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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.