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The roles of criteria, data and classification methods in designing land cover classification systems: evidence from existing land cover data sets
Lei Guangbin; Li Ainong; Bian Jinhu; Zhang Zhengjian
2020
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
EISSN2589-059X
Volume41Issue:14Pages:5062-5082
SubtypeArticle
Contribution Rank1
AbstractThe Land Cover Classification System (LCCS) is a fundamental element and representative feature for any Land Cover Data Set (LCDS). Although various LCCSs have been proposed during the past few decades, discrepancies of LCCSs have widely existed in various LCDSs, which have caused negative impacts on comprehensive comparison and integrated utilization of multiple LCDSs. This study attempted to summarize the independent diagnostic criteria hidden in the existing LCCSs based on the induction method, and to synchronously discover the roles of data sources and classification methods in designing LCCSs. A total of 13 existing regional- or global-scale LCDSs were chosen. The analysis results show that phenology, coverage rate, vertical structure, and leaf type were the most frequently adopted criteria in the LCCSs of existing LCDSs. The decision of whether to adopt a diagnostic criterion in the LCCS of LCDS depended on the availability, effectiveness, and quality of the relevant data sources and classification methods. Currently, optical remote sensing images are still the prominent data source for regional- or global-scale LCDSs, and the potential of each diagnostic criterion could not be fully played. Multi-source and heterogeneous spatial data, ARD (Analysis Ready Data), and a fusion of optical, LiDAR (Light Detection And Ranging), radar, and other kinds of images have provided practical solutions. A lack of tools with high computing and storage capacities has been an alternative challenge. With the increasing advancement of new technologies, such as big earth data, crowdsourcing, deep learning, and cloud computing, more potential diagnostic criteria may be adopted for designing LCCS, and the richness and flexibility of the LCCS in the planned LCDS will gradually improve. This work not only offers beneficial references and revelations for the design of a new LCCS, but also provides insights for land cover mapping in large regions and the rational utilization of LCDSs.
KeywordC accumulation Peatland expansion Peatland initiation Monsoon Holocene
DOI10.1016/j.iswcr.2020.07.004
Indexed BySCI
WOS KeywordSURFACE REFLECTANCE ; CHINA ; VALIDATION ; COLLECTION ; GENERATION ; ALGORITHM ; PHENOLOGY ; FORESTS ; AVHRR ; FIELD
Language英语
Quartile3区
Funding ProjectStrategic Leader Science and Technology project of Chinese Academy of Sciences[XDA19030303] ; National Key Research and Development Program of China[2016YFA0600103] ; National Key Research and Development Program of China[2016YFC0500201-06] ; National Natural Science Foundation of China[41701433] ; National Natural Science Foundation of China[41631180] ; National Natural Science Foundation of China[41701432] ; National Natural Science Foundation of China[41701430] ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS[SDS-135-1708]
TOP
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000526425300001
Funding OrganizationStrategic Leader Science and Technology project of Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/34253
Collection数字山地与遥感应用中心
Corresponding AuthorLi Ainong
AffiliationResearch Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Corresponding Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Lei Guangbin,Li Ainong,Bian Jinhu,et al. The roles of criteria, data and classification methods in designing land cover classification systems: evidence from existing land cover data sets[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2020,41(14):5062-5082.
APA Lei Guangbin,Li Ainong,Bian Jinhu,&Zhang Zhengjian.(2020).The roles of criteria, data and classification methods in designing land cover classification systems: evidence from existing land cover data sets.INTERNATIONAL JOURNAL OF REMOTE SENSING,41(14),5062-5082.
MLA Lei Guangbin,et al."The roles of criteria, data and classification methods in designing land cover classification systems: evidence from existing land cover data sets".INTERNATIONAL JOURNAL OF REMOTE SENSING 41.14(2020):5062-5082.
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