A regression approach to the mapping of bio-physical characteristics of surface sediment using in situ and airborne hyperspectral acquisitions SCIE SCOPUS

DC Field Value Language
dc.contributor.author Ibrahim, Elsy -
dc.contributor.author Kim, Wonkook -
dc.contributor.author Crawford, Melba -
dc.contributor.author Monbaliu, Jaak -
dc.date.accessioned 2020-04-16T10:55:13Z -
dc.date.available 2020-04-16T10:55:13Z -
dc.date.created 2020-01-28 -
dc.date.issued 2017-02 -
dc.identifier.issn 1616-7341 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/1294 -
dc.description.abstract Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression models to quantify sediment properties instead of classifying them. Two regression approaches, namely multiple regression (MR) and support vector regression (SVR), are used in this study for the retrieval of bio-physical variables of intertidal surface sediment of the IJzermonding, a Belgian nature reserve. In the regression analysis, mud content, chlorophyll a concentration, organic matter content, and soil moisture are estimated using radiometric variables of two airborne sensors, namely airborne hyperspectral sensor (AHS) and airborne prism experiment (APEX) and and using field hyperspectral acquisitions by analytical spectral device (ASD). The performance of the two regression approaches is best for the estimation of moisture content. SVR attains the highest accuracy without feature reduction while MR achieves good results when feature reduction is carried out. Sediment property maps are successfully obtained using the models and hyperspectral imagery where SVR used with all bands achieves the best performance. The study also involves the extraction of weights identifying the contribution of each band of the images in the quantification of each sediment property when MR and principal component analysis are used. -
dc.description.uri 1 -
dc.language English -
dc.subject GRAIN-SIZE -
dc.subject STABILITY -
dc.subject SPECTROMETRY -
dc.subject ENGLAND -
dc.subject IMAGERY -
dc.title A regression approach to the mapping of bio-physical characteristics of surface sediment using in situ and airborne hyperspectral acquisitions -
dc.type Article -
dc.citation.endPage 316 -
dc.citation.startPage 299 -
dc.citation.title OCEAN DYNAMICS -
dc.citation.volume 67 -
dc.citation.number 2 -
dc.identifier.bibliographicCitation OCEAN DYNAMICS, v.67, no.2, pp.299 - 316 -
dc.identifier.doi 10.1007/s10236-016-1024-1 -
dc.identifier.scopusid 2-s2.0-85008192278 -
dc.identifier.wosid 000394155900008 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.subject.keywordPlus INTERTIDAL SEDIMENTS -
dc.subject.keywordPlus GRAIN-SIZE -
dc.subject.keywordPlus STABILITY -
dc.subject.keywordPlus CLASSIFICATION -
dc.subject.keywordPlus SPECTROMETRY -
dc.subject.keywordPlus ENGLAND -
dc.subject.keywordPlus IMAGERY -
dc.subject.keywordAuthor Intertidal sediment -
dc.subject.keywordAuthor Hyperspectral -
dc.subject.keywordAuthor Multiple regression -
dc.subject.keywordAuthor Support vector regression -
dc.subject.keywordAuthor AHS -
dc.subject.keywordAuthor APEX -
dc.subject.keywordAuthor Mud -
dc.subject.keywordAuthor Chlorophyll alpha -
dc.subject.keywordAuthor Organic matter -
dc.subject.keywordAuthor Moisture -
dc.relation.journalWebOfScienceCategory Oceanography -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Oceanography -
Appears in Collections:
Ocean Research, Operations & Support Department > Korea Ocean Satellite Center > 1. Journal Articles
Files in This Item:
There are no files associated with this item.


Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.