000060620 001__ 60620
000060620 005__ 20171130135648.0
000060620 0247_ $$2doi$$a10.3390/rs70708631
000060620 0248_ $$2sideral$$a91589
000060620 037__ $$aART-2015-91589
000060620 041__ $$aeng
000060620 100__ $$0(orcid)0000-0001-6288-2780$$aMontealegre, A.L.$$uUniversidad de Zaragoza
000060620 245__ $$aInterpolation routines assessment in ALS-derived Digital Elevation Models for forestry applications
000060620 260__ $$c2015
000060620 5060_ $$aAccess copy available to the general public$$fUnrestricted
000060620 5203_ $$aAirborne Laser Scanning (ALS) is capable of estimating a variety of forest parameters using different metrics extracted from the normalized heights of the point cloud using a Digital Elevation Model (DEM). In this study, six interpolation routines were tested over a range of land cover and terrain roughness in order to generate a collection of DEMs with spatial resolution of 1 and 2 m. The accuracy of the DEMs was assessed twice, first using a test sample extracted from the ALS point cloud, second using a set of 55 ground control points collected with a high precision Global Positioning System (GPS). The effects of terrain slope, land cover, ground point density and pulse penetration on the interpolation error were examined stratifying the study area with these variables. In addition, a Classification and Regression Tree (CART) analysis allowed the development of a prediction uncertainty map to identify in which areas DEMs and Airborne Light Detection and Ranging (LiDAR) derived products may be of low quality. The Triangulated Irregular Network (TIN) to raster interpolation method produced the best result in the validation process with the training data set while the Inverse Distance Weighted (IDW) routine was the best in the validation with GPS (RMSE of 2.68 cm and RMSE of 37.10 cm, respectively).
000060620 536__ $$9info:eu-repo/grantAgreement/ES/UZ/CUD2014-HUM-01
000060620 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000060620 590__ $$a3.036$$b2015
000060620 591__ $$aREMOTE SENSING$$b5 / 28 = 0.179$$c2015$$dQ1$$eT1
000060620 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000060620 700__ $$0(orcid)0000-0002-8954-7517$$aLamelas, M.T.
000060620 700__ $$0(orcid)0000-0003-2615-270X$$aDe La Riva, J.R.$$uUniversidad de Zaragoza
000060620 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDepartamento de Geografía y Ordenación del Territorio$$cAnálisis Geográfico Regional
000060620 773__ $$g7, 7 (2015), 8631-8654$$pRemote sens. (Basel)$$tRemote sensing (Basel)$$x2072-4292
000060620 8564_ $$s4546246$$uhttp://zaguan.unizar.es/record/60620/files/texto_completo.pdf$$yVersión publicada
000060620 8564_ $$s89579$$uhttp://zaguan.unizar.es/record/60620/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000060620 909CO $$ooai:zaguan.unizar.es:60620$$particulos$$pdriver
000060620 951__ $$a2017-11-30-13:53:40
000060620 980__ $$aARTICLE