Characterizing canopy height with UAS structure from-motion photogrammetry—results analysis of a maize field trial with respect to multiple factors

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dc.contributor.authorChu, Tianxing
dc.contributor.authorStarek, Michael J.
dc.contributor.authorBrewer, Michael J.
dc.contributor.authorMurray, Seth C.
dc.contributor.authorPruter, Luke S.
dc.contributor.authorChu, Tianxing
dc.contributor.authorStarek, Michael J.
dc.contributor.authorBrewer, Michael J.
dc.contributor.authorMurray, Seth C.
dc.contributor.authorPruter, Luke S.
dc.date.accessioned2021-11-10T20:01:58Z
dc.date.accessioned2021-11-10T20:01:58Z
dc.date.available2021-11-10T20:01:58Z
dc.date.available2021-11-10T20:01:58Z
dc.date.issued2018-05-06
dc.date.issued2018-05-062018-05-06
dc.date.issued2018-05-06
dc.description.abstractUnmanned aircraft system (UAS) measured canopy height has frequently been determined by means of digital surface models (DSMs) derived from structure-from-motion (SfM) photogrammetry without examining specific metrics in detail. Multiple geospatial factors to be considered for the purpose of generating an accurate height estimation were characterized and summarized in this letter using UAS-SfM photogrammetry over an experimental maize field trial. This particular study demonstrated that: 1) the 99th percentile height in a 25 cm-wide crop row polygon provided the best canopy height estimation accuracy; 2) the height difference between using a rasterized DSM and direct three-dimensional (3D) point cloud was minor yet steadily increased when the DSM resolution value grew; and 3) the accuracy of the DSM-based canopy height estimation dropped significantly after the DSM resolution became coarser than 12 cm. Results also suggested that the cost function introduced in this letter has the potential to be used for optimizing the height estimation accuracy of various crop types given ground truth.en_US
dc.description.abstractUnmanned aircraft system (UAS) measured canopy height has frequently been determined by means of digital surface models (DSMs) derived from structure-from-motion (SfM) photogrammetry without examining specific metrics in detail. Multiple geospatial factors to be considered for the purpose of generating an accurate height estimation were characterized and summarized in this letter using UAS-SfM photogrammetry over an experimental maize field trial. This particular study demonstrated that: 1) the 99th percentile height in a 25 cm-wide crop row polygon provided the best canopy height estimation accuracy; 2) the height difference between using a rasterized DSM and direct three-dimensional (3D) point cloud was minor yet steadily increased when the DSM resolution value grew; and 3) the accuracy of the DSM-based canopy height estimation dropped significantly after the DSM resolution became coarser than 12 cm. Results also suggested that the cost function introduced in this letter has the potential to be used for optimizing the height estimation accuracy of various crop types given ground truth.
dc.identifier.citationChu, T., Starek, M.J., Brewer, M.J., Murray, S.C. and Pruter, L.S., 2018. Characterizing canopy height with UAS structure-from-motion photogrammetry—results analysis of a maize field trial with respect to multiple factors. Remote Sensing Letters, 9(8), pp.753-762.en_US
dc.identifier.citationChu, T., Starek, M.J., Brewer, M.J., Murray, S.C. and Pruter, L.S., 2018. Characterizing canopy height with UAS structure-from-motion photogrammetry—results analysis of a maize field trial with respect to multiple factors. Remote Sensing Letters, 9(8), pp.753-762.
dc.identifier.doihttps://doi.org/10.1080/2150704X.2018.1475771
dc.identifier.doihttps://doi.org/10.1080/2150704X.2018.1475771https://doi.org/10.1080/2150704X.2018.1475771
dc.identifier.doihttps://doi.org/10.1080/2150704X.2018.1475771
dc.identifier.urihttps://hdl.handle.net/1969.6/90012
dc.identifier.urihttps://hdl.handle.net/1969.6/90012https://hdl.handle.net/1969.6/90012
dc.identifier.urihttps://hdl.handle.net/1969.6/90012
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherTaylor & Francisen_US
dc.publisherTaylor & Francis
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectanalysisen_US
dc.subjectuasen_US
dc.subjectmaize fielden_US
dc.subjectremote sensingen_US
dc.subjectunmanned aircraft system (uas)en_US
dc.subjectunmanned aircraft systemsen_US
dc.subjectdigital surface modelsen_US
dc.subjectanalysis
dc.subjectuas
dc.subjectmaize field
dc.subjectremote sensing
dc.subjectunmanned aircraft system (uas)
dc.subjectunmanned aircraft systems
dc.subjectdigital surface models
dc.titleCharacterizing canopy height with UAS structure from-motion photogrammetry—results analysis of a maize field trial with respect to multiple factorsen_US
dc.titleCharacterizing canopy height with UAS structure from-motion photogrammetry—results analysis of a maize field trial with respect to multiple factors
dc.typeArticleen_US
dc.typeArticle

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