Measurement‐Based Evaluation of Photogrammetric Accuracy in Small Object Models Using Turntable and Manual Acquisition

We wished to determine whether the individual bias (mean difference) and precision (standard deviation of the difference) values of 2 variables, arterial oxygen saturation (SaO2) and mixed venous oxygen saturation (SvO2), could be used to predict the bias and precision values of the combined dual oximetry variable (SaO2-SvO2). We simultaneously measured SaO2 by pulse oximetry and arterial blood gas co-oximetry and SvO2 by fiberoptic reflectance oximetry pulmonary artery catheter and venous blood gas co-oximetry in 238 data sets from 55 patients. Three different methods were used to predict the standard deviation of the difference of (SaO2-SvO2) [s delta(SaO2-SvO2)]: simple sum, root mean square (RMS) error, and RMS error with correction term. We derived the equation for the RMS error with correction term because initial results showed that the simple sum and RMS error methods did not predict s delta(SaO2-SvO2) well. The correction term accounts for the non-independence of simultaneous SaO2 and SvO2 measurements. The observed overall bias of the SaO2, SvO2, and (SaO2-SvO2) measurement methods were 0.17, -1.76, and 1.94, respectively. The observed overall s delta(SaO2-SvO2) of the (SaO2-SvO2) measurement method was 5.12. The simple sum method overestimated the actual s delta(SaO2-SvO2) by 38%, the RMS error method differed from the actual s delta(SaO2-SvO2) by 3%, and the RMS error with correction term method matched the actual s delta(SaO2-SvO2). The bias of a (SaO2-SvO2) measurement method is simply the bias of the SaO2 measurement method less the bias of the SvO2 measurement method. s delta(SaO2-SvO2) is best predicted by the derived equation, RMS error with correction term. The same principles and equations also apply to other situations in which 2 variables with the same dimensions are combined into 1 variable, such as (PaCO2-EtCO2) gradients and perfusion-pressure gradients. Although the difference between the s delta(SaO2-SvO2) predicted by the RMS error equation and the derived RMS error equation with correction term was small, the difference may be significant for other combined variables.

In this paper we present a methodological workflow to obtain planimetric and topographic data from historical aerial photos using photogrammetric methods through Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms. This methodology is applied in two case studies located in the Upper Cinca River catchment (Southern Pyrenees). These sites have suffered a series of direct anthropogenic disturbances that have modified landscape topography. Specifically,topographic changes associated with the construction of a road and the extraction of materials from a pit quarry have been analysed. The resultant products extracted by the application of SfM-MVS are orthomosaics with a root mean square error (RMSE) between 0.5 and 1 m, and points clouds (topography) with a RMSE between 1 and 2 m. The topography before and after each of the impacts was compared with the objective to analyse the changes attributed to these disturbances. A simple minimum Level of Detection (minLoD) was estimated based on the RMSEs in order to distinguish potential real changes from those due to the noise ascribable to the uncertainty of the topographic data sets. The significance of both impacts is relevantin terms of topographic changes (from -20 to +15 m). The dominant topographic change (in extension) in the case of the road construction (51% of the surface) is extraction (i.e. erosion). In the case of the pit quarry, the dominant process is deposition (i.e. sedimentation; 27% of the area). The extension below the minLoD is around the 23% and 48%, respectively, indicating that the magnitude of the changesis substantially higher in the case of the road construction. In both cases the net volume is negative (-913,710 and -16,197 m3 in the case of the road and the quarry, respectively), that show the differences in terms of the extension of the processes and their magnitude in each case study. Finally, both impacts had a direct effect on landscape morphometry (e.g. changes in the slope and flow direction). The developed approach provides an opportunity to analysed and quantify landscape changes that may help to improve our understanding of the long-term evolution of the transfer of water and sediment through landscapes.

To establish reference intervals for amniotic sac volume (ASV) in early pregnancy (7-12weeks) and compare the accuracy, reproducibility, and clinical utility of manual and VOCAL ultrasound methods. This prospective observational study involved 68 singleton pregnancies from two maternal-fetal medicine centers. ASV was measured using manual tracing and virtual organ computer-aided analysis (VOCAL). Regression analysis was performed to determine reference intervals and assess correlations with gestational age and crown-rump length (CRL). Measurement accuracy was evaluated using mean absolute error (MAE) and root mean square error (RMSE), while inter- and intraobserver variability were assessed using intraclass correlation coefficients (ICC) and Bland-Altman analysis. ASV measurements demonstrated strong correlations with both gestational age and CRL (R2=0.999, p<0.001). VOCAL yielded higher accuracy (MAE: 0.35 cc; RMSE: 0.6 cc) and excellent reproducibility (ICC>0.90) compared to manual tracing, which exhibited greater variability (MAE: 16.94 cc; RMSE: 26.19 cc; ICC<0.50). Despite these limitations, manual methods may still offer clinical value in settings without access to advanced imaging technology. This study provides early pregnancy reference intervals for ASV and supports VOCAL as a reliable and precise method for volume assessment. Manual techniques, while less consistent, remain feasible in low-resource contexts. These findings may contribute to more accurate gestational age estimation and early detection of developmental abnormalities.

Unmanned Aerial Vehicle (UAV) has been widely used for slope stability analysis. The objective of this research is to test the digital surface model (DSM) results generated from UAV images with the data acquired from total station for a deformed slope. A slope along the Pan Borneo Highway was selected for the study. The UAV survey was undertaken by utilizing DJI Inspire 1 with Zenmuse X3 Gimbal. A total of 10 ground control points (GCPs) were marked during the surveying for validation purposes. Structure from motion (SfM) technique adopted Pix4D enterprise version 4.3.33 to stitch the images for the production of orthophotos and DSM. The root mean square error (RMSE) of the GCPs were checked, where the horizontal RMSE in x direction and y direction are 1.4 cm and 1.8 cm respectively while RMSE in z direction is 2.6 cm. The total station surveying was taken at various locations of slope, which include slope surface with slight to moderate deformation, slope surface with severe deformation, surface channel and at the edge of surface channel. The elevations of DSM results were tested with those surveying data acquired from site. The results show that for slope with slight to moderate deformation, the accuracy of the RMSE in elevation of 4.2 cm can be achieved. Similar RSME accuracy can be attained for surface channel which is 5 cm. However, the RMSE for slope portion with severe deformation is 10.6 cm. From this research, it is found that the UAV-based DSM lower accuracy will be attained for locations of sharp changes in elevation.KeywordsUnmanned aerial vehicle (UAV)Total stationPhotogrammetrySlope with deformation

Forest structural parameters are key indicators for forest growth assessment, and play a critical role in forest resources monitoring and ecosystem management. Terrestrial laser scanning (TLS) can obtain three-dimensional (3D) forest structures with ultra-high precision without destruction, whereas some shortcomings such as non-portability and cost-consuming can limit the quick and broad acquisition of forest structure. Structure from motion (SfM) and backpack laser scanning (BLS) technology have the advantages of low-cost and high-portability while obtaining 3D structure information of forests. In this study, the high-overlapped images and the BLS point cloud, combined with the point cloud registration and individual tree segmentation to extract the forest structural parameters and compared with the TLS for assessing the accuracy and efficiency of low-cost SfM and portable BLS point clouds. Three plots with different forest structural complexity (coniferous, broadleaf and mixed plot) in the northern subtropical forests were selected. Firstly, portable photography camera, BLS and TLS were used to acquire 3D SfM and LiDAR point clouds, and spatial co-registration of different-sourced point cloud datasets were carried out based on the understory markers. Secondly, the point clouds of individual tree trunk and crown were segmented by the comparative shortest-path algorithm (CSP), and then the height and position of individual tree were extracted based on the tree crown point cloud. Thirdly, the trunk diameter at different heights were calculated by point cloud slices using the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and combined with the stem curve of individual tree which was constructed using four Taper equations to estimate the individual tree volume. Finally, the extraction accuracy of forest structural parameters based on SfM and BLS point clouds were verified and comprehensively compared with field-measured and TLS data. The results showed that: (1) the individual tree segmentation based on SfM and BLS point clouds all performed quite well, among which the segmentation accuracy (F) of SfM point cloud was 0.80 and the BLS point cloud was 0.85; and (2) the accuracy of DBH and tree height extraction based on the SfM and BLS point clouds in comparison with the field-measured data were relatively high. The root mean square error (RMSE) of DBH and tree height extraction based on SfM point cloud were 2.15 cm and 4.08 m, and the RMSE of DBH and tree height extraction based on BLS point cloud were 2.06 cm and 1.63 m. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry can be applied quite well in extracting DBH.

Land surface temperature (LST) retrieval from NOAA-AVHRR data is mainly through so-called split window algorithms. During the last 20 years 17 split window algorithms has been published. These algorithms can be grouped into four categories: emissivity-dependent models, two-factors models, complicated models and radiance model. In this paper we intend to compare these split window algorithms in terms of their computation and accuracy. Two methods are used for the comparison: ground datasets and simulation datasets. Results from comparison shows that different algorithms have different performances under different situations. For simulation datasets, the algorithms of Qin et al. and Sobrino et al. are the best. The average root mean square (RMS) error of the two algorithms is less than 0.3degC. The algorithms of Franca and Cracknell, Prata and Uliverir et al. also have very low RMS errors (0.5-0.7degC). Results from comparison with ground datasets indicates that the algorithms of Qin et al. and Sobrino et al. are among the best for the dataset without precise in situ atmospheric water vapor contents. These algorithms are able to provide LST retrieval with average RMS error less than 1.9degC for the 361 measurements of the two Australian sites. An obvious contrast to the generally higher RMS error for the dataset is the much lower RMS error of the algorithms for the intensive experiments with precise in situ atmospheric water vapor contents. Based on the above two methods for comparison, it can be concluded that, comprehensively, the algorithm of Qin et al. is the best alternative for LST retrieval from AVHRR followed by Sobrino et al., Franca and Cracknell, and Prata when data are available to estimate both emissivity and transmittance

Evaluation of structure from motion (SfM) photogrammetry on the measurement of rill and interrill erosion in a typical loess

Among emerging 3D scanning and imaging techniques that are commercially available, simultaneous localization and mapping (SLAM) is being substantially studied to generate 2D/3D maps of an unknown environment while reliably keeping track of the user’s pinpoint locations. Its ubiquitous mobility has demonstrated great mapping capabilities for infrastructures where vertical information may frequently be occluded using unmanned aircraft system (UAS) structure from-motion (SfM) photogrammetry. In addition, indoor mapping with terrestrial laser scanning can be a cumbersome task due to possible multiple scan locations. Intending to provide a cohesive 3D model by fusing point clouds collected via aerial SfM photogrammetry, terrestrial laser scanning (TLS), and SLAM, the purpose of this work is to assess the performance of SLAM point cloud generated by a proprietary mobile backpack laser scanner (BLS). Considering maximum scanning range and information integration strategy as variables, the point clouds generated by the BLS were evaluated against SfM and TLS datasets in terms of the internal consistency as well as external accuracy. TLS, SfM and SAM data collection efforts were made in a typical university campus environment. For the internal consistency, the SLAM-based point cloud with a maximum scanning range of 70 m presented a root mean square error (RMSE) of 2 mm. The SLAM+GNSS-based point cloud presented the lowest internal precision of RMSE = 0.861 m. The SLAM+GNSS 70 point cloud after a fine adjustment of misalignment presented the highest vertical accuracy with an RMSE = 0.069 m, while the point cloud generated from SfM photogrammetry presented RMSE = 0.297 m. The BLS was able to generate point cloud with an accuracy similar to GNSS-RTK surveying and it can be considered as a viable solution for indoor and outdoor mapping applications.

This paper proposes a new Asian single site tropospheric correction model called the Single Site Improved European Geostationary Navigation Overlay Service model (SSIEGNOS) by refining the European Geostationary Navigation Overlay Service (EGNOS) model at a single site. The performance of the SSIEGNOS model is analyzed. The results show that (1) the bias and root mean square (RMS) error of zenith tropospheric delay (ZTD) calculated from the EGNOS model are 0.12 cm and 5.87 cm, respectively; whereas those of the SSIEGNOS model are 0 cm and 2.52 cm, respectively. (2) The bias and RMS error show seasonal variation in the EGNOS model; however, little seasonal variation is observed in the SSIEGNOS model. (3) The RMS error decreases with increasing altitude or latitude in the two models; however, no such relationships were found in the bias. In addition, the annual predicted bias and RMS error in Asia are −0.08 cm and 3.14 cm for the SSIEGNOS model, respectively; however, the EGNOS and UNB3m (University of New Brunswick) models show comparable predicted results. Relative to the EGNOS model, the annual predicted bias and RMS error decreased by 55% and 48%, respectively, for the SSIEGNOS model.

Background and purposeThe study evaluated the differences in leaf positioning deviations by the log files of three advanced accelerators with two delivery techniques, and established specific assessment parameters of leaf positioning deviations for different types of accelerators.MethodsA total of 420 treatment plans with 5 consecutive treatment log files were collected from the Trilogy, TrueBeam and Halcyon accelerators. Millennium MLC was equipped on the Trilogy and TrueBeam accelerators. A jawless design and dual-layer MLC were adopted on the Halcyon accelerator. 70 IMRT and 70 VMAT plans were selected randomly on each accelerator. The treatment sites of all plans included head and neck, chest, breast, pelvis and other sites. The parsing tasks for 2100 log files were proceeded by SunCheck software from Sun Nuclear Corporation. The maximum leaf root mean square (RMS) errors, 95th percentile errors and percentages of different leaf positioning errors were statistically analyzed. The correlations between these evaluation parameters and accelerator performance parameters (maximum leaf speed, mean leaf speed, gantry and arc angle) were analyzed.ResultsThe average maximum leaf RMS errors of the Trilogy in the IMRT and VMAT plans were 0.44 ± 0.09 mm and 0.79 ± 0.07 mm, respectively, which were higher than the TrueBeam's 0.03 ± 0.01 mm, 0.03 ± 0.01 mm and the Halcyon's 0.05 ± 0.01 mm, 0.07 ± 0.01 mm. Similar data results were shown in the 95th percentile error. The maximum leaf RMS errors were strongly correlated with the 95th percentile errors (Pearson index > 0.5). The leaf positioning deviations in VMAT were higher than those in IMRT for all accelerators. In TrueBeam and Halcyon, leaf position errors above 1 mm were not found in IMRT and VMAT plans. The main influencing factor of leaf positioning deviation was the leaf speed, which has no strong correlation with gantry and arc angles.ConclusionsCompared with the quality assurance guidelines, the MLC positioning deviations tolerances of the three accelerators should be tightened. For both IMRT and VMAT techniques, the 95th percentile error and the maximum RMS error are suggested to be tightened to 1.5 and 1 mm respectively for the Trilogy accelerator. In TrueBeam and Halcyon accelerators, the 95th percentile error and maximum RMS error of 1 and 0.5 mm, respectively, are considered appropriate.

BackgroundThe multileaf collimator (MLC) is a critical component to accurate intensity-modulated radiotherapy (IMRT) delivery. This study examined MLC positional accuracy via MLC logs from Varian machines from six institutions and three delivery techniques to evaluate typical positional accuracy and treatment and mechanical parameters that affect accuracy. Typical accuracy achieved was compared against TG-142 recommendations for MLC performance; more appropriate recommendations are suggested.MethodsOver 85,000 Varian MLC treatment logs were collected from six institutions and analyzed with FractionCHECK. Data were binned according to institution and treatment type to determine overall root mean square (RMS) and 95th percentile error values, and then to look for correlations between those errors and with mechanical and treatment parameters including mean and maximum leaf speed, gantry angle, beam-on time, mean leaf error, and number of segments.ResultsResults of treatment logs found that leaf RMS error and 95th percentile leaf error were consistent between institutions, but varied by treatment type. The step and shoot technique had very small errors: the mean RMS leaf error was 0.008 mm. For dynamic treatments the mean RMS leaf error was 0.32 mm, while volumetric-modulated arc treatment (VMAT) showed an RMS leaf error of 0.46 mm. Most MLC leaf errors were found to be well below TG-142 recommended tolerances. For the dynamic and VMAT techniques, the mean and maximum leaf speeds were significantly linked to the leaf RMS error. Additionally, for dynamic delivery, the mean leaf error was correlated with RMS error, whereas for VMAT the average gantry speed was correlated. For all treatments, the RMS error and the 95th percentile leaf error were correlated.ConclusionsRestricting the maximum leaf speed can help improve MLC performance for dynamic and VMAT deliveries. Furthermore, the tolerances of leaf RMS and error counts for all treatment types should be tightened from the TG-142 values to make them more appropriate for clinical performance. Values of 1 mm for the 95th percentile of leaf RMS error and 1.5 mm for the 95th percentile leaf error are suggested as action levels for all treatment types.

Purpose: This study examined MLC positional accuracy via MLC logs from multiple institutions and multiple delivery techniques to evaluate typical positional accuracy and treatment and mechanical parameters that affect accuracy. Typical accuracy achieved was compared against TG‐142 recommendations for MLC performance; more appropriate recommendations are suggested. Methods: Over 85,000 Varian MLC treatment logs were collected from six institutions and analyzed with Fraction CHECK. Data were binned according to institution and treatment type to determine root mean square (RMS) and 95th percentile error values, and then to look for correlations between those errors and with mechanical and treatment parameters. Results: Results of treatment logs from six institutions found that leaf RMS error and 95th percentile leaf error were fairly consistent between institutions, but varied by treatment type. The step and shoot technique had very small errors: the mean RMS leaf error was 0.008 mm. For dynamic treatments the mean RMS leaf error was 0.32 mm, while VMAT showed the largest mean RMS leaf error at 0.46 mm. For the dynamic and VMAT techniques, the mean and maximum leaf speeds were significantly linked to the leaf RMS error. For dynamic delivery, the mean leaf error was correlated with RMS error, whereas for VMAT the average gantry speed was correlated. For all treatments, the RMS error and the 95th percentile leaf error were correlated. Conclusion: Restricting the maximum leaf speed can help improve MLC performance for dynamic and VMAT deliveries. Furthermore, the tolerances of leaf RMS and error counts for all treatment types should be tightened from the TG‐142 values to make them more appropriate for clinical performance. Values of 1 mm for the 95th percentile of leaf RMS error and 1.5 mm for the 95th percentile error are suggested as action levels for all treatment types.

An assessment of conventional and drone-based measurements for tree attributes in timber volume estimation: A case study on stone pine plantation

Measuring humeral head translation using fluoroscopy: A validation study

The use of fixed–wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer–broadleaf forest