Chu, TianxingStarek, Michael J.Brewer, Michael J.Murray, Seth C.Pruter, Luke S.Chu, TianxingStarek, Michael J.Brewer, Michael J.Murray, Seth C.Pruter, Luke S.2021-11-102021-11-102021-11-102021-11-102018-05-062018-05-062018-05-06Chu, 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.Chu, 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.https://hdl.handle.net/1969.6/90012https://hdl.handle.net/1969.6/90012https://hdl.handle.net/1969.6/90012https://hdl.handle.net/1969.6/90012Unmanned 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.Unmanned 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-USAttribution 4.0 InternationalAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://creativecommons.org/licenses/by-nc-nd/4.0/analysisuasmaize fieldremote sensingunmanned aircraft system (uas)unmanned aircraft systemsdigital surface modelsanalysisuasmaize fieldremote sensingunmanned aircraft system (uas)unmanned aircraft systemsdigital surface modelsArticlehttps://doi.org/10.1080/2150704X.2018.1475771https://doi.org/10.1080/2150704X.2018.1475771https://doi.org/10.1080/2150704X.2018.1475771https://doi.org/10.1080/2150704X.2018.1475771