Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/134286
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dc.contributor.authorLuna, Inties_ES
dc.contributor.authorLobo, Agustínes_ES
dc.date.accessioned2016-06-30T10:55:37Z-
dc.date.available2016-06-30T10:55:37Z-
dc.date.issued2016-06-
dc.identifier.citationRemote Sensing, 8(6): Article number 500 (2016)es_ES
dc.identifier.issn2072-4292-
dc.identifier.urihttp://hdl.handle.net/10261/134286-
dc.description.abstractSugarcane is an important economic resource for many tropical countries and optimizing plantations is a serious concern with economic and environmental benefits. One of the best ways to optimize the use of resources in those plantations is to minimize the occurrence of gaps. Typically, gaps open in the crop canopy because of damaged rhizomes, unsuccessful sprouting or death young stalks. In order to avoid severe yield decrease, farmers need to fill the gaps with new plants. Mapping gap density is therefore critical to evaluate crop planting quality and guide replanting. Current field practices of linear gap evaluation are very labor intensive and cannot be performed with sufficient intensity as to provide detailed spatial information for mapping, which makes replanting difficult to perform. Others have used sensors carried by land vehicles to detect gaps, but these are complex and require circulating over the entire area. We present a method based on processing digital mosaics of conventional images acquired from a small Unmanned Aerial Vehicle (UAV) that produced a map of gaps at 23.5 cm resolution in a study area of 8.7 ha with 92.9% overall accuracy. Linear Gap percentage estimated from this map for a grid with cells of 10 m × 10 m linearly correlates with photo-interpreted linear gap percentage with a coefficient of determination (R2)= 0.9; a root mean square error (RMSE) = 5.04; and probability (p) ≪ 0.01. Crop Planting Quality levels calculated from image-derived gaps agree with those calculated from a photo-interpreted version of currently used field methods (Spearman coefficient = 0.92). These results clearly demonstrate the effectiveness of processing mosaics of Unmanned Aerial System (UAS) images for mapping gap density and, together with previous studies using satellite and hand-held spectroradiometry, suggests the extension towards multi-spectral imagery to add insight on plant condition.es_ES
dc.language.isoenges_ES
dc.publisherMultidisciplinary Digital Publishing Institutees_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccess-
dc.subjectGapes_ES
dc.subjectNicaraguaes_ES
dc.subjectPlanting qualityes_ES
dc.subjectPrecision agriculturees_ES
dc.subjectSugarcanees_ES
dc.subjectUAVes_ES
dc.titleMapping crop planting quality in sugarcane from UAV imagery: A pilot study in Nicaraguaes_ES
dc.typeartículo-
dc.identifier.doi10.3390/rs8060500-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/rs8060500es_ES
dc.identifier.e-issn2072-4292-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/es_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.type.coarhttp://purl.org/coar/resource_type/c_6501es_ES
item.grantfulltextopen-
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item.cerifentitytypePublications-
item.languageiso639-1en-
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