Methane Mapping with Future Satellite Imaging Spectrometers

This study evaluates a new generation of satellite imaging spectrometers to measure point source methane emissions from anthropogenic sources. We used the Airborne Visible and Infrared Imaging Spectrometer Next Generation(AVIRIS-NG) images with known methane plumes to create two simulated satellite products. One simulation had a 30 m spatial resolution with similar to 200 Signal-to-Noise Ratio (SNR) in the Shortwave Infrared (SWIR) and the other had a 60 m spatial resolution with similar to 400 SNR in the SWIR; both products had a 7.5 nm spectral spacing. We applied a linear matched filter with a sparsity prior and an albedo correction to detect and quantify the methane emission in the original AVIRIS-NG images and in both satellite simulations. We also calculated an emission flux for all images. We found that all methane plumes were detectable in all satellite simulations. The flux calculations for the simulated satellite images correlated well with the calculated flux for the original AVIRIS-NG images. We also found that coarsening spatial resolution had the largest impact on the sensitivity of the results. These results suggest that methane detection and quantification of point sources will be possible with the next generation of satellite imaging spectrometers.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Biasing hydrogen bond donating host systems towards chemical warfare agent recognition

A series of neutral ditopic and negatively charged, monotopic host molecules have been evaluated for their ability to bind chloride and dihydrogen phosphate anions, and neutral organophosphorus species dimethyl methylphosphonate (DMMP), pinacolyl methylphosphonate (PMP) and the chemical warfare agent (CWA) pinacolyl methylphosphonofluoridate (GD, soman) in organic solvent via hydrogen bonding. Urea, thiourea and boronic acid groups are shown to bind anions and neutral guests through the formation of hydrogen bonds, with the urea and thiourea groups typically exhibiting higher affinity interactions. The introduction of a negative charge on the host structure is shown to decrease anion affinity, whilst still allowing for high stability host-GD complex formation. Importantly, the affinity of the host for the neutral CWA GD is greater than for anionic guests, thus demonstrating the potential for selectivity reversal based on charge repulsion

Genome-Wide Association Study Using Extreme Truncate Selection Identifies Novel Genes Affecting Bone Mineral Density and Fracture Risk

Osteoporotic fracture is a major cause of morbidity and mortality worldwide. Low bone mineral density (BMD) is a major predisposing factor to fracture and is known to be highly heritable. Site-, gender-, and age-specific genetic effects on BMD are thought to be significant, but have largely not been considered in the design of genome-wide association studies (GWAS) of BMD to date. We report here a GWAS using a novel study design focusing on women of a specific age (postmenopausal women, age 55–85 years), with either extreme high or low hip BMD (age- and gender-adjusted BMD z-scores of +1.5 to +4.0, n = 1055, or −4.0 to −1.5, n = 900), with replication in cohorts of women drawn from the general population (n = 20,898). The study replicates 21 of 26 known BMD–associated genes. Additionally, we report suggestive association of a further six new genetic associations in or around the genes CLCN7, GALNT3, IBSP, LTBP3, RSPO3, and SOX4, with replication in two independent datasets. A novel mouse model with a loss-of-function mutation in GALNT3 is also reported, which has high bone mass, supporting the involvement of this gene in BMD determination. In addition to identifying further genes associated with BMD, this study confirms the efficiency of extreme-truncate selection designs for quantitative trait association studies

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

WNT16 Influences Bone Mineral Density, Cortical Bone Thickness, Bone Strength, and Osteoporotic Fracture Risk

Peer reviewe

Tradeoffs between UAS Spatial Resolution and Accuracy for Deep Learning Semantic Segmentation Applied to Wetland Vegetation Species Mapping

Recent advances in image classification of fine spatial resolution imagery from unoccupied aircraft systems (UASs) have allowed for mapping vegetation based on both multispectral reflectance and fine textural details. Convolutional neural network (CNN)-based models can take advantage of the spatial detail present in UAS imagery by implicitly learning shapes and textures associated with classes to produce highly accurate maps. However, the spatial resolution of UAS data is infrequently examined in CNN classification, and there are important tradeoffs between spatial resolution and classification accuracy. To improve the understanding of the relationship between spatial resolution and classification accuracy for a CNN-based model, we captured 7.6 cm imagery with a UAS in a wetland environment containing graminoid (grass-like) plant species and simulated a range of spatial resolutions up to 76.0 cm. We evaluated two methods for the simulation of coarser spatial resolution imagery, averaging before and after orthomosaic stitching, and then trained and applied a U-Net CNN model for each resolution and method. We found untuned overall accuracies exceeding 70% at the finest spatial resolutions, but classification accuracy decreased as spatial resolution coarsened, particularly beyond a 22.8 cm resolution. Coarsening the spatial resolution from 7.6 cm to 22.8 cm could permit a ninefold increase in survey area, with only a moderate reduction in classification accuracy. This study provides insight into the impact of the spatial resolution on deep learning semantic segmentation performance and information that can potentially be useful for optimizing precise UAS-based mapping projects

Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions