Comparison of NDVI and RVI Vegetation Indices Using Satellite Images

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dc.contributor.authorGonenc, Abdurrahman
dc.contributor.authorOzerdem, Mehmet Sirac
dc.contributor.authorAcar, Emrullah
dc.date.accessioned2024-04-24T17:11:22Z
dc.date.available2024-04-24T17:11:22Z
dc.date.issued2019
dc.departmentDicle Üniversitesien_US
dc.description8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) -- JUL 16-19, 2019 -- Istanbul, TURKEYen_US
dc.description.abstractRemote Sensing is the acquisition of information about its physical properties without direct contact with an object. This information is obtained through sensors. These sensors do not come into contact with objects. There are two different systems for remote sensing. These are Active and Passive Sensor Systems. Passive Sensor Systems measure the energy of the rays reflected from the objects by the rays sent by the sun. On the other hand, Active Sensor Systems measure the energy reflected from the objects by transmitting their rays to the object. Passive Sensor Systems can be shown as an example of optical sensor systems. The Landsat-8 satellite works with an optical sensor system. Synthetic Aperture Radar (SAR) systems are examples of active sensor systems. SAR systems have a wide range of usage in all weather conditions and they are a radar system that displays the earth in high resolution. Radarsat-2 satellite has SAR sensor systems. The aim of this study is to compare each of the vegetation indices by using Landsat-8 and Radarsat-2 satellite images with two different types of sensors. In this study, Radar Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) were investigated. For the calculation of the RVI index, the back-scattering coefficient of the four different bands (HH, HV, VH, VV) of the multi-time full-polarimetric Radarsat-2 FQ satellite image dated 8 April 2015 was used. In the calculation of NDVI index, Band 5 (Near Infrared) and Band 4 (Red) of the Landsat-8 satellite image of May 25, 2015 were used. Dicle University agricultural areas were chosen as the study area. 100 different GPS points belonging to this agricultural area were determined and RVI and NDVI values of these points were calculated. A good correlation was observed between RVI and NDVI indices with the aid of statistically approach.en_US
dc.description.sponsorshipGeorge Mason Univ, Ctr Spatial Informat Sci & Syst,Istanbul Techn Univ,TARBIL Agr Informat Appl Res Ctr,CSISS Fdn Inc,USDA NIFA,Inst Elect & Elect Engineers,IEEE Geoscience & Remote Sensing Soc,Open Geospatial Consortiumen_US
dc.description.sponsorshipDicle University Scientific Research Projects (DUBAP) [10-MF-109]en_US
dc.description.sponsorshipThe authors would like to thank ESA and for providing the Sentinel -I software and USGS for providing Landsat Collection. This work was backed up by TUBITAK 1001 (No.14E543), Dicle University Scientific Research Projects (DUBAP No: 10-MF-109) and TARBIL project.en_US
dc.identifier.doi10.1109/agro-geoinformatics.2019.8820225
dc.identifier.isbn978-1-7281-2116-1
dc.identifier.issn2334-3168
dc.identifier.scopus2-s2.0-85072928629
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/agro-geoinformatics.2019.8820225
dc.identifier.urihttps://hdl.handle.net/11468/17445
dc.identifier.wosWOS:000562356600025
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRemote Sensingen_US
dc.subjectActive And Passive Sensoren_US
dc.subjectSaren_US
dc.subjectVegetation Indexen_US
dc.subjectRvien_US
dc.subjectNdvien_US
dc.titleComparison of NDVI and RVI Vegetation Indices Using Satellite Imagesen_US
dc.titleComparison of NDVI and RVI Vegetation Indices Using Satellite Images
dc.typeConference Objecten_US

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