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dc.contributor.authorPerea Moreno, Alberto Jesús
dc.contributor.authorAguilera Ureña, M. Jesús
dc.contributor.authorMeroño de Larriva, José Emilio
dc.contributor.authorManzano-Agugliaro, Francisco
dc.date.accessioned2017-11-09T10:40:26Z
dc.date.available2017-11-09T10:40:26Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10396/15386
dc.description.abstractGolf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceWater 8(12), 584 (2016)es_ES
dc.subjectWater managementes_ES
dc.subjectGolf coursees_ES
dc.subjectMemory-prediction theoryes_ES
dc.subjectObject-based classificationes_ES
dc.subjectUnmanned aerial vehiclees_ES
dc.titleAssessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courseses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/w8120584es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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