Advancing the remote sensing of desert dust

The irregular shape of mineral dust provides a strong signature on active and passive polarimetric remote sensing observations. Nowadays, advanced lidar systems operating in the framework of ACTRIS are capable of providing quality assured, calibrated multi-wavelength linear particle depolarization ratio measurements, while new developments will provide us more polarimetric measurements in the near future. Passive polarimeters are already part of ACTRIS and their integration in operational algorithms is expected in the near future. This wealth of new information combined with updated scattering databases and sophisticated inversion schemes provide the means towards an improved characterization of desert dust in the future. We present here some examples from the ACTRIS journey on dust research during the last decade, aiming to demonstrate the progress on issues such as: (a) the discrimination of desert dust in external mixtures, (b) the separation and estimation of the fine and coarse particle modes, (c) the synergy of passive and active remote sensing for the derivation of dust concentration profiles, (d) the provision of dust-related CCN and IN particle concentrations for aerosol-cloud interaction studies, (e) the development of new scattering databases based on realistic particle shapes, (e) the application of these techniques on spaceborne lidar retrievals for the provision of global and regional climatological datasets. Future plans within ACTRIS for the evaluation and advancement of the methodologies and retrievals are also discussed, combined with new developments within the framework of the D-TECT ERC Grant

Classification of Sentinel-2 Images Utilizing Abundance Representation

This paper deals with (both supervised and unsupervised) classification of multispectral Sentinel-2 images, utilizing the abundance representation of the pixels of interest. The latter pixel representation uncovers the hidden structured regions that are not often available in the reference maps. Additionally, it encourages class distinctions and bolsters accuracy. The adopted methodology, which has been successfully applied to hyperpsectral data, involves two main stages: (I) the determination of the pixel’s abundance representation; and (II) the employment of a classification algorithm applied to the abundance representations. More specifically, stage (I) incorporates two key processes, namely (a) endmember extraction, utilizing spectrally homogeneous regions of interest (ROIs); and (b) spectral unmixing, which hinges upon the endmember selection. The adopted spectral unmixing process assumes the linear mixing model (LMM), where each pixel is expressed as a linear combination of the endmembers. The pixel’s abundance vector is estimated via a variational Bayes algorithm that is based on a suitably defined hierarchical Bayesian model. The resulting abundance vectors are then fed to stage (II), where two off-the-shelf supervised classification approaches (namely nearest neighbor (NN) classification and support vector machines (SVM)), as well as an unsupervised classification process (namely the online adaptive possibilistic c-means (OAPCM) clustering algorithm), are adopted. Experiments are performed on a Sentinel-2 image acquired for a specific region of the Northern Pindos National Park in north-western Greece containing water, vegetation and bare soil areas. The experimental results demonstrate that the ad-hoc classification approaches utilizing abundance representations of the pixels outperform those utilizing the spectral signatures of the pixels in terms of accuracy

Novel Measurements of Desert Dust Electrical Properties: A Multi-Instrument Approach during the ASKOS 2022 Campaign

Synergetic measurements of the vertical atmospheric field and the total charge density in the presence of dust events are presented through the launches of balloon-borne instrumentation, including a MiniMill electrometer and a space charge sensor, under dust events during the AEOLUS Cal/Val campaign of ASKOS in Cabo Verde, in June/September 2022. The electric field profiling measurements obtained by different instrumentations are compared, and the near-ground observations are evaluated with a reference ground-based fieldmill electrometer. Moreover, their performance is assessed by utilizing measurements of the co-located Polly XT lidar and its extracted products above the launching site

Assessing the impact of risk-taking behavior on road crash involvement among University students residing in two Mediterranean countries

Surveillance systems are indispensable for injury prevention; yet, detailed electronic records are rarely available. The “Student’s Health Card” is a self-reporting electronic tool addressing health issues of University students, while aiming to actively involve them in preventive practices and health promotion. Utilizing data from the injury prevention related section, this study sought to investigate the impact of risk-taking behavior on road crash involvement among University students residing in two Mediterranean countries. A total of 978 University students, 451 Greek and 527 Italian, provided information on prior road crash involvement, as well as on eight behavioral variables, comprising a risky behavior score. Multiple logistic regression analysis was performed. The already known tendency for clustering of risky behaviors was evident. One degree increment in the risky behavior score was found to increase the risk of road crash involvement by 35%. Driving after drinking (OR = 2.55, CI = 1.53–4.26), riding with a drunk driver (OR = 2.19, CI = 1.08–4.45) and tobacco smoking (OR = 1.95, CI = 1.18–3.22) significantly multiplied the risk. Despite their better compliance with safety measures, Italian students, compared with Greek, reported worse alcohol-related driving habits and engaged more frequently in mobile phone use while driving. Clustering of risky behaviors was found to be an important predictor of road crash involvement. Screening and awareness of risk-taking propensity of University students could guide early intervention. The “Student’s Health Card” could provide, at minimal cost, reliable risk-taking and road crash involvement information, which is needed for both personal risk assessment and surveillance purposes

Assessing the impact of risk-taking behavior on road crash involvement among University students residing in two Mediterranean countries

Surveillance systems are indispensable for injury prevention; yet, detailed electronic records are rarely available. The “Student’s Health Card” is a self-reporting electronic tool addressing health issues of University students, while aiming to actively involve them in preventive practices and health promotion. Utilizing data from the injury prevention related section, this study sought to investigate the impact of risk-taking behavior on road crash involvement among University students residing in two Mediterranean countries. A total of 978 University students, 451 Greek and 527 Italian, provided information on prior road crash involvement, as well as on eight behavioral variables, comprising a risky behavior score. Multiple logistic regression analysis was performed. The already known tendency for clustering of risky behaviors was evident. One degree increment in the risky behavior score was found to increase the risk of road crash involvement by 35%. Driving after drinking (OR = 2.55, CI = 1.53-4.26), riding with a drunk driver (OR = 2.19, CI = 1.08-4.45) and tobacco smoking (OR = 1.95, CI = 1.18-3.22) significantly multiplied the risk. Despite their better compliance with safety measures, Italian students, compared with Greek, reported worse alcohol-related driving habits and engaged more frequently in mobile phone use while driving. Clustering of risky behaviors was found to be an important predictor of road crash involvement. Screening and awareness of risk-taking propensity of University students could guide early intervention. The “Student’s Health Card” could provide, at minimal cost, reliable risk-taking and road crash involvement information, which is needed for both personal risk assessment and surveillance purposes. (C) 2011 Elsevier Ltd. All rights reserved