How Good Are Global Layers for Mapping Rural Settlements? Evidence from China

Ningcheng Wang, Xinyi Zhang, Shenjun Yao (), Jianping Wu and Haibin Xia
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Ningcheng Wang: Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Xinyi Zhang: Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Shenjun Yao: Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Jianping Wu: Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Haibin Xia: Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China

Land, 2022, vol. 11, issue 8, 1-21

Abstract: Global urbanization has brought about a significant transition to rural areas. With the development of remote sensing technologies, land use/land cover (LULC) datasets allow users to analyze the changes in global rural settlements. However, few studies have examined the performances of the LULC datasets in mapping rural settlements. Taking China as the study area, this research selected eight of the latest LULC datasets (ESRI Land Cover, WSF, ESA WorldCover, GHS-BUILT-S2, GISD30, GISA2.0, GLC30, and GAIA) to compare their accuracy for rural settlement detection. Spatial stratified sampling was used for collecting and sampling rural settlements. We conducted omission tests, area comparison, and pixel-based accuracy tests for comparison. The results show that: (1) the performances of the 10 m resolution datasets are better than those of the 30 m resolution datasets in almost all scenarios. (2) the mapping of villages in Western China is a challenge for all datasets. (3) GHS-BUILT-S2 performs the best in almost every scenario, and can allow users to adjust the threshold value for determining a proper range of rural settlement size; ESRI outperforms any other dataset in detecting the existence of rural settlements, but it dramatically overestimates the area of rural settlements. (4) GISD30 is the best among the 30 m resolution datasets, notably in the Pearl River Delta. Finally, we provide useful suggestions on ideal map selection in various regions and scenarios.

Keywords: rural settlement mapping; land-cover; accuracy assessment; remote sensing; GHSL (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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