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Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information

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Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information

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dc.contributor.author Mauro, Francisco es_ES
dc.contributor.author Monleón, V. J. es_ES
dc.contributor.author Temesgen, H. es_ES
dc.contributor.author Ruiz Fernández, Luis Ángel es_ES
dc.date.accessioned 2018-06-16T04:24:54Z
dc.date.available 2018-06-16T04:24:54Z
dc.date.issued 2017 es_ES
dc.identifier.issn 0045-5067 es_ES
dc.identifier.uri http://hdl.handle.net/10251/104215
dc.description.abstract [EN] Accounting for spatial correlation of LiDAR model errors can improve the precision of model-based estimators. To estimate spatial correlation, sample designs that provide close observations are needed, but their implementation might be prohibitively expensive. To quantify the gains obtained by accounting for the spatial correlation of model errors, we examined (i) the spatial correlation patterns of residuals from LiDAR linear models developed to predict volume, total and stem biomass per hectare, quadratic mean diameter (QMD), basal area, mean and dominant height, and stand density and (ii) the impact of field plot size on the spatial correlation patterns in a standwise managed Mediterranean forest in central Spain. For all variables, the correlation range of model residuals consistently increased with plot radius and was always below 60 m except for stand density, where it reached 85 m. Except for QMD, correlation ranges of model residuals were between 1.06 and 8.16 times shorter than those observed for the raw variables. Based on the relatively short correlation ranges observed when the LiDAR metrics were used as predictors, the assumption of independent errors in many forest management inventories seems to be reasonable and appropriate in practice. es_ES
dc.description.sponsorship The authors wish to thank Jay Ver Hoef and Isabel Molina for their valuable comments on earlier versions of the manuscript. The U.S. Bureau of Land Management, the Spanish Ministry of Industry, Tourism and Trade, and the Spanish Ministry of Science and Innovation provided financial support in the framework of the projects "Use of LIDAR and other remote sensing data with FIA plots for mapping forest inventory in Southwest Oregon," InForest II TSI-020100-2009-815, and CGL2010-19591/BTE, respectively. es_ES
dc.language Inglés es_ES
dc.publisher Canadian Science Publishing es_ES
dc.relation AGENCIA ESTATAL DE INVESTIGACION/CGL2016-80705-R es_ES
dc.relation MINISTERIO DE ECONOMIA INDUSTRIA Y COMPETITIVIDAD /CGL2010-19591 es_ES
dc.relation.ispartof Canadian Journal of Forest Research es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Spatial correlation es_ES
dc.subject LiDAR es_ES
dc.subject Forest inventory es_ES
dc.subject Linear models es_ES
dc.subject Spatial models es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1139/cjfr-2016-0296 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria es_ES
dc.description.bibliographicCitation Mauro, F.; Monleón, VJ.; Temesgen, H.; Ruiz Fernández, LÁ. (2017). Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information. Canadian Journal of Forest Research. 47(6):788-799. doi:10.1139/cjfr-2016-0296 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1139/cjfr-2016-0296 es_ES
dc.description.upvformatpinicio 788 es_ES
dc.description.upvformatpfin 799 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 47 es_ES
dc.description.issue 6 es_ES
dc.relation.pasarela 342093 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder MINISTERIO DE ECONOMIA INDUSTRIA Y COMPETITIVIDAD es_ES


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