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dc.contributor.authorMartín García, Saray
dc.contributor.authorBalenović, Ivan
dc.contributor.authorJurjević, Luka
dc.contributor.authorLizarralde, Iñigo
dc.contributor.authorBuján, Sandra
dc.contributor.authorAlonso Ponce, Rafael
dc.date.accessioned2023-11-22T09:59:19Z
dc.date.available2023-11-22T09:59:19Z
dc.date.issued2022
dc.identifier.citationRemote Sensing, 2022, Vol. 14, Nº. 9, 2095es
dc.identifier.issn2072-4292es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/63147
dc.descriptionProducción Científicaes
dc.description.abstractUnderstorey evaluation is essential in wildlife habitat management, biomass storage and wildfire suppression, among other areas. The lack of a standardised methodology in the field measurements, and in their subsequent analysis, forces researchers to look for procedures that effectively extract understorey data to make management decisions corresponding to actual stand conditions. In this sense, when analysing the understorey characteristics from LiDAR data, it is very usual to ask: “what value should we set the understorey height range to?” It is also usual to answer by setting a numeric value on the basis of previous research. Against that background, this research aims to identify the optimal height to canopy base (HCB) filter–LiDAR metric relationship for estimating understorey height (UH) and understorey cover (UC) using LiDAR data in the Pokupsko Basin lowland forest complex (Croatia). First, several HCB values per plot were obtained from field data (measured HCBi—HCBM-i, where i ϵ (minimum, maximum, mean, percentiles)), and then they were modelled based on LiDAR metrics (estimated HCBi—HCBE-i). These thresholds, measured and estimated HCBi per plot, were used as point cloud filters to estimate understorey parameters directly on the point cloud located under the canopy layer. In this way, it was possible to predict the UH with errors (RMSE) between 0.90 and 2.50 m and the UC with errors (RMSE) between 8.8 and 18.6 in cover percentage. Finally, the sensitivity analysis showed the HCB filter (the upper threshold to select the understorey LiDAR points) is the most important factor affecting the UH estimates, while this factor and the LiDAR metric are the most important factors affecting the UC estimates.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectUnderstoreyes
dc.subjectForests and forestryes
dc.subjectBosques y silviculturaes
dc.subjectArbustoses
dc.subjectDeciduous forestes
dc.subjectForest fires - Prevention and controles
dc.subjectBosques - Incendios - Prevención y controles
dc.subjectForest ecologyes
dc.subjectEcología forestales
dc.subjectForest managementes
dc.subjectBosques - Gestiónes
dc.subjectSustainable developmentes
dc.subjectDesarrollo sosteniblees
dc.subjectCroatiaes
dc.titleWhat is the most suitable height range of ALS point cloud and LiDAR metric for understorey analysis? A study case in a mixed deciduous forest, Pokupsko basin, Croatiaes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2022 The Authorses
dc.identifier.doi10.3390/rs14092095es
dc.relation.publisherversionhttps://www.mdpi.com/2072-4292/14/9/2095?trk=organization-update_share-update_update-textes
dc.identifier.publicationfirstpage2095es
dc.identifier.publicationissue9es
dc.identifier.publicationtitleRemote Sensinges
dc.identifier.publicationvolume14es
dc.peerreviewedSIes
dc.description.projectMinisterio de Economía, Industria y Competitividad - (grant DI-16-08446)es
dc.description.projectComisión Europea - (grant H2020-EO-2017; 776045)es
dc.description.projectFundación Científica de Croacia - (project IP-2016-06-7686)es
dc.identifier.essn2072-4292es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco3106 Ciencia Forestales
dc.subject.unesco3106.08 Silviculturaes


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