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Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

文献类型：期刊论文

作者Yin, Jiadi1,4,5; Fu, Ping1,4; Hamm, Nicholas A. S.1,4; Li, Zhichao5; You, Nanshan2,5; He, Yingli2,5; Cheshmehzangi, Ali3; Dong, Jinwei5
刊名REMOTE SENSING
出版日期2021-04-01
卷号13期号:8页码:17
关键词urban land use remote sensing geospatial big data decision-level integration feature-level integration Hangzhou
DOI10.3390/rs13081579
通讯作者Fu, Ping(Ping.Fu@nottingham.edu.cn)
英文摘要Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.
WOS关键词GOOGLE EARTH ENGINE ; TIME-SERIES ; SOCIAL-MEDIA ; USE CLASSIFICATION ; FUNCTIONAL ZONES ; COVER ; IMAGERY ; AREA ; SURFACE ; POPULATION
资助项目National Natural Science Foundation of China[41971078] ; National Natural Science Foundation of China[41871349] ; National Natural Science Foundation of China[41801336] ; Chinese Academy of Sciences the Strategic Priority Research Program[XDA19040301] ; Key Research Program of Frontier Sciences[QYZDB-SSW-DQC005]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000644671100001
出版者MDPI
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences the Strategic Priority Research Program ; Key Research Program of Frontier Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/161640]  
专题中国科学院地理科学与资源研究所
通讯作者Fu, Ping
作者单位1.Univ Nottingham Ningbo China, Geospatial & Geohazards Res Grp, Fac Sci & Engn, Ningbo 315100, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Univ Nottingham Ningbo China, Dept Architecture & Built Environm, Ningbo 315100, Peoples R China
4.Univ Nottingham Ningbo China, Sch Geog Sci, Fac Sci & Engn, Ningbo 315100, Peoples R China
5.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Jiadi,Fu, Ping,Hamm, Nicholas A. S.,et al. Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping[J]. REMOTE SENSING,2021,13(8):17.
APA Yin, Jiadi.,Fu, Ping.,Hamm, Nicholas A. S..,Li, Zhichao.,You, Nanshan.,...&Dong, Jinwei.(2021).Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping.REMOTE SENSING,13(8),17.
MLA Yin, Jiadi,et al."Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping".REMOTE SENSING 13.8(2021):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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