Show simple item record

dc.contributor.authorLudwig, Marvin
dc.contributor.authorRunge, Christian M.
dc.contributor.authorFriess, Nicolas
dc.contributor.authorKoch, Tiziana L.
dc.contributor.authorRichter, Sebastian
dc.contributor.authorSeyfried, Simon
dc.contributor.authorWraase, Luise
dc.contributor.authorLobo, Agustin
dc.contributor.authorSebastià, Ma. T.
dc.contributor.authorReudenbach, Christoph
dc.contributor.authorNauss, Thomas
dc.date.accessioned2021-01-14T07:41:00Z
dc.date.available2021-01-14T07:41:00Z
dc.date.issued2020-11-22
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10459.1/70194
dc.description.abstractUnmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.ca_ES
dc.description.sponsorshipThis research was funded by the Hessian State Ministry for Higher Education, Research and the Arts, Germany, as part of the LOEWE priority project Nature 4.0—Sensing Biodiversity. The grassland study was funded by the Spanish Science Foundation FECYT-MINECO through the BIOGEI (GL2013- 49142-C2-1-R) and IMAGINE (CGL2017-85490-R) projects, and by the University of Lleida; and supported by a FI Fellowship to C.M.R. (2019 FI_B 01167) by the Catalan Government.ca_ES
dc.language.isoengca_ES
dc.publisherMDPIca_ES
dc.relationMINECO/PN2013-2016/GL2013-49142-C2-1-Rca_ES
dc.relationMINECO/PN2017-2020/CGL2017-85490-Rca_ES
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.3390/rs12223831ca_ES
dc.relation.ispartofRemote Sensing, 2020, vol. 12, núm. 22, p. 3831ca_ES
dc.rightscc-by, (c) Ludwig et al., 2020ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectUnmanned aerial systemsca_ES
dc.subjectUnmanned aerial vehicleca_ES
dc.subjectTime seriesca_ES
dc.subjectAccuracyca_ES
dc.subjectReproducibilityca_ES
dc.subjectOrthomosaicca_ES
dc.subjectValidationca_ES
dc.subjectPhotogrammetryca_ES
dc.titleQuality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaicsca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.identifier.idgrec031229
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.3390/rs12223831


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

cc-by, (c) Ludwig et al., 2020
Except where otherwise noted, this item's license is described as cc-by, (c) Ludwig et al., 2020