With the development of satellite imaging technology, many Earth observation satellites have been launched and almost all areas of the Earth are daily covered by satellite images. It is promising to ...utilize such images in change detection, monitoring, semantic classification, etc. To obtain geometric information on each pixel in images, Rational Polynomial Coefficients (RPCs) of a Rational Functional Model (RFM) are provided with satellite images. However initial RPCs may include geometric errors. Therefore, errors in initial RPCs must be corrected before utilization. In this study, we attempt to perform rigorous block adjustments for refining RFMs of overlapping satellite images without ground control points. Our rigorous block adjustment method operated based on automatically extracted tie-points within overlapping areas. It estimates the optimal adjustment coefficients of RPCs and ground coordinates of tie-points. We achieved relative geometric correction of multiple satellite images by transforming the images based on the estimated adjustment coefficients. As a result, an initial RFM model with an error of around 21.73 pixels was corrected to within 1 pixel, and the reprojection error of check points decreased to 0.87 pixels. We also confirmed that our method showed more accurate results than general image registration methods, such as 2D homography transformation.
A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) ...crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology—that is, the deep learning with a CNN method established in this study—demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.
Since UAV images are taken at a low altitude, compared to satellite or aerial images, they usually have narrow ground coverage. In order to use them over a large area of interest, it is essential to ...mosaic them into a large image. In addition, UAV images may have different brightness values at the same ground locations according to different weather conditions and relative position between sun and sensor at the time of photographing. To mosaic a large number of UAV images, it is essential to calibrate different brightness values through a relative radiometric calibration method. In this paper, we propose a relative radiometric calibration method of UAV images capable of reducing over-calibration and minimizing error transfers during image mosaicking. We applied a gain compensation technique to minimize the effect of exposure difference when generating mosaic image. We also applied an image blending technique to remove seamlines among adjacent images to merge. As a result, smooth mosaic images were generated without any noticeable brightness difference around seamlines. For quantitative validation, differences in brightness values in overlapping areas were calculated. The differences have been decreased after applying the proposed method. The results indicated that the proposed method could generate a natural mosaic image by correcting differences in brightness values and removing seamlines of UAV images. Our method has an advantage of being able to calibrate differences in brightness values only with DN values.
In this convergent parallel mixed-methods study, we investigated the early impact of the COVID-19 stay-at-home mandate in Illinois on 16 caregivers of children with autism. Our goal was to understand ...contributors to caregivers’ stress by integrating qualitative and quantitative data. Through a joint display, we explored the intricate relationship between caregivers’ perceptions of their child’s needs, the loss of essential services, and stress levels. The caregivers’ reported needs, wishes, barriers, and coping strategies informed and corroborated final quantitative results on stress levels. Significant associations were found between stress levels and caregivers’ agreement with statements on child supervision, service loss, and perceived level of their child’s independence. These findings underscore the importance for robust support systems that enhance family resilience and validate prior research during exceptional circumstances. They offer insights for policymakers and service providers seeking to improve the well-being of families raising children with autism, particularly in times of crisis.
Recently, with increasing use of unmanned aerial vehicle (UAV), radiometric calibration of UAV images has become an important pre-processing step for application such as vegetation mapping, crop ...field monitoring, etc. In order to obtain accurate spectral reflectance, some UAVs measure irradiance at the time of image acquisition. However, most of UAV systems do not have such irradiance sensors. In these cases, vicarious radiometric correction method has to be used. Digital numbers (DNs) of imaged ground reflectance targets are measured and spectral reflectance is acquired from with known reflectance values of the targets. For automated vicarious calibration, a technique for automatically detecting image location of ground reflectance targets has been developed. In this study, we report an improved version of automated reflectance target detection and a new semi-automatic reflectance target detection developed. Test results showed that among the 14 reflectance targets, 13 targets were detected with the automatic target detection method. The undetected target was extracted by the proposed semi-automatic target detect method. Additional test was conducted on the remaining targets to confirm the applicability of our semi-automatic target detection method. As a result, other targets were also detected. The proposed automated and semi-automated target detection method can be used for automated vicarious calibration of UAV images.
Multiple stressors are an increasing concern in the management and conservation of ecosystems, and have been identified as a key gap in research. Coral reefs are one example of an ecosystem where ...management of local stressors may be a way of mitigating or delaying the effects of climate change. Predicting how multiple stressors interact, particularly in a spatially explicit fashion, is a difficult challenge. Here we use a combination of an expert-elicited Bayesian network (BN) and spatial environmental data to examine how hypothetical scenarios of climate change and local management would result in different outcomes for coral reefs on the Great Barrier Reef (GBR), Australia. Parameterizing our BN using the mean responses from our experts resulted in predictions of limited efficacy of local management in combating the effects of climate change. However, there was considerable variability in expert responses and uncertainty was high. Many reefs within the central GBR appear to be at risk of further decline based on the pessimistic opinions of our expert pool. Further parameterization of the model as more data and knowledge become available could improve predictive power. Our approach serves as a starting point for subsequent work that can fine-tune parameters and explore uncertainties in predictions of responses to management.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor ...biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.
Zoosporic fungi play an important role in aquatic environments, but their diversity, especially that of parasitic fungi of phytoplankton, has still not been fully revealed. We conducted monthly ...analyses of the community structure of zoosporic fungi at a pelagic site in Lake Biwa, Japan, from May to December 2016. Metabarcoding analysis, targeted to a large subunit region of ribosomal DNA in the nano-size fraction of particles (2-20 µm), was carried out on the samples. We also counted large phytoplankton and chytrid sporangia attached to the hosts. We detected 3 zoosporic fungal phyla (Blastocladiomycota, Chytridiomycota and Cryptomycota) within 107 operational taxonomic units (OTUs), in which Chytridiomycota was the most diverse and abundant phylum. Few fungal OTUs overlapped between months, and specific communities were detected in each month. These results showed that diverse zoosporic fungi with high temporal variability inhabited the lake. Five large phytoplankton species were found to be infected by chytrids:
Staurastrum dorsidentiferum
,
S. rotula
,
Closterium aciculare
,
Asterionella formosa
and
Aulacoseira granulata
. Some chytrids were detected by metabarcoding analysis:
Zygophlyctis asterionellae
infecting
A. formosa
,
Staurastromyces oculus
infecting
S. dorsidentiferum
and
Pendulichytrium sphaericum
infecting
A. granulata
. One OTU detected in association with infected
C. aciculare
by microscopic counting might have been an obligate parasitic chytrid of
C. aciculare
. The results indicated that a combination of metabarcoding and microscopic analysis revealed more information on zoosporic fungi, including those that are parasitic.
The Aspergillus series Nigri contains biotechnologically and medically important species. They can produce hazardous mycotoxins, which is relevant due to the frequent occurrence of these species on ...foodstuffs and in the indoor environment. The taxonomy of the series has undergone numerous rearrangements, and currently, there are 14 species accepted in the series, most of which are considered cryptic. Species-level identifications are, however, problematic or impossible for many isolates even when using DNA sequencing or MALDI-TOF mass spectrometry, indicating a possible problem in the definition of species limits or the presence of undescribed species diversity. To re-examine the species boundaries, we collected DNA sequences from three phylogenetic markers ( benA , CaM and RPB2 ) for 276 strains from series Nigri and generated 18 new whole-genome sequences. With the three-gene dataset, we employed phylogenetic methods based on the multispecies coalescence model, including four single-locus methods (GMYC, bGMYC, PTP and bPTP) and one multilocus method (STACEY). From a total of 15 methods and their various settings, 11 supported the recognition of only three species corresponding to the three main phylogenetic lineages: A. niger , A. tubingensis and A. brasiliensis . Similarly, recognition of these three species was supported by the GCPSR approach (Genealogical Concordance Phylogenetic Species Recognition) and analysis in DELINEATE software. We also showed that the phylogeny based on benA , CaM and RPB2 is suboptimal and displays significant differences from a phylogeny constructed using 5 752 single-copy orthologous proteins; therefore, the results of the delimitation methods may be subject to a higher than usual level of uncertainty. To overcome this, we randomly selected 200 genes from these genomes and performed ten independent STACEY analyses, each with 20 genes. All analyses supported the recognition of only one species in the A. niger and A. brasiliensis lineages, while one to four species were inconsistently delimited in the A. tubingensis lineage. After considering all of these results and their practical implications, we propose that the revised series Nigri includes six species: A. brasiliensis , A. eucalypticola , A. luchuensis (syn. A. piperis ), A. niger (syn. A. vinaceus and A. welwitschiae ), A. tubingensis (syn. A. chiangmaiensis , A. costaricensis , A. neoniger and A. pseudopiperis ) and A. vadensis . We also showed that the intraspecific genetic variability in the redefined A. niger and A. tubingensis does not deviate from that commonly found in other aspergilli. We supplemented the study with a list of accepted species, synonyms and unresolved names, some of which may threaten the stability of the current taxonomy.
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