000065619 001__ 65619
000065619 005__ 20180518103754.0
000065619 0247_ $$2doi$$a10.1109/JSTARS.2015.2436974
000065619 0248_ $$2sideral$$a92307
000065619 037__ $$aART-2015-92307
000065619 041__ $$aeng
000065619 100__ $$0(orcid)0000-0001-6288-2780$$aMontealegre, Antonio Luis$$uUniversidad de Zaragoza
000065619 245__ $$aA comparison of open-source LiDAR filtering algorithms in a mediterranean forest environment
000065619 260__ $$c2015
000065619 5060_ $$aAccess copy available to the general public$$fUnrestricted
000065619 5203_ $$aLight detection and ranging (LiDAR) is an emerging remote-sensing technology with potential to assist in mapping, monitoring, and assessment of forest resources. Despite a growing body of peer-reviewed literature documenting the filtering methods of LiDAR data, there seems to be little information about qualitative and quantitative assessment of filtering methods to select the most appropriate to create digital elevation models with the final objective of normalizing the point cloud in forestry applications. Furthermore, most algorithms are proprietary and have high purchase costs, while a few are openly available and supported by published results. This paper compares the accuracy of seven discrete return LiDAR filtering methods, implemented in nonproprietary tools and software in classification of the point clouds provided by the Spanish National Plan for Aerial Orthophotography (PNOA). Two test sites in moderate to steep slopes and various land cover types were selected. The classification accuracy of each algorithm was assessed using 424 points classified by hand and located in different terrain slopes, cover types, point cloud densities, and scan angles. MCC filter presented the best overall performance with an 83.3% of success rate and a Kappa index of 0.67. Compared to other filters, MCC and LAStools balanced quite well the error rates. Sprouted scrub with abandoned logs, stumps, and woody debris and terrain slopes over 15° were the most problematic cover types in filtering. However, the influence of point density and scan-angle variables in filtering is lower, as morphological methods are less sensitive to them.
000065619 536__ $$9info:eu-repo/grantAgreement/ES/DGA/B121-11$$9info:eu-repo/grantAgreement/ES/UZ/CUD2013-04
000065619 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000065619 590__ $$a2.145$$b2015
000065619 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b61 / 255 = 0.239$$c2015$$dQ1$$eT1
000065619 591__ $$aGEOGRAPHY, PHYSICAL$$b21 / 49 = 0.429$$c2015$$dQ2$$eT2
000065619 591__ $$aIMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY$$b7 / 24 = 0.292$$c2015$$dQ2$$eT1
000065619 591__ $$aREMOTE SENSING$$b11 / 28 = 0.393$$c2015$$dQ2$$eT2
000065619 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000065619 700__ $$0(orcid)0000-0002-8954-7517$$aLamelas, María Teresa
000065619 700__ $$0(orcid)0000-0003-2615-270X$$aRiva, Juan Ramón de la$$uUniversidad de Zaragoza
000065619 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDepartamento de Geografía y Ordenación del Territorio$$cAnálisis Geográfico Regional
000065619 773__ $$g8, 8 (2015), 4072-4085$$pIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing$$tIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing$$x1939-1404
000065619 8564_ $$s813173$$uhttp://zaguan.unizar.es/record/65619/files/texto_completo.pdf$$yPostprint
000065619 8564_ $$s153036$$uhttp://zaguan.unizar.es/record/65619/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000065619 909CO $$ooai:zaguan.unizar.es:65619$$particulos$$pdriver
000065619 951__ $$a2018-05-18-10:36:39
000065619 980__ $$aARTICLE