Demystifying the Differences between Structure-from-Motion Software Packages for Pre-Processing Drone Data

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows.

Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEMavg (k = 0.542 and IoU = 0.667), and DEMmin (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning.

Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2–0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R2 = 0.20–0.25) and higher error (RMSE = 700–800 kg/ha) than training models (R2 = 0.50–0.60; RMSE = 500–690 kg/ha). In South Shore, validation models were only able to explain approx. 15–20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass.

Effect of Microwave-Assisted Aqueous Two-Phase Extraction of α-Solanine from S. retroflexum and Analysis on UHPLC-qTOF-MS

A new, fast and efficient method, hyphenated microwave-assisted aqueous two-phase extraction (MA-ATPE) was applied in the extraction of α-solanine from Solanum retroflexum. This environmentally friendly extraction method applied water and ethanol as extraction solvents. Central composite design (CCD) was performed which included numerical parameters such as time, mass of plant powder and microwave power. The categorical factors included the chaotrope — NaCl or the kosmotrope — Na2CO3. Fitting the central composite design response surface model to the data generated a quadratic model with a good fit (R2 = 0.920). The statistically significant (p < 0.05) parameters such as time and mass of plant powder were influential in the extraction of α-solanine. Quantification of α-solanine was achieved using a robust and sensitive feature of the ultra-high performance quadrupole time of flight mass spectrometer (UHPLC-qTOF-MS), multiple reaction monitoring (MRM). The optimized condition for the extraction of α-solanine in the presence of NaCl and Na2CO3 was a period of 1 min at a mass of 1.2 g using a microwave power of 40%. Maximal extraction of α-solanine was 93.50 mg kg−1 and 72.16 mg kg−1 for Na2CO3 and NaCl, respectively. The synergistic effect of salting-out and microwave extraction was influential in extraction of α-solanine. Furthermore, the higher negative charge density of the kosmotrope (Na2CO3) was responsible for its greater extraction of α-solanine than chaotrope (NaCl). The shorter optimal extraction times of MA-ATPE make it a potential technique that could meet market demand as it is a quick, green and efficient method for removal of toxic metabolites in nutraceuticals.

Dynamic Characteristics Analysis of a Rigid Body System with Spatial Multi-Point Supports

This paper presents the dynamic characteristics analysis of a rigid body system with spatial multi-point elastic supports, as well as the sensitivity analysis of support parameters. A rigid object is characterized by six degrees-of-freedom (DOFs) motions and considering the spatial location vector decomposition of elastic supports, a rigid body system dynamic model with spatial multi-point elastic supports is derived via the Lagrangian energy method. The system modal frequencies are calculated, and to be verified by finite element modal analysis results. Next, based on the above-mentioned model, system modal frequencies are obtained under different support locations, where the support stiffness components are different. Interpolate the stiffness components corresponding to each support location, calculate system modal frequencies, and the response surface model (RSM) for system modal frequencies is established. Further, based on the RSM modal analysis results, the allowable support location for the system modal insensitive area can be obtained. At last, a lubricating oil-tank system with four supports is taken as an example, and the effects of support spatial locations and stiffness components on the system inherent characteristics are discussed. This present work can provide a basis for the dynamic design of the spatial location and stiffness for this type of installation structures.

Assessment of Aquatic Reed Stands from Airborne Photogrammetric 3K Data

Aquatic reed beds provide important ecological functions, yet their monitoring by remote sensing methods remains challenging. In this study, we propose an approach of assessing aquatic reed stand status indicators based on data from the airborne photogrammetric 3K-system of the German Aerospace Center (DLR). By a Structure from Motion (SfM) approach, we computed stand surface models of aquatic reeds for each of the 14 areas of interest (AOI) investigated at Lake Chiemsee in Bavaria, Germany. Based on reed heights, we subsequently calculated the reed area, surface structure homogeneity and shape of the frontline. For verification, we compared 3K aquatic reed heights against reed stem metrics obtained from ground-based infield data collected at each AOI. The root mean square error (RMSE) for 1358 reference points from the 3K digital surface model and the field-measured data ranged between 39 cm and 104 cm depending on the AOI. Considering strong object movements due to wind and waves, superimposed by water surface effects such as sun glint altering 3K data, the results of the aquatic reed surface reconstruction were promising. Combining the parameter height, area, density and frontline shape, we finally calculated an indicator for status determination: the aquatic reed status index (aRSI), which is based on metrics, and thus is repeatable and transferable in space and time. The findings of our study illustrate that, even under the adverse conditions given by the environment of the aquatic reed, aerial photogrammetry can deliver appropriate results for deriving objective and reconstructable parameters for aquatic reed status (Phragmites australis) assessment.

Airfoil Surface Forming and Conformity Test Using Laser Tracker

In this study, NACA0018 airfoil surface conformity test was conducted using API tracker3 in combination with SpatialAnalyzer (SA) and modeling software SolidWorks. Plaster of Paris is used as a plug making material and a woven-type fiberglass is used as mold and airfoil surface making material. For airfoil surface analysis, three-dimensional model of the airfoil surface was developed in SolidWorks software and imported in IGES file format to SpatialAnalyzer (SA) software. Then, measurements were taken from manufactured airfoil surface using laser tracker through surface scanning method. Surface conformity test was conducted through fitting of measured points to surface model imported from SolidWorks to SpatialAnalyzer (SA) software. The optimized fit summary result shows that the average fit difference is 0.0 having standard deviation from 0.22224 from the average and zero with RMS of 0.2210. The maximum magnitude of the difference including x and y together is 0.5336 and the minimum −0.5077. Thus, with a given range of surface quality specification, laser tracker is an easy and reliable measurement and inspection tool to be considered.