Ground Samples Research Articles

Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground

Abstract. Hail is a major threat associated with severe thunderstorms, and estimating the hail size is important for issuing warnings to the public. For the validation of existing operational, radar-derived hail estimates, ground-based observations are necessary. Automatic hail sensors, for example within the Swiss Hail Network, record the kinetic energy of hailstones to estimate the hail sizes. Due to the small size of the observational area of these sensors (0.2 m2), the full hail size distribution (HSD) cannot be retrieved. To address this issue, we apply a state-of-the-art custom trained deep learning object detection model to drone-based aerial photogrammetric data to identify hailstones and estimate the HSD. Photogrammetric data of hail on the ground were collected for one supercell thunderstorm crossing central Switzerland from southwest to northeast in the afternoon of 20 June 2021. The hail swath of this intense right-moving supercell was intercepted a few minutes after the passage at a soccer field near Entlebuch (canton of Lucerne, Switzerland) and aerial images were taken by a commercial DJI drone, equipped with a 45-megapixel full-frame camera system. The resulting images have a ground sampling distance (GSD) of 1.5 mm per pixel, defined by the focal length of 35 mm of the camera and a flight altitude of 12 m above the ground. A 2-dimensional orthomosaic model of the survey area (750.4 m2) is created based on 116 captured images during the first drone mapping flight. Hail is then detected using a region-based convolutional neural network (Mask R-CNN). We first characterize the hail sizes based on the individual hail segmentation masks resulting from the model detections and investigate the performance using manual hail annotations by experts to generate validation and test data sets. The final HSD, composed of 18 207 hailstones, is compared with nearby automatic hail sensor observations, the operational weather-radar-based hail product MESHS (Maximum Expected Severe Hail Size) and crowdsourced hail reports. Based on the retrieved data set, a statistical assessment of sampling errors of hail sensors is carried out. Furthermore, five repetitions of the drone-based photogrammetry mission within 18.65 min facilitate investigations into the hail-melting process on the ground.

First Report of Enterobacter mori Causing Bacterial Wilt on Tomato in China.

Tomato (Solanum lycopersicum L.) is one of the most important vegetable crops in China. In October 2023, a new bacterial disease was discovered on tomato plants in a 0.3-acre farm's greenhouse (35.514806N, 118.996106E) in Longshan Town, Shandong Province, China. Over 50% of the tomato plants showed symptoms of stem rot, leaf wilt, or plant death. Three diseased tomato plants were collected for pathogen isolation and purification. Two leaf samples, each about 1 cm2, were cut from the junction area of healthy and diseased parts and disinfected with 75% alcohol for 60 s, followed by 0.1% HgCl2 for 90 s, and then washed three times with sterilized H2O. The samples were subsequently ground with 1.0 mL sterilized H2O. The ground samples were diluted to 10-4, 10-5, and 10-6 and then were plated on a potato dextrose agar (PDA) plate, respectively. White mucous bacterial colonies appeared at 28℃ for 24~48 h, no fungal colony was observed. Six bacterial colonies were randomly selected for gram staining and found to be gram-negative. To further determine their species classification, fragments of the 16SrDNA, hsp60, gyrB, and rpoB genes were separately amplified using previously reported PCR conditions and with primer pairs, including 27F/1492R (Wu et al., 2023), HSP60-F /HSP60-R (Gül et al., 2023), gyrB UP-1 / gyrB UP-2r (Yamamoto et al., 1995) and rpoB CM81-F / rpoB CM32b-R (Brady et al., 2008). Sequence analysis showed that the obtained sequences of the 16SrDNA, hsp60, gyrB, and rpoB genes among these six colonies were identical and 100%, 100%, 99.31%, and 99.36% similar to those of Enterobacter mori accessions OP601841 (with a coverage of 100%), MT199160 (83%), OP676246 (100%), and MN594495 (100%), at nucleotide level, respectively. Sequences of the above four genes of 23LSFQ were submitted to GenBank under the accession numbers PP461247, PP474090, PP136037, and PP136038, respectively. We selected one of these six colonies, 23LSFQ, for further analysis. The phylogenetic tree based on the concatenated sequences of the above four genes using the maximum likelihood method with MEGAX software showed that 23LSFQ is grouped with E. mori LMG25706 (NCBI: txid980518). To determine the pathogenicity of 23LSFQ , we sprayed 23LSFQ (OD600=0.8) onto five 30-day-old healthy plants of the tomato cultivars Alisa Craig, Jinpeng NO.1, and Chaobei, respectively. These seedlings were incubated in a chamber at 28°C with a 16 h light/ 8h dark photoperiod and 60% relative humidity. The leaves of the inoculated plants became curled and wilted at two days post inoculation (dpi) and appeared necrotic at 10 dpi. The symptoms were similar to those observed in field-infected tomato plants. No symptoms were observed on the plants inoculated with water. We further sequenced the re-isolated bacteria from the symptomatic inoculated seedlings. Results showed that they belong to E. mori. The experiment was repeated three times. E. mori has been found to cause diseases on peaches (Ahmad et al., 2021), watermelons (Wu et al., 2023), Canna indica, (Zhang et al., 2023), and strawberries (Ji et al., 2023). E. cloacae has been found to cause diseases on tomatoes in Heilongjiang province (Jin et al., 2023). This is the first report of E. mori causing leaf yellowing and wilting on tomatoes in China. These results are significant for the safe production and disease control of greenhouse tomatoes.

Digitizing Historical Aerial Images: Evaluation of the Effects of Scanning Quality on Aerial Triangulation and Dense Image Matching

In the last decade, many aerial photographic archives have started to be digitized for multiple purposes, including digital preservation and geoprocessing. This paper analyzes the effects of professional photogrammetric versus consumer-grade scanners on the processing of analog historical aerial photographs. An image block over Warsaw is considered, featuring 38 photographs acquired in 1986 (Wild RC10, Normal Aviogon II lens, 23 × 23 cm format) with a ground sampling distance (GSD) of 4 cm. Aerial triangulation (AT) and dense image matching (DIM) procedures are considered, analyzing how scanning modalities are important in the massive digitization of analog images for georeferencing and 3D product generation. The achieved results show how consumer-grade scanners, unlike more expensive photogrammetric scanners, do not possess adequate recording quality to ensure high accuracy and geometric precision for geoprocessing purposes. However, consumer-grade scanners can be used for time and cost-efficient applications where a partial loss of data quality is not critical.

A Drone Based Image Dataset Generation Methodology for Single Image Super Resolution

The advancements in drone technologies, digital imaging, computer vision techniques, and the liberalized laws related to drone flying have opened up drone-based applications such as the delivery of supplies, search and rescue, aerial surveillance, and so on. The drones, especially the nano/micro/small drones, may be mounted with only low-resolution camera(s) due to their maximum takeoff weight limitations. The low-resolution images generated by the cameras, if used for landing, can result in faulty detection unless the photos are taken from a very close distance to the point of interest. Detection and recognition of the point(s) of interest as early as possible is required to ensure sufficient response time for safe maneuvering. Hence, the images are to be captured at greater heights or distances from the point(s) of interest, and obtaining the high-resolution images from the captured low-resolution images is crucial. The High Resolution (HR) and the Low Resolution (LR) image pairs for training super-resolution models in the works presented in literature are generated using two different cameras or the HR images are captured by the camera and LR images are generated by degrading the HR images. As both methods are not appropriate for small/micro/nano category drones, we propose a novel method based on Ground Sampling Distance (GSD) to capture the LR and HR images. In this paper, we have presented the designed methodology for the creation of a dataset using drone-mounted cameras covering a broad spectrum of views of the target(s) suitable for training and testing of the Single Image Super-Resolution (SISR) models. We also present a methodology for selecting an appropriate target for imaging that enables the visual quality assessment of the developed super-resolution model.

Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases

The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis.The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60–70%. Conversely, this reduction in accuracy was relatively contained (10–15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts.The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

Crop type classification and spatial mapping in River Nile and Northern State, Sudan, using Sentinel-2 satellite data and field observation

Maintaining productive farmland necessitates precise crop mapping and identification. While satellite remote sensing makes it possible to generate such maps, there are still issues to resolve, such as how to choose input data and the best classifier algorithm, especially in areas with scarce field data. Accurate assessments of the land used for farming are a crucial part of national food supply and production accounting in many African countries, and to this end, remote sensing tools are being increasingly put to use. The aim of this study was to assess the potentiality of Sentinel-2 to distinguish and discriminate crop species in the study area and constraints on accurately mapping cropping patterns in the winter season in River Nile and Northern State, Sudan. The research utilized Sentinel-2 Normalized Different Vegetation Index (NDVI) at 10 m resolution, unsupervised and supervised classification method with ground sample and accuracy assessment. The results of the study found that the signatures of grain sorghum, wheat, okra, Vicia faba, alfalfa, corn, haricot, onion, potato, tomato, lupine, tree cover, and garlic have clear distinctions, permitting an overall accuracy of 87.38%, with trees cover, onion, wheat, potato, garlic, alfalfa, tomato, lupine and Vicia faba achieving more than 87% accuracy. Major mislabeling problems occurred primarily in irrigated areas for grain sorghum, okra, corn, and haricot, in wooded areas comprised of small parcels of land. The research found that high-resolution temporal images combined with ground data had potential and utility for mapping cropland at the field scale in the winter.

WO3/ZnWO4 microcomposites with potential application in photocatalysis

The paper presents the results of a comprehensive study of the structural, optical and strength features, changes in the morphology of grains of (1-x)WO3 – xZnO ceramics depending on the concentration of the initial components when they vary. Using the X-ray diffraction method, it was found that mechanochemical grinding of samples of (1-x)WO3 – xZnO ceramics, regardless of the concentration of the components, does not lead to the initiation of phase transformation processes associated with the formation of the monoclinic ZnWO4 phase, the formation of which is observed during thermal annealing of samples at a temperature of 1000 °C. Based on data obtained using scanning electron microscopy and X-ray diffraction methods, the dynamics of phase transformations in ceramics of the type WO3/ZnWO4 → ZnWO4 → ZnO/ZnWO4 was established. It was found that with a given ratio of WO3: ZnO components equal to 0.7 : 0.3, under selected conditions of mechanochemical grinding and an annealing temperature of 1000 °C, highly ordered single-phase ZnWO4 ceramics with a wolframite structure can be obtained. Analysis of the optical characteristics and changes in the band gap depending on the ratio of the components showed a direct dependence of changes in the band gap on the phase composition of the ceramics. An assessment of the strength characteristics made it possible to establish that the formation of two-phase WO3/ZnWO4 ceramics leads to strengthening of the surface layer, while for two-phase ZnO/ZnWO4 ceramics a decrease in hardness is observed, the decrease of which directly correlates with changes in the content of the ZnO phase in the composition of the ceramics.

Quantifying Seagrass Density Using Sentinel-2 Data and Machine Learning

Seagrasses, rooted aquatic plants growing completely underwater, are extremely important for the coastal ecosystem. They are an important component of the total carbon burial in the ocean, they provide food, shelter, and nursery to many aquatic organisms in coastal ecosystems, and they improve water quality. Due to human activity, seagrass coverage has been rapidly declining, and there is an urgent need to monitor seagrasses consistently. Seagrass coverage has been closely monitored in the Chesapeake Bay since 1970 using air photos and ground samples. These efforts are costly and time-consuming. Many studies have used remote sensing data to identify seagrass bed outlines, but few have mapped seagrass bed density. This study used Sentinel-2 satellite data and machine learning in Google Earth Engine and the Chesapeake Bay Program field data to map seagrass density. We used seagrass density data from the Chincoteague and Sinepuxent Bay to train machine learning algorithms and evaluate their accuracies. Out of the four machine learning models tested (Naive Bayes (NB), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF)), the RF model outperformed the other three models with overall accuracies of 0.874 and Kappa coefficients of 0.777. The SVM and CART models performed similarly and NB performed the poorest. We tested two different approaches to assess the models’ accuracy. When we used all the available ground samples to train the models, whereby our analysis showed that model performance was associated with seagrass density class, and that higher seagrass density classes had better consumer accuracy, producer accuracy, and F1 scores. However, the association of model performance with seagrass density class disappeared when using the same training data size for each class. Very sparse and dense seagrass classes had replacedhigherbetter accuracies than the sparse and moderate seagrass density classes. This finding suggests that training data impacts machine learning model performance. The uneven training data size for different classes can result in biased assessment results. Selecting proper training data and machine learning models are equally important when using machine learning and remote sensing data to map seagrass density. In summary, this study demonstrates the potential to map seagrass density using satellite data.

In situ extraction and enrichment of multiple amino acids by integrated mechanochemical extraction and aqueous two-phase system

A highly efficient and environment-friendly extraction technology was established to extract and enrich multiple amino acids (AAs) in situ by deep eutectic solvent (DES)-assisted mechanochemical extraction (MCE) and aqueous two-phase system (ATPS). Firstly, the DES was prepared from betaine and ethylene glycol in a 1:2 M ratio. Then, a series of conditions were evaluated by single-factor and multifactor experiments to select the optimum experimental parameters. The highest extraction yields of AAs were obtained by DES-assisted MCE and ATPS enrichment, including that the amount of DES was 0.97 mL, the milling time was 4.88 min, the amount of salt was 8 g, and the solid-to-liquid ratio was 0.055 g mL−1. After extracting, the diluted AA extracts were analyzed by a high-performance liquid chromatography-fluorescence detector after pre-column derivatization with 6-aminoquinolyl-N-hydroxysccinimidyl carbamate. The methodological validation demonstrated the proposed method exhibited satisfactory limits of detection ranged from 0.11 to 0.82 ng mL−1, reliable interday precision (%RSD) of 0.014–0.067 % for retention time and 1.29–3.34 % for the peak area, acceptable reproducibility with %RSD below 4.1 %, and good %recovery between 90.3 % and 122.5 % at three spiked concentrations. The surface morphology of raw, only ground, and DES-ground sample powders was tested by scanning electron microscopy to further explore the changes in the physicochemical properties of samples. By comparing with other AAs extraction methods, the developed method is not only eco-friendly, but greatly improves the yield of free AAs extracted from chrysanthemum.

Estimating Total Length of Partially Submerged Crocodylians from Drone Imagery

Understanding the demographic structure is vital for wildlife research and conservation. For crocodylians, accurately estimating total length and demographic class usually necessitates close observation or capture, often of partially immersed individuals, leading to potential imprecision and risk. Drone technology offers a bias-free, safer alternative for classification. We evaluated the effectiveness of drone photos combined with head length allometric relationships to estimate total length, and propose a standardized method for drone-based crocodylian demographic classification. We evaluated error sources related to drone flight parameters using standardized targets. An allometric framework correlating head to total length for 17 crocodylian species was developed, incorporating confidence intervals to account for imprecision sources (e.g., allometric accuracy, head inclination, observer bias, terrain variability). This method was applied to wild crocodylians through drone photography. Target measurements from drone imagery, across various resolutions and sizes, were consistent with their actual dimensions. Terrain effects were less impactful than Ground-Sample Distance (GSD) errors from photogrammetric software. The allometric framework predicted lengths within ≃11–18% accuracy across species, with natural allometric variation among individuals explaining much of this range. Compared to traditional methods that can be subjective and risky, our drone-based approach is objective, efficient, fast, cheap, non-invasive, and safe. Nonetheless, further refinements are needed to extend survey times and better include smaller size classes.

Cellular mechanotransduction of human osteoblasts in microgravity

Astronauts experience significant and rapid bone loss as a result of an extended stay in space, making the International Space Station (ISS) the perfect laboratory for studying osteoporosis due to the accelerated nature of bone loss on the ISS. This prompts the question, how does the lack of load due to zero-gravity propagate to bone-forming cells, human fetal osteoblasts (hFOBs), altering their maturation to mineralization? Here, we aim to study the mechanotransduction mechanisms by which bone loss occurs in microgravity. Two automated experiments, microfluidic chips capable of measuring single-cell mechanics via aspiration and cell spheroids incubated in pressure-controlled chambers, were each integrated into a CubeLab deployed to the ISS National Laboratory. For the first experiment, we report protrusion measurements of aspirated cells after exposure to microgravity at the ISS and compare these results to ground control conducted inside the CubeLab. We found slightly elongated protrusions for space samples compared to ground samples indicating softening of hFOB cells in microgravity. In the second experiment, we encapsulated osteoblast spheroids in collagen gel and incubated the samples in pressure-controlled chambers. We found that microgravity significantly reduced filamentous actin levels in the hFOB spheroids. When subjected to pressure, the spheroids exhibited increased pSMAD1/5/9 expression, regardless of the microgravity condition. Moreover, microgravity reduced YAP expression, while pressure increased YAP levels, thus restoring YAP expression for spheroids in microgravity. Our study provides insights into the influence of microgravity on the mechanical properties of bone cells and the impact of compressive pressure on cell signaling in space.

Analysing the Relationship between Spatial Resolution, Sharpness and Signal-to-Noise Ratio of Very High Resolution Satellite Imagery Using an Automatic Edge Method

Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR.

Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula b~σslope estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC b~.

Nondestructive Quantification of Isoflavones in Cotyledons by Near-Infrared Spectroscopy and Potential and Limits for Sustainable Soybean Breeding

The isoflavones in the cotyledon of soybean seeds mimic human estrogen in structure, conferring them complex effects on health. Their regulation represents a major challenge for the sustainable breeding of new varieties with lower levels of potential endocrine disruptors. To develop a rapid, nondestructive, and eco-friendly analysis method, this study explores how sample grinding affects the results of near-infrared spectroscopy (NIRS) and the preprocessing methods. The prediction of the daidzein and genistein content would help the specific reduction in isoflavones in the cotyledon without harming seed development. The potential of a nonlinear approach (artificial neural network) is also compared with the more conventional partial least squares (PLS) regression. The isoflavone content of cotyledons from 529 soybean samples (65 genotypes) was quantified by HPLC, and the NIR spectra of these samples were collected using a Brucker multi-purpose analyzer. The spectra of whole and ground cotyledons were also collected for 155 samples. The results show that grain fragmentation improves the model calibration, although spectral preprocessing can harmonize this effect. Although the best PLS regression in cross-validation did not suffice to quantify the daidzein and genistein percentages, the artificial neural network (ANN) approach allowed us to develop much more reliable models than PLS. The performance of ANNs in external validation is remarkable in terms of both precision and applicability (R2 = 0.89 and a ratio of prediction to deviation of 2.92), making ANNs suitable in the breeding context for screening soybean grains regarding their isoflavone content.

Crystallographic and Topographic Analysis of Ultra-Translucent Zirconia After Various Surface Treatments

The aim of the present study was to analyze the effects of 4 different surface treatments, on the crystallographic characteristics of Ultra-Translucent Zirconia. Fully sintered zirconia specimens of highly translucent yttria partially stabilized zirconia (Y-PSZ) (KATANA UTML) were divided into four experimental groups and a control group (n=10). Each group received one of the following surface treatments: sandblasting with 50µm alumina particles (Al₂O₃), sandblasting with 110µm alumina particles, and grinding with a rotary high-speed turbine with and without water irrigation. For each sample, x-ray diffraction was carried out to analyze peak intensity, calculate the crystallite size, and detect the presence of compressive and tensile stress. Surface roughness was measured on all specimens using a standard scanning profilometer. Additionally, scanning electron microscopy (SEM) was performed to qualitatively analyze the surfaces of the specimens. Statistical analysis included repeated measures analysis of variance and post hoc Tukey test (p≤0.05). The control group exhibited the highest crystallite size (323nm). All surface treatments led to a reduction in the crystallite size, with the most significant reduction observed in the groups subjected to sandblasting with 110µm alumina particles and high-speed grinding with irrigation. Sandblasting with 50µm alumina particles resulted in less transformation of the crystallite size. A general tendency of the diffraction peaks to shift to a lower angle can be observed in the experimental groups, indicating the presence of compressive stress on the samples. Profilometry revealed higher roughness in the ground samples (6,14µm and 6,57µm) compared to the sandblasted groups (2,93µm and 2,02µm). The crystal domain size showed a tendency to decrease after the surface treatments. Sandblasted samples, as well as ground samples without irrigation, exhibited compressive stress. Sandblasted samples had lower surface roughness compared to the ground samples. Sandblasting with 50µm alumina particles caused the least decrease in crystallite size.

Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target

Unmanned aerial vehicle (UAV) photogrammetry is an emerging means of acquiring high-precision rapid spatial information and data because it is cost-effective and highly efficient. However, securing uniform quality in the results of UAV photogrammetry is difficult due to the use of low-cost navigation devices, non-surveying cameras, and rapid changes in shooting locations depending on the aircraft’s behavior. In addition, no specific procedures or guidelines exist for performing quantitative quality tests or certification methods on UAV images. Additionally, test tools for UAV image quality assessment only use the ground sample distance (GSD), often resulting in a reduced image quality compared with that of manned aircraft images. In this study, we performed a modulation transfer function (MTF) analysis using a slanted edge target and a GSD analysis to confirm the necessity of MTF analysis in UAV image quality assessments. In this study, we aimed to address this issue by conducting a modulation transfer function (MTF) analysis using a slanted edge target and a ground sample distance (GSD) analysis. This was carried out to confirm the necessity of MTF analysis in evaluating UAV image quality. Furthermore, we analyzed the impact of flight height and mounted sensors on image quality at different study sites.

Systemic design of the very-high-resolution imaging payload of an optical remote sensing satellite for launch into the VLEO using an small launch vehicle

The very low Earth orbit (VLEO), which includes orbits with altitudes of 100–450 km, has distinct advantages for remote-sensing spacecraft. Lower altitudes allow payloads with smaller dimensions, weight, and power to achieve performances similar to or better than larger payloads in higher orbits in remote sensing and Earth observation missions. The most important advantage of low orbits for optical imaging systems is the higher resolution these payloads can attain. The present paper systemically designs the very high-resolution imaging payload of an optical remote sensing satellite for operations in the VLEO. The aim of this design is to achieve a high spatial resolution of the optical imaging platform and reach a 1-m ground sampling distance (GSD) as reported. Therefore, considering an orbital altitude of 300 km and system calculations, the implementation of this method is described. The results obtained show that the proposed platform (VHR4) has better diffraction-limited ground resolution compared to several examples of operational satellite platforms under approximately equal orbital conditions. Also, by calculating and estimating the weight, power, and dimensions of the proposed imaging platform (VHR4), it is shown that the platform meets the requirements of a small satellite launcher in terms of weight, dimensions, and power.

Water Quality Estimation Using Gaofen-2 Images Based on UAV Multispectral Data Modeling in Qinba Rugged Terrain Area

This study presents an innovative method for large-scale surface water quality assessment in rugged terrain areas, specifically tailored for regions like the Qinba Mountains. The approach combines the use of high-resolution (10 cm) multispectral data acquired by unmanned aerial vehicles (UAVs) with synchronized ground sampling and 1 m resolution multispectral imagery from China’s Gaofen-2 satellite. By integrating these technologies, the study aims to capitalize on the convenience and synchronized observation capabilities of UAV remote sensing, while leveraging the broad coverage of satellite remote sensing to overcome the limitations of each individual technique. Initially, a multispectral estimation model is developed for key water quality parameters, including chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), utilizing data from UAVs and coordinated ground samples. Subsequently, a comparison is made between the spectral band ratios (R/G and NIR/G) obtained from the UAV data and those from the Gaofen-2 satellite data, revealing a substantial similarity. Ultimately, this integrated methodology is successfully employed in monitoring water quality across a vast area, particularly along the midstream of the Hanjiang River in the Qinba Mountain region. The results underscore the feasibility, advantages, improved efficiency, and enhanced accuracy of this approach, making it particularly suitable for large-scale water quality monitoring in mountainous terrain. Furthermore, this method reduces the burden associated with traditional ground-based spectral acquisitions, paving the way for a more practical and cost-effective solution in monitoring vast water bodies.

Application of near-infrared spectroscopy for hay evaluation at different degrees of sample preparation.

A study was conducted to quantify the performance differences of the nearinfrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. The performance of the developed NIRS calibration models was evaluated based on R2, standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R2 (coefficient of determination), standard error, and RPD. Although the R2 of calibration models was mainly greater than 0.90 across the feed value indexes, the R2 of cross-validations was much lower. The R2 of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

A Method for Reconstruction of Size Distributions from 3D Drone Image Analysis: A Case Study

AbstractThis paper describes a novel procedure to assess fragmentation from automatic analysis of 3D photogrammetric models with a commercial software. The muckpiles from 12 blasts were photographed with a conventional drone to build 3D photogrammetric models; the flights were made with a relatively constant ground sampling distance (GSD) of 6.2 sd 0.92 mm (mean and standard deviation, respectively). A comparison with already published mass-based size distributions from 11 of these blasts, shows a good performance of automatic 3D-fragmentation measurements in the coarse range (P ≥ 60%), while deviations between mass-based and 3D model fragmentation analysis grow towards the central-fines range. As a solution, the Swebrec function is fitted to the reliable part of the size distributions, well above the GSD, and then is extended towards the fines, down to a percentage passing of 5–10%. The suitable fitting range is obtained iteratively from the mass-based fragmentation data; the lower fragment size considered is independent of the model’s resolution (i.e. GSD) with mean of 357 mm (equivalent to a passing in the range 66–86%, and well above the GSD of our models). The resulting distributions match properly mass-based size distributions with relative errors in percentile sizes of 15.5 sd 3.4%, and they can be represented with the simplest form of the fragmentation-energy-fan. As a guideline for reconstruction of size distributions and fines assessment when mass-based data is not available, the lower-fitting limit of 357 mm yields reasonable results (mean errors in pass in the range 5–36%) for the present case. The errors are limited enough to keep a sound description of the variation of fragmentation with change in blast design.