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Open AccessArticle

Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes

1
Division for Forest Management and Forestry Economics, Croatian Forest Research Institute, Trnjanska cesta 35, HR-10000 Zagreb, Croatia
2
Chair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, Kačićeva 26, HR-10000 Zagreb, Croatia
3
School of Earth, Environment and Society, Bowling Green State University, 190 Overman Hall, Bowling Green, OH 43403, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 404; https://doi.org/10.3390/rs12030404
Received: 19 December 2019 / Revised: 10 January 2020 / Accepted: 24 January 2020 / Published: 27 January 2020
(This article belongs to the Section Forest Remote Sensing)
The quality and accuracy of Unmanned Aerial System (UAS) products greatly depend on the methods used to define image orientations before they are used to create 3D point clouds. While most studies were conducted in non- or partially-forested areas, a limited number of studies have evaluated the spatial accuracy of UAS products derived by using different image block orientation methods in forested areas. In this study, three image orientation methods were used and compared: (a) the Indirect Sensor Orientation (InSO) method with five irregularly distributed Ground Control Points (GCPs); (b) the Global Navigation Satellite System supported Sensor Orientation (GNSS-SO) method using non-Post-Processed Kinematic (PPK) single-frequency carrier-phase GNSS data (GNSS-SO1); and (c) using PPK dual-frequency carrier-phase GNSS data (GNSS-SO2). The effect of the three methods on the accuracy of plot-level estimates of Lorey’s mean height (HL) was tested over the mixed, even-aged pedunculate oak forests of Pokupsko basin located in Central Croatia, and validated using field validation across independent sample plots (HV), and leave-one-out cross-validation (LOOCV). The GNSS-SO2 method produced the HL estimates of the highest accuracy (RMSE%: HV = 5.18%, LOOCV = 4.06%), followed by the GNSS-SO1 method (RMSE%: HV = 5.34%, LOOCV = 4.37%), while the lowest accuracy was achieved by the InSO method (RMSE%: HV = 5.55%, LOOCV = 4.84%). The negligible differences in the performances of the regression models suggested that the selected image orientation methods had no considerable effect on the estimation of HL. The GCPs, as well as the high image overlaps, contributed considerably to the block stability and accuracy of image orientation in the InSO method. Additional slight improvements were achieved by replacing single-frequency GNSS measurements with dual-frequency GNSS measurements and by incorporating PPK into the GNSS-SO2 method. View Full-Text
Keywords: Unmanned Aerial System (UAS); photogrammetry; Structure from Motion (SfM); plot-level Lorey’s mean height; hold-out validation; leave-one-out-cross validation; pedunculate oak (Quercus robur L.) Unmanned Aerial System (UAS); photogrammetry; Structure from Motion (SfM); plot-level Lorey’s mean height; hold-out validation; leave-one-out-cross validation; pedunculate oak (Quercus robur L.)
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MDPI and ACS Style

Jurjević, L.; Gašparović, M.; Milas, A.S.; Balenović, I. Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes. Remote Sens. 2020, 12, 404.

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