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dc.contributorKogab, Naokien_US
dc.contributorShinjo, Hitoshien_US
dc.contributorFujita, Haruhiroen_US
dc.contributorGintzburger, Gustaveen_US
dc.contributorAkira, Miyazakien_US
dc.creatorHirata, Masahiroen_US
dc.date2010-11-25en_US
dc.date.accessioned2020-10-30T20:03:53Z
dc.date.available2020-10-30T20:03:53Z
dc.identifierhttps://mel.cgiar.org/dspace/limiteden_US
dc.identifier.citationMasahiro Hirata, Naoki Kogab, Hitoshi Shinjo, Haruhiro Fujita, Gustave Gintzburger, Miyazaki Akira. (25/11/2010). Vegetation classification by satellite image processing in a dry area of north-eastern Syria. International Journal of Remote Sensing, 22 (4), pp. 507-516.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/12003
dc.description.abstractAs vegetation classification on the highly diverse rangeland is an inevitable procedure in evaluating total forage resources and assessing human impact in large areas, a supervised classification was conducted by satellite image processing using geocoded bands 2, 3 and 4 of Landsat 5 Thematic Mapper (TM) images, dated 13 April 1994 in the Abdal Aziz Mountain study area in northeastern Syria. The rangeland was categorized into six classes according to the plant contacts of dominant shrubs (Artemisia herba-alba and Noaea mucronata) and herbaceous plants. In addition, cultivated fields were categorized into two classes. An average classification accuracy of 85% in the supervised processing and an average ground verification accuracy of 81% on the Landsat-estimated vegetation classes were achieved for the rangeland. These show that a 30m X 30m resolution of the Landsat TM image had the ability to recognize vegetation at six sub-divided community levels, and the successful classification was conducted on the whole rangeland of the study area. The distinctive feature of this work is that this vegetation classification using Landsat TM images was accomplished at the level of classifying a A. herba-alba and N. mucronata dominant community into six sub-community classes. This detailed vegetation classification was conducted with the final aim of forage resource estimation and human impact assessment in mind.en_US
dc.languageenen_US
dc.publisherTaylor & Francisen_US
dc.rightsCopyrighted; all rights reserveden_US
dc.sourceInternational Journal of Remote Sensing;22,(2010) Pagination 507,516en_US
dc.titleVegetation classification by satellite image processing in a dry area of north-eastern Syriaen_US
dc.typeJournal Articleen_US
cg.subject.agrovocvegetationen_US
cg.subject.agrovocsyriaen_US
cg.subject.agrovocsatellite imageryen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerGeneral Commission for Scientific Agricultural Research - GCSARen_US
cg.contributor.centerJapan International Research Center for Agricultural Sciences - JIRCASen_US
cg.contributor.centerKyoto University, School of Agricultureen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.projectCommunication and Documentation Information Services (CODIS)en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.date.embargo-end-dateTimelessen_US
cg.coverage.regionWestern Asiaen_US
cg.coverage.countrySYen_US
cg.contactshinhit@kais.kyoto-u.ac.jpen_US
cg.identifier.doihttps://dx.doi.org/10.1080/01431160050505829en_US
dc.identifier.statusTimeless limited accessen_US
mel.impact-factor2.976en_US


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