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dc.contributor.authorLima-Cueto, Francisco Javier 
dc.contributor.authorBlanco-Sepúlveda, Rafael 
dc.contributor.authorGómez-Moreno, María Luisa 
dc.contributor.authorGalacho-Jiménez, Federico Benjamín 
dc.date.accessioned2024-02-08T07:27:46Z
dc.date.available2024-02-08T07:27:46Z
dc.date.issued2019
dc.identifier.citationLima-Cueto, F.J.; Blanco-Sepúlveda, R.; Gómez-Moreno, M.L.; Galacho-Jiménez, F.B. Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain. Remote Sens. 2019, 11, 2564. https://doi.org/10.3390/rs11212564es_ES
dc.identifier.urihttps://hdl.handle.net/10630/30032
dc.description.abstractIn olive groves, vegetation ground cover (VGC) plays an important ecological role. The EU Common Agricultural Policy, through cross-compliance, acknowledges the importance of this factor, but, to determine the real impact of VGC, it must first be quantified. Accordingly, in the present study, eleven vegetation indices (VIs) were applied to quantify the density of VGC in olive groves (Olea europaea L.), according to high spatial resolution (10–12 cm) multispectral images obtained by an unmanned aerial vehicle (UAV). The fieldwork was conducted in early spring, in a Mediterranean mountain olive grove in southern Spain presenting various VGC densities. A five-step method was applied: (1) generate image mosaics using UAV technology; (2) apply the VIs; (3) quantify VGC density by means of sampling plots (ground-truth); (4) calculate the mean reflectance of the spectral bands and of the VIs in each sampling plot; and (5) quantify VGC density according to the VIs. The most sensitive index was IRVI, which accounted for 82% (p < 0.001) of the variability of VGC density. The capability of the VIs to di erentiate VGC densities increased in line with the cover interval range. RVI most accurately distinguished VGC densities > 80% in a cover interval range of 10% (p < 0.001), while IRVI was most accurate for VGC densities < 30% in a cover interval range of 15% (p < 0.01). IRVI, NRVI, NDVI, GNDVI and SAVI di erentiated the complete series of VGC densities when the cover interval range was 30% (p < 0.001 and p < 0.05).es_ES
dc.language.isoenges_ES
dc.publisherRemote Sensing (MDPI)es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectOlivoses_ES
dc.subject.otherUAVes_ES
dc.subject.otherVegetation ground coveres_ES
dc.subject.otherMultispectrales_ES
dc.subject.otherVegetation indiceses_ES
dc.subject.otherAgro-environmental measureses_ES
dc.titleUsing Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroFacultad de Filosofía y Letrases_ES
dc.identifier.doi10.3390/rs11212564
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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