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dc.contributor.authorSánchez Espeso, Javier María 
dc.contributor.authorBastarrika, Aitor
dc.contributor.authorLopez­Guede, Jose Manuel
dc.contributor.authorTorre-Tojal, Leyre
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2022-03-23T14:43:29Z
dc.date.available2022-03-23T14:43:29Z
dc.date.issued2018
dc.identifier.issn2246-2929
dc.identifier.urihttp://hdl.handle.net/10902/24336
dc.description.abstractABSTRACT: Forest ecosystems play a very important role in carbon cycle because they suppose one of thebiggest carbon reservoirs and sinks. Estimating the aboveground forest biomass is critical tounderstand the global carbon storage process. Different models to estimate aboveground biomassin the Pinus radiata specie in a specific region of Spain have been developed, using, exclusively,public and accessible data with low point density gathered periodically from Light Detection andRanging (LiDAR) flights. The point clouds data were processed to obtain metrics considered aspredictive variables and afterwards, the multiple regression technique has been applied togenerate the biomass estimation models. The best models explain 76% of its variability with astandard error of 0.26 ton/ha in logarithmic units. The methodology can be considered as highlyautomated and extensible to other territories with similar characteristics. Our results support theuse of this approach for more sustainable management of forest areas.es_ES
dc.format.extent12 p.es_ES
dc.language.isoenges_ES
dc.publisherAalborg University Presses_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceInternational Journal of Sustainable Energy Planning and Management 2018, 17, 79-90es_ES
dc.subject.otherLiDARes_ES
dc.subject.otherBiomasses_ES
dc.subject.otherMultiple linear regressiones_ES
dc.titleBiomass estimation using LiDAR dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttp://doi.org/10.5278/ijsepm.2018.17.7es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.5278/ijsepm.2018.17.7
dc.type.versionpublishedVersiones_ES


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Atribución-NoComercial-SinDerivadas 3.0 EspañaExcepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-SinDerivadas 3.0 España