Search

An inverse measurement procedure for the determination of a full set of piezoelectric material parameters using a single sample is presented. The basis for the measurement procedure is a measurement of the frequency-dependent impedance of the sample. To yield sufficient sensitivity of this measurement with respect to all material parameters (mechanical, electrical, and coupling parameters), an optimal electrode configuration for the sample is determined before the inverse measurement procedure is realised using a novel topology optimisation approach. After initial estimates for the material parameters are provided by analytical expressions, a sensitivity-based, staged, local optimisation procedure yields material parameters for the sample by fitting the impedance of a finite element simulation model to the measured electrical impedance. Results for different absorption models as well as for different piezoelectric materials (hard, soft, and lead-free piezoceramics) are included.

In this paper, we investigate the usage of unmanned aerial vehicles (UAV) to assess the crop geometry with special focus on the crop height extraction. Crop height is classified as a reliable trait in crop phenotyping and recognized as a good indicator for biomass, expected yield, lodging or crop stress. The current industrial standard for crop height measurement is a manual procedure using a ruler, but this method is considered as time consuming, labour intensive and subjective. This study investigates methods for reliable and rapid deriving of the crop height from high spatial, spectral and time resolution UAV data considering the influences of the reference surface and the selected crop height generation method to the final calculation. To do this, we performed UAV missions during two winter wheat growing seasons and generate point clouds from areal images using photogrammetric methods. For the accuracy assessment we compare UAV based crop height with ruler based crop height as current industrial standard and terrestrial laser scanner (TLS) based crop height as a reliable validation method. The high correlation between UAV based and ruler based crop height and especially the correlation with TLS data shows that the UAV based crop height extraction method can provide reliable winter wheat height information in a non-invasive and rapid way. Along with crop height as a single value per area of interest, 3D UAV crop data should provide some additional information like lodging area, which can also be of interest in the plant breeding community.

Terrestrial laser scanner (TLS) measurements are unavoidably affected by systematic influences due to internal misalignments. The magnitude of the resulting errors can exceed the magnitude of random errors significantly deteriorating the quality of the obtained point clouds. Hence, the task of calibrating TLSs is important for applications with high demands regarding accuracy. In recent years, multiple in-situ self-calibration approaches were derived allowing the successful estimation of up-to-date calibration parameters. These approaches rely either on using manually placed targets or on using man-made geometric objects found in surroundings. Herein, we widen the existing toolbox with an alternative approach for panoramic TLSs, for the cases where such prerequisites cannot be met. We build upon the existing target-based two-face calibration method by substituting targets with precisely localized 2D keypoints, i.e. local features, detected in panoramic intensity images using the Förstner operator. To overcome the detriment of the perspective change on the feature localization accuracy, we estimate the majority of the relevant calibration parameters from a single station. The approach is verified on real data obtained with the Leica ScanStation P20. The obtained results were tested against the affirmed target-based two-face self-calibration. Analysis proved that the estimated calibration parameters are directly comparable both in the terms of parameter precision and correlation. In the end, we employ an effective evaluation procedure for testing the impact of the calibration results on the point cloud quality.

For some years now, UAVs (unmanned aerial vehicles) are commonly used for different mobile mapping applications, such as in the fields of surveying, mining or archeology. To improve the efficiency of these applications an automation of the flight as well as the processing of the collected data is currently aimed at. One precondition for an automated mapping with UAVs is that the georeferencing is performed directly with cm-accuracies or better. Usually, a cm-accurate direct positioning of UAVs is based on an onboard multi-sensor system, which consists of an RTK-capable (real-time kinematic) GPS (global positioning system) receiver and additional sensors (e.g. inertial sensors). In this case, the absolute positioning accuracy essentially depends on the local GPS measurement conditions. Especially during mobile mapping applications in urban areas, these conditions can be very challenging, due to a satellite shadowing, non-line-of sight receptions, signal diffraction or multipath effects. In this paper, two straightforward and easy to implement strategies will be described and analyzed, which improve the direct positioning accuracies for UAV-based mapping and surveying applications under challenging GPS measurement conditions. Based on a 3D model of the surrounding buildings and vegetation in the area of interest, a GPS geometry map is determined, which can be integrated in the flight planning process, to avoid GPS challenging environments as far as possible. If these challenging environments cannot be avoided, the GPS positioning solution is improved by using obstruction adaptive elevation masks, to mitigate systematic GPS errors in the RTK-GPS positioning. Simulations and results of field tests demonstrate the profit of both strategies.

We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.

In recent years, unmanned aerial vehicles (UAVs) have increasingly been used in various application areas, such as in the remote sensing or surveying. For these applications the UAV has to be equipped with a mapping sensor, which is mostly a camera. Furthermore, a georeferencing of the UAV platform and/or the acquired mapping data is required. The most efficient way to realize this georeferencing is the direct georeferencing, which is based on an onboard multi-sensor system. In recent decades, direct georeferencing systems have been researched and used extensively in airborne, ship and land vehicle applications. However, these systems cannot easily be adapted to UAV platforms, which is mainly due to weight and size limitations. In this paper a direct georeferencing system for micro- and mini-sized UAVs is presented, which consists of a dual-frequency geodetic grade OEM GPS board, a low-cost single-frequency GPS chip, a tactical grade IMU and a magnetometer. To allow for cm-level position and sub-degree attitude accuracies, RTK GPS (real-time kinematic) and GPS attitude (GPS compass) determination algorithms are running on this system, as well as a GPS/IMU integration. Beside the direct georeferencing, also the precise time synchronization of the camera, which acts as the main sensor for mobile mapping applications, and the calibration of the lever arm between the camera reference point and the direct georeferencing reference point are explained in this paper. Especially the high accurate time synchronization of the camera is very important, to still allow for high surveying accuracies, when the images are taken during the motion of the UAV. Results of flight tests demonstrate that the developed system, the camera synchronization and the lever arm calibration make directly georeferenced UAV based single point measurements possible, which have cm-level accuracies on the ground.

In tropical eastern Africa, vegetation distribution is largely controlled by regional hydrology, which has varied over the past 20 000 years. Therefore, accurate reconstructions of past vegetation and hydrological changes are crucial for a better understanding of climate variability in the tropical southeastern African region. We present high-resolution pollen records from a marine sediment core recovered offshore of the Rufiji River delta. Our data document significant shifts in pollen assemblages during the last deglaciation, identifying, through changes in both upland and lowland vegetation, specific responses of plant communities to atmospheric (precipitation) and coastal (coastal dynamics and sea-level changes) alterations. Specifically, arid conditions reflected by a maximum pollen representation of dry and open vegetation occurred during the Northern Hemisphere cold Heinrich event 1 (H1), suggesting that the expansion of drier upland vegetation was synchronous with cold Northern Hemisphere conditions. This arid period is followed by an interval in which forest and humid woodlands expanded, indicating a hydrologic shift towards more humid conditions. Droughts during H1 and the shift to humid conditions around 14.8 kyr BP in the uplands are consistent with latitudinal shifts of the intertropical convergence zone (ITCZ) driven by high-latitude Northern Hemisphere climatic fluctuations. Additionally, our results show that the lowland vegetation, consisting of well-developed salt marshes and mangroves in a successional pattern typical for vegetation occurring in intertidal habitats, has responded mainly to local coastal dynamics related to marine inundation frequencies and soil salinity in the Rufiji Delta as well as to the local moisture availability. Lowland vegetation shows a substantial expansion of mangrove trees after ~ 14.8 kyr BP, suggesting an increased moisture availability and river runoff in the coastal area. The results of this study highlight the decoupled climatic and environmental processes to which the vegetation in the uplands and the Rufiji Delta has responded during the last deglaciation.

In many unmanned aerial vehicle (UAV) applications a direct georeferencing is required. The reason can be that the UAV flies autonomous and must be navigated precisely, or that the UAV performs a remote sensing operation, where the position of the camera has to be known at the moment of the recording. In our application, a project called Mapping on Demand, we are motivated by both of these reasons. The goal of this project is to develop a lightweight autonomously flying UAV that is able to identify and measure inaccessible three-dimensional objects by use of visual information. Due to payload and space limitations, precise position and attitude determination of micro- and mini-sized UAVs is very challenging. The limitations do not only affect the onboard computing capacity, but they are also noticeable when choosing the georeferencing sensors. In this article, we will present a new developed onboard direct georeferencing system which is real-time capable, applicable for lightweight UAVs and provides very precise results (position accuracy σ < 5 cm and attitude accuracy σ < 0.5 deg). In this system GPS, inertial sensors, magnetic field sensors, a barometer as well as stereo video cameras are used as georeferencing sensors. We will describe the hardware development and will go into details of the implemented software. In this context especially the RTK-GPS software and the concept of the attitude determination by use of inertial sensors, magnetic field sensors as well as an onboard GPS baseline will be highlighted. Finally, results of first field tests as well as an outlook on further developments will conclude this contribution.

IntroductionThe COVID-19 pandemic with its containment measures such as closures of schools and daycare facilities led to numerous restrictions in daily life, putting developmental opportunities and health-related quality of life in children at risk. However, studies show that not every family was impacted equally by the pandemic and that this exceptional health and societal situation reinforced pre-existing health inequalities among the vulnerable. Our study aimed at analyzing changes in behavior and health-related quality of life of children attending elementary schools and daycare facilities in Bavaria, Germany in spring 2021. We also sought to identify associated factors contributing to inequalities in quality of life.MethodsData from a multi-center, open cohort study (“COVID Kids Bavaria”) conducted in 101 childcare facilities and 69 elementary schools across all electoral districts of Bavaria were analyzed. Children attending these educational settings (aged 3-10 years) were eligible for participation in a survey on changes in behavior and health-related quality of life. The KINDLR questionnaire (based on children’s self-report and parental report) was administered about one year after the onset of the pandemic (spring 2021). Descriptive and logistic regression analyses and comparisons to pre-pandemic KiGGS (German Health Interview and Examination Survey for Children and Adolescents) data were undertaken.ResultsAmong respondents, a high percentage of parents reported changes in their children's eating and sleeping behavior, sports and outdoor activities as well as altered screen time. Health-related quality of life in KINDLR analyses compared to pre-pandemic population averages were lower in all age groups (for 3–6-year-old KINDLR-total score: COVID Kids Bavaria MD 74.78 ± 10.57 vs KiGGS data 80.0 ± 8.1; 7-10 years-old KINDLR-total score: COVID Kids Bavaria MD 73.88 ± 12.03 vs KiGGS data 79.30 ± 9.0). No significant differences were detected with regard to associated factors, namely type of institution, sex of the child, migration background, household size and parental education.ConclusionThese findings suggest a relevant impact of the COVID-19 pandemic on children’s behavior and health-related quality of life one year after the onset of the pandemic. Further analyses in large-scale longitudinal studies are needed to determine the effects of specific pandemic or crisis associated factors contributing to health inequalities.

IntroductionHere we report our results of a multi-center, open cohort study (“COVID-Kids-Bavaria”) investigating the distribution of acute SARS-CoV-2 infections among children and staff in 99 daycare facilities and 48 elementary schools in Bavaria, Germany.Materials and MethodsOverall, 2,568 children (1,337 school children, 1,231 preschool children) and 1,288 adults (466 teachers, 822 daycare staff) consented to participate in the study and were randomly tested in three consecutive phases (September/October 2020, November/December 2020, March 2021). In total, 7,062 throat swabs were analyzed for SARS-CoV-2 by commercial RT-PCR kits.ResultsIn phase I, only one daycare worker tested positive. In phase II, SARS-CoV-2 was detected in three daycare workers, two preschool children, and seven school children. In phase III, no sample tested positive. This corresponds to a positive test rate of 0.05% in phase I, 0.4% in phase II and 0% in phase III. Correlation of a positive PCR test result with the local-7-day incidence values showed a strong association of a 7-day-incidence of more than 100/100,000 as compared to