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

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 o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Peng Han, Qing Zhang, Yanyun Zhao, Frank Yonghong Li
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Weighted relative wealth index Classification tree Inner Mongolia grassland Multi-scale Geography (General) G1-922 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/5dd8886173354da581be81b038d39977
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5dd8886173354da581be81b038d39977
record_format	dspace
spelling	oai:doaj.org-article:5dd8886173354da581be81b038d399772021-11-10T04:42:15ZHigh-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China2666-683910.1016/j.geosus.2021.10.002https://doaj.org/article/5dd8886173354da581be81b038d399772021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666683921000729https://doaj.org/toc/2666-6839The 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.Peng HanQing ZhangYanyun ZhaoFrank Yonghong LiElsevierarticleWeighted relative wealth indexClassification treeInner Mongolia grasslandMulti-scaleGeography (General)G1-922Environmental sciencesGE1-350ENGeography and Sustainability, Vol 2, Iss 4, Pp 254-263 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Weighted relative wealth index Classification tree Inner Mongolia grassland Multi-scale Geography (General) G1-922 Environmental sciences GE1-350
spellingShingle	Weighted relative wealth index Classification tree Inner Mongolia grassland Multi-scale Geography (General) G1-922 Environmental sciences GE1-350 Peng Han Qing Zhang Yanyun Zhao Frank Yonghong Li High-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China
description	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.
format	article
author	Peng Han Qing Zhang Yanyun Zhao Frank Yonghong Li
author_facet	Peng Han Qing Zhang Yanyun Zhao Frank Yonghong Li
author_sort	Peng Han
title	High-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China
title_short	High-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China
title_full	High-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China
title_fullStr	High-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China
title_full_unstemmed	High-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China
title_sort	high-resolution remote sensing data can predict household poverty in pastoral areas, inner mongolia, china
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5dd8886173354da581be81b038d39977
work_keys_str_mv	AT penghan highresolutionremotesensingdatacanpredicthouseholdpovertyinpastoralareasinnermongoliachina AT qingzhang highresolutionremotesensingdatacanpredicthouseholdpovertyinpastoralareasinnermongoliachina AT yanyunzhao highresolutionremotesensingdatacanpredicthouseholdpovertyinpastoralareasinnermongoliachina AT frankyonghongli highresolutionremotesensingdatacanpredicthouseholdpovertyinpastoralareasinnermongoliachina
_version_	1718440560386113536

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

Ejemplares similares