The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM

Gašparović, Mateo; Seletković, Ante; Berta, Alen; Balenović, Ivan

doi:10.15177/seefor.17-16

Cited by 18 publications

(11 citation statements)

References 34 publications

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“…Although widely used, remote sensing-based research remains challenging due to different properties of the sensors and the structural complexity of forests [1,2]. Over the last twenty number of studies have evaluated the spatial accuracy of UAS products derived by using different image block orientation methods in forested areas [38,39]. Gašparović et al [38] evaluated the vertical accuracy of the Digital Surface Model (DSM) generated from UAS images collected with the low-cost UAS (DJI Phantom 4 Pro) over a dense forested area.…”

Section: Introductionmentioning

confidence: 99%

“…Over the last twenty number of studies have evaluated the spatial accuracy of UAS products derived by using different image block orientation methods in forested areas [38,39]. Gašparović et al [38] evaluated the vertical accuracy of the Digital Surface Model (DSM) generated from UAS images collected with the low-cost UAS (DJI Phantom 4 Pro) over a dense forested area. When GNSS-SO approaches with no GCPs and with seven irregularly distributed GCPs were compared, a considerable improvement in the DSM vertical accuracy with GCPs was observed.…”

Section: Introductionmentioning

confidence: 99%

“…When GNSS-SO approaches with no GCPs and with seven irregularly distributed GCPs were compared, a considerable improvement in the DSM vertical accuracy with GCPs was observed. Gašparović et al [38] concluded that DSMs generated using GNSS-SO without GCPs can be used for visualization and monitoring purposes, whereas DSMs generated using GCPs have greater potential to be used in forest inventory. Another recent study that evaluated the spatial accuracy of UAS products using different image block orientation methods in a forested area was conducted by Tomaštik et al [39].…”

Section: Introductionmentioning

confidence: 99%

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Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes

et al. 2020

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“…Due to the relatively low cost and wide array of sensors available, the deployment and evaluation of structure-from-motion (SfM) photogrammetry from UAV systems has been more broadly discussed in the published research [28][29][30][31][32][33]. Som-ard et al [34], and Shi et al [35], for example, utilised UAV-based SfM imagery with ground observations to derive biophysical measures of a sugarcane crop and a variety of sensor types have been applied from UAVs in agricultural research as reviewed by Hunt and Daughtry [36].…”

Section: Imaging From Uavsmentioning

confidence: 99%

“…Well documented in the literature, significant progress has been made in the collection techniques and performance evaluation of photogrammetric imagery systems [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Examining the utility of Simultaneous Localisation and Mapping (SLAM) LiDAR on a low-altitude unmanned aerial vehicle (UAV) across environments of various composition and dynamic complexity

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Rapid technological advancements in aerospace technology and imagery capture have seen a dramatic rise in sensor types available, which has expanded the potential use unmanned aerial vehicles (UAVs) in remote sensing research applications. The proliferation of this technology has resulted in numerous instrument and configuration options available across a broad range of scientific, industrial and management applications, with each configuration having specific strengths and limitations. Information provided by manufacturers, however, is often limited, providing little, or no, information on how these systems may perform across different environments or operating conditions. As a result,

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