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

Geography and Sustainability - Tập 2 - Trang 254-263 - 2021
Peng Han1, Qing Zhang1, Yanyun Zhao1, Frank Yonghong Li1
1Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, China

Tài liệu tham khảo

Abdi, 2010, Principal component analysis, Wiley Interdiscip. Rev. Comput. Stat., 2, 433, 10.1002/wics.101 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. Aditian, 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, 10.1016/j.geomorph.2018.06.006 Angelsen, 2014, Environmental income and rural livelihoods: A global-comparative analysis, World Dev., 64, S12, 10.1016/j.worlddev.2014.03.006 Barbier, 2018, Land degradation and poverty, Nat. Sustain., 1, 623, 10.1038/s41893-018-0155-4 Barnett, 2020, A multilevel analysis of the drivers of household water consumption in a semi-arid region, Sci. Total Environ., 712, 10.1016/j.scitotenv.2019.136489 Benz, 2004, Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS J. Photogramm. Remote Sens., 58, 239, 10.1016/j.isprsjprs.2003.10.002 Berchoux, 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, 10.1007/s13280-019-01150-9 Briske, 2015, Strategies to alleviate poverty and grassland degradation in Inner Mongolia: Intensification vs production efficiency of livestock systems, J. Environ. Manage., 152, 177, 10.1016/j.jenvman.2014.07.036 Chen, 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, 10.5194/isprsarchives-XL-4-W3-47-2013 Christiaensen, 2013, Pathways out of poverty in lagging regions: Evidence from rural western China, Agric. Econ., 44, 25, 10.1111/j.1574-0862.2012.00630.x Clary, 1983, Pronghorn reactions to winter sheep grazing, plant communities, and topography in the Great Basin, J. Range Manage., 36, 749, 10.2307/3898201 Cleve, 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, 10.1016/j.compenvurbsys.2007.10.001 Coudouel, 2002, Poverty measurement and analysis, 29 Elvidge, 2009, A global poverty map derived from satellite data, Comput. Geosci., 35, 1652, 10.1016/j.cageo.2009.01.009 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). Entwisle, 2005, Population and upland crop production in Nang Rong,, Thailand. Popul. Env., 26, 449, 10.1007/s11111-005-0007-9 Fan, 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, 10.1016/j.landusepol.2014.07.011 Filmer, 2001, Estimating wealth effects without expenditure data—Or tears: An application to educational enrollments in states of India, Demography, 38, 115 Frazier, 2019, Ecological civilization: Perspectives from landscape ecology and landscape sustainability science, Landscape Ecol., 34, 1, 10.1007/s10980-019-00772-4 Fu, 2011, Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China, Ecol. Complex., 8, 284, 10.1016/j.ecocom.2011.07.003 Gislason, 2006, Random forests for land cover classification, Pattern Recogn. Lett., 27, 294, 10.1016/j.patrec.2005.08.011 Han, 2016, Effects of climate change on primary production in the Inner Mongolia Plateau, China, Int. J. Remote Sens., 37, 5551, 10.1080/01431161.2016.1230286 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. Henderson, 2012, Measuring economic growth from outer space, Am. Econ. Rev., 102, 994, 10.1257/aer.102.2.994 Heringer, 2020, Acaciainvasion is facilitated by landscape permeability: The role of habitat degradation and road networks, Appl. Veg. Sci., 23, 598, 10.1111/avsc.12520 Howe, 2008, Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries, Emerg. Themes Epidemiol., 5, 3, 10.1186/1742-7622-5-3 Hulme, 2013, Poverty in development thought: Symptoms or causes…Synthesis or uneasy compromise?, 81 Jain, 2005, Score normalization in multimodal biometric systems, Pattern Recognit. Lett., 38, 2270, 10.1016/j.patcog.2005.01.012 Jean, 2016, Combining satellite imagery and machine learning to predict poverty, Science, 353, 790, 10.1126/science.aaf7894 Kilic, 2017, Costing household surveys for monitoring progress toward ending extreme poverty and boosting shared prosperity, Policy Research Working Paper Series, 7951 Kuhn, 2008, Building predictive models in R using the caret package, J. Stat. Softw., 28, 1, 10.18637/jss.v028.i05 Li, 2011, China's grassland contract policy and its impacts on herder ability to benefit in Inner Mongolia: Tragic feedbacks, Ecol. Soc., 16, 14, 10.5751/ES-03969-160201 Liu, 2017, Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies, J. Rural Stud., 52, 66, 10.1016/j.jrurstud.2017.04.002 Liu, 2020, Ecological restoration is the dominant driver of the recent reversal of desertification in the Mu Us Desert (China), J. Clean Prod., 268, 122241, 10.1016/j.jclepro.2020.122241 McKenzie, 2005, Measuring inequality with asset indicators, J. Popul. Economics., 18, 229, 10.1007/s00148-005-0224-7 Michelson, 2013, Measuring socio-economic status in the Millennium Villages: The role of asset index choice, J. Dev. Stud., 49, 917, 10.1080/00220388.2013.785525 Mikša, 2020, Ecosystem services and legal protection of private property. Problem or solution?, Geogr. Sustain., 1, 173 Nixson, 2006, Privatization, income distribution, and poverty: The Mongolian experience, World Dev., 34, 1557, 10.1016/j.worlddev.2005.12.007 Oksanen, 2013, Package ‘vegan’, Commun. Ecol. Package, 2, 1 Palmer-Jones, 2006, It is where you are that matters: The spatial determinants of rural poverty in India, Agric. Econ., 34, 229, 10.1111/j.1574-0864.2006.00121.x Pearson, 1901, Principal components analysis. On lines and planes of closest fit to system of points in space, Philos. Mag., 2, 557, 10.1080/14786440109462720 Jenks, G., 1967. The Data Model Concept in Statistical Mapping. In:Frenzel, K. (Eds.), International Yearbook of Cartography (vol. 7). George Philip & Son Ltd., pp.186–190. Perez, A., Yeh, C., Azzari, G., Burke, M., Lobell, D., Ermon, S., 2017. Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning. https://arxiv.org/abs/1711.03654v1 (accesssed on 1 October 2020). Sandefur, 2015, The political economy of bad data: Evidence from African Survey and Administrative Statistics, J. Dev. Stud., 51, 116, 10.1080/00220388.2014.968138 Scott, 2010, Spatial statistics in ArcGIS, 27 Séguin, 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, 10.1007/s12061-011-9068-6 Serajuddin, 2015 Team, R. C., 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Therneau, T. M., Atkinson, E. J., Foundation, M., 1997. An introduction to recursive partitioning using the RPART routines, Technical report. Thongdara, 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, 10.1007/s12061-011-9066-8 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). United Nations, 2015. Transforming our World: The 2030 Agenda for Sustainable Development. United Nations. Wang, 2004, Karst rocky desertification in southwestern China: Geomorphology, landuse, impact and rehabilitation, Land Degrad. Dev., 15, 115, 10.1002/ldr.592 Watmough, 2013, Exploring the links between census and environment using remotely sensed satellite sensor imagery, J. Land Use Sci., 8, 284, 10.1080/1747423X.2012.667447 Watmough, 2016, Understanding the evidence base for poverty-environment relationships using remotely sensed satellite data: An example from Assam,, India. World Dev., 78, 188, 10.1016/j.worlddev.2015.10.031 Watmough, 2019, Socioecologically informed use of remote sensing data to predict rural household poverty, Proc. Natl. Acad. Sci. U.S.A., 116, 1213, 10.1073/pnas.1812969116 Xilingol Bureau of Statisics. 2016. Xilingol Statistical Yearbook. Xilinhot. (in Chinese) Yang, 2020, Prioritizing sustainable development goals and linking them to ecosystem services: A global expert's knowledge evaluation, Geogr. Sustain., 1, 321 Yu, 2013, Multidimensional poverty in China: Findings based on the CHNS, Soc. Indic. Res., 112, 315, 10.1007/s11205-013-0250-x Zaleniene, 2021, Higher education for sustainability: A global perspective, Geogr. Sustain., 2, 99 Zhang, 2020, Ecology and sustainability of the Inner Mongolian Grassland: Looking back and moving forward, Landsc. Ecol., 35, 2413, 10.1007/s10980-020-01083-9 Zhang, 2017, Functional dominance rather than taxonomic diversity and functional diversity mainly affects community aboveground biomass in the Inner Mongolia grassland, Ecol. Evol., 7, 1605, 10.1002/ece3.2778 Zhang, 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, 10.1071/RJ13050 Zhang, 2019, Optimal herdsmen household management modes in a typical steppe region of Inner Mongolia,, China. J. Clean. Prod., 231, 1, 10.1016/j.jclepro.2019.05.205 Zhao, 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, 5368, 10.3390/rs6065368 Zhao, 2018, Metacoupling supply and demand for soil conservation service, Curr. Opin. Environ. Sustain., 33, 136, 10.1016/j.cosust.2018.05.011 Zhao, 2019, Estimation of poverty using random forest regression with multi-source data: A case study in Bangladesh, Remote Sens., 11, 375, 10.3390/rs11040375 Zhao, 2019, 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, 10.1016/j.jclepro.2019.05.351