000078167 001__ 78167
000078167 005__ 20210301081639.0
000078167 0247_ $$2doi$$a10.3390/rs11030261
000078167 0248_ $$2sideral$$a110912
000078167 037__ $$aART-2019-110912
000078167 041__ $$aeng
000078167 100__ $$0(orcid)0000-0002-8362-7559$$aDomingo, D.$$uUniversidad de Zaragoza
000078167 245__ $$aTemporal transferability of pine forest attributes modeling using low-density airborne laser scanning data
000078167 260__ $$c2019
000078167 5060_ $$aAccess copy available to the general public$$fUnrestricted
000078167 5203_ $$aThis study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.
000078167 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/CGL2014-57013-C2-2-R
000078167 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000078167 590__ $$a4.509$$b2019
000078167 592__ $$a1.422$$b2019
000078167 591__ $$aREMOTE SENSING$$b9 / 30 = 0.3$$c2019$$dQ2$$eT1
000078167 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2019$$dQ1
000078167 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000078167 700__ $$aAlonso, R.
000078167 700__ $$0(orcid)0000-0002-8954-7517$$aLamelas, M.T.
000078167 700__ $$0(orcid)0000-0001-6288-2780$$aMontealegre, A.L.$$uUniversidad de Zaragoza
000078167 700__ $$aRodríguez, F.
000078167 700__ $$0(orcid)0000-0003-2615-270X$$ade la Riva, J.$$uUniversidad de Zaragoza
000078167 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000078167 773__ $$g11, 3 (2019), 261 [28 pp]$$pRemote sens. (Basel)$$tRemote Sensing$$x2072-4292
000078167 8564_ $$s1618588$$uhttps://zaguan.unizar.es/record/78167/files/texto_completo.pdf$$yVersión publicada
000078167 8564_ $$s106666$$uhttps://zaguan.unizar.es/record/78167/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000078167 909CO $$ooai:zaguan.unizar.es:78167$$particulos$$pdriver
000078167 951__ $$a2021-03-01-08:01:30
000078167 980__ $$aARTICLE