., Muniz, M., Derosa, K., 2013. Measuring socio-economic status in the Millennium Villages: The role of asset index choice. J. Dev. Stud. 49, 917-935.

[38] Mikša, K., Kalinauskas, M., Inácio, M., Gomes, E., Pereira, P., 2020. Ecosystem services and legal protection of private property. Problem or solution? Geogr. Sustain. 1 (3), 173-180.

[39] Nixson, F., Walters, B., 2006. Privatization, income distribution, and poverty: The Mongolian experience. World Dev. 34, 1557-1579.

[40] Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O’hara, R., Simpson, G.L., Solymos, P., Stevens, M.H.H., Wagner, H., 2013. Package ‘vegan’. Commun. Ecol. Package 2, 1-295.

[41] Palmer-Jones, R., Sen, K., 2006. It is where you are that matters: The spatial determinants of rural poverty in India. Agric. Econ. 34, 229-242.

[42] Pearson, K., 1901. Principal components analysis. On lines and planes of closest fit to system of points in space. Philos. Mag. 2, 557-572.

[43] Jenks, G., 1967. The Data Model Concept in Statistical Mapping. In:Frenzel, K. (Eds.), International Yearbook of Cartography ( 186-190. Perez, A., Yeh, C., Azzari, G., Burke, M., Lobell, D., Ermon, S., vol. 7). George Philip & Son Ltd., pp. 2017. Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning. https://arxiv.org/abs/1711.03654v1 (accesssed on 1 October 2020).

[44] Sandefur, J., Glassman, A., 2015. The political economy of bad data: Evidence from African Survey and Administrative Statistics. J. Dev. Stud. 51, 116-132.

[45] Scott, L. M., Janikas, M. V., 2010. Spatial statistics in ArcGIS. In: Fischer M.M., Getis A. (Handbook of Applied Spatial Analysis.Eds.), Springer, pp. 27-41.

[46] Séguin, A.M., Apparicio, P., Riva, M., 2012. The impact of geographical scale in identifying areas as possible sites for area-based interventions to tackle poverty: The case of Montréal. Appl. Spat. Anal. Polic. 5, 231-251.

[47] Serajuddin, U., Uematsu, H., Wieser, C., Yoshida, N., Dabalen, A., 2015. Data deprivation: Another deprivation to end. World Bank, Washington, D.C. Policy Research Working Paper 7252 https://ssrn.com/abstract = 2600334.

[48] Team, R. C., 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

[49] Therneau, T. M., Atkinson, E. J., Foundation, M., 1997. An introduction to recursive partitioning using the RPART routines, Technical report.

[50] Thongdara, R., Samarakoon, L., Shrestha, R.P., Ranamukhaarachchi, S.L., 2012. Using GIS and spatial statistics to target poverty and improve poverty alleviation programs: A case study in Northeast Thailand. Appl. Spat. Anal. Policy. 5, 157-182.

[51] UNDP, OPHI, Kivilo, M., 2019. Global multidimensional poverty index 2019: Illuminating inequalities, Oxford Poverty and Human Development Initiative (OPHI). https://ophi.org.uk/global-multidimensional-poverty-index-2019-illuminating-inequalities. (accessed on 1 September 2020).

[52] United, Nations, 2015. Transforming our World: The 2030 Agenda for Sustainable Development. United Nations.

[53] Wang, S.J., Liu, Q.M., Zhang, D.F., 2004. Karst rocky desertification in southwestern China: Geomorphology, landuse, impact and rehabilitation. Land Degrad. Dev. 15, 115-121.

[54] Watmough, G.R., Atkinson, P.M., Hutton, C.W., 2013. Exploring the links between census and environment using remotely sensed satellite sensor imagery. J. Land Use Sci. 8, 284-303.

[55] Watmough, G.R., Atkinson, P.M., Saikia, A., Hutton, C.W., 2016. Understanding the evidence base for poverty-environment relationships using remotely sensed satellite data: An example from Assam, India. World Dev. 78, 188-203.

[56] Watmough, G.R., Marcinko, C.L.J., Sullivan, C., Tschirhart, K., Mutuo, P.K., Palm, C.A., Svenning, J.C., 2019. Socioecologically informed use of remote sensing data to predict rural household poverty. Proc. Natl. Acad. Sci. U.S.A. 116, 1213-1218.

[57] Xilingol Bureau of Statisics. 2016. Xilingol Statistical Yearbook. Xilinhot. (in Chinese)

[58] Yang, S., Zhao, W., Liu, Y., Cherubini, F., Fu, B., Pereira, P., 2020. Prioritizing sustainable development goals and linking them to ecosystem services: A global expert’s knowledge evaluation. Geogr. Sustain. 1 (4), 321-330.

[59] Yu, J., 2013. Multidimensional poverty in China: Findings based on the CHNS. Soc. Indic. Res. 112, 315-336.

[60] Zaleniene, I., Pereira, P., 2021. Higher education for sustainability: A global perspective. Geogr. Sustain. 2 (2), 99-106.

[61] Zhang, Q., Buyantuev, A., Fang, X., Han, P., Li, A., Li, F.Y., Liang, C., Liu, Q., Ma, Q., Niu, J., Shang, C., Yan, Y., Zhang, J., 2020. Ecology and sustainability of the Inner Mongolian Grassland: Looking back and moving forward. Landsc. Ecol. 35, 2413-2432.

[62] Zhang, Q., Buyantuev, A., Li, F.Y., Jiang, L., Niu, J., Ding, Y., Kang, S., Ma, W., 2017. Functional dominance rather than taxonomic diversity and functional diversity mainly affects community aboveground biomass in the Inner Mongolia grassland. Ecol. Evol. 7 (5), 1605-1615.

[63] Zhang, Q., Ding, Y., Ma, W., Kang, S., Li, X., Niu, J., Hou, X., Li, X., 2014. Grazing primarily drives the relative abundance change of C-4 plants in the typical steppe grasslands across households at a regional scale. Rangel. J. 36, 565-572.

[64] Zhang, Q., Zhao, Y., Li, F.Y., 2019. Optimal herdsmen household management modes in a typical steppe region of Inner Mongolia, China. J. Clean. Prod. 231, 1-9.

[65] Zhao, F., Xu, B., Yang, X., Jin, Y., Li, J., Xia, L., Chen, S., Ma, H., 2014. Remote sensing estimates of grassland aboveground biomass based on MODIS net primary productivity (NPP): A case study in the Xilingol Grassland of Northern China. Remote Sens. 6 (6), 5368-5386.

[66] Zhao, W., Liu, Y., Daryanto, S., Fu, B., Wang, S., Liu, Y., 2018. Metacoupling supply and demand for soil conservation service. Curr. Opin. Environ. Sustain. 33, 136-141.

[67] Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., Wu, J., 2019a. Estimation of poverty using random forest regression with multi-source data: A case study in Bangladesh. Remote Sens. 11 (4), 375.

[68] Zhao, Y., Zhang, Q., Li, F.Y., 2019b. Patterns and drivers of household carbon footprint of the herdsmen in the typical steppe region of inner Mongolia, China: A case study in Xilinhot City. J. Clean. Prod. 232, 408-416.