62 Ingenieurwissenschaften
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- Sentinel-1 (2)
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Hatred directed at members of groups due to their origin, race, gender, religion, or sexual orientation is not new, but it has taken on a new dimension in the online world. To date, very little is known about online hate among adolescents. It is also unknown how online disinhibition might influence the association between being bystanders and being perpetrators of online hate. Thus, the present study focused on examining the associations among being bystanders of online hate, being perpetrators of online hate, and the moderating role of toxic online disinhibition in the relationship between being bystanders and perpetrators of online hate. In total, 1480 students aged between 12 and 17 years old were included in this study. Results revealed positive associations between being online hate bystanders and perpetrators, regardless of whether adolescents had or had not been victims of online hate themselves. The results also showed an association between toxic online disinhibition and online hate perpetration. Further, toxic online disinhibition moderated the relationship between being bystanders of online hate and being perpetrators of online hate. Implications for prevention programs and future research are discussed.
Hatred directed at members of groups due to their origin, race, gender, religion, or sexual orientation is not new, but it has taken on a new dimension in the online world. To date, very little is known about online hate among adolescents. It is also unknown how online disinhibition might influence the association between being bystanders and being perpetrators of online hate. Thus, the present study focused on examining the associations among being bystanders of online hate, being perpetrators of online hate, and the moderating role of toxic online disinhibition in the relationship between being bystanders and perpetrators of online hate. In total, 1480 students aged between 12 and 17 years old were included in this study. Results revealed positive associations between being online hate bystanders and perpetrators, regardless of whether adolescents had or had not been victims of online hate themselves. The results also showed an association between toxic online disinhibition and online hate perpetration. Further, toxic online disinhibition moderated the relationship between being bystanders of online hate and being perpetrators of online hate. Implications for prevention programs and future research are discussed.
Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
(2018)
The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.
Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
(2018)
The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.
Classification of clouds, cirrus, snow, shadows and clear sky areas is a crucial step in the pre-processing of optical remote sensing images and is a valuable input for their atmospheric correction. The Multi-Spectral Imager on board the Sentinel-2's of the Copernicus program offers optimized bands for this task and delivers unprecedented amounts of data regarding spatial sampling, global coverage, spectral coverage, and repetition rate. Efficient algorithms are needed to process, or possibly reprocess, those big amounts of data. Techniques based on top-of-atmosphere reflectance spectra for single-pixels without exploitation of external data or spatial context offer the largest potential for parallel data processing and highly optimized processing throughput. Such algorithms can be seen as a baseline for possible trade-offs in processing performance when the application of more sophisticated methods is discussed. We present several ready-to-use classification algorithms which are all based on a publicly available database of manually classified Sentinel-2A images. These algorithms are based on commonly used and newly developed machine learning techniques which drastically reduce the amount of time needed to update the algorithms when new images are added to the database. Several ready-to-use decision trees are presented which allow to correctly label about 91% of the spectra within a validation dataset. While decision trees are simple to implement and easy to understand, they offer only limited classification skill. It improves to 98% when the presented algorithm based on the classical Bayesian method is applied. This method has only recently been used for this task and shows excellent performance concerning classification skill and processing performance. A comparison of the presented algorithms with other commonly used techniques such as random forests, stochastic gradient descent, or support vector machines is also given. Especially random forests and support vector machines show similar classification skill as the classical Bayesian method.
The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent gravitational field can be observed through the Gravity Recovery and Climate Experiment (GRACE) gravimetric satellite mission, provided that the mass variation is above the sensitivity of GRACE. This study evaluates mass changes in prominent reservoir regions through three independent approaches viz. fluxes, storages, and gravity, by combining remote sensing products, in-situ data and hydrological model outputs using WaterGAP Global Hydrological Model (WGHM) and Global Land Data Assimilation System (GLDAS). The results show that the dynamics revealed by the GRACE signal can be better explored by a hybrid method, which combines remote sensing-based reservoir volume estimates with hydrological model outputs, than by exclusive model-based storage estimates. For the given arid/ semi-arid regions, GLDAS based storage estimations perform better than WGHM.
The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b(899). The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part.
The genesis of chronic pain is explained by a biopsychosocial model. It hypothesizes an interdependency between environmental and genetic factors provoking aberrant long-term changes in biological and psychological regulatory systems. Physiological effects of psychological and physical stressors may play a crucial role in these maladaptive processes. Specifically, long-term demands on the stress response system may moderate central pain processing and influence descending serotonergic and noradrenergic signals from the brainstem, regulating nociceptive processing at the spinal level. However, the underlying mechanisms of this pathophysiological interplay still remain unclear. This paper aims to shed light on possible pathways between physical (exercise) and psychological stress and the potential neurobiological consequences in the genesis and treatment of chronic pain, highlighting evolving concepts and promising research directions in the treatment of chronic pain. Two treatment forms (exercise and mindfulness-based stress reduction as exemplary therapies), their interaction, and the dose-response will be discussed in more detail, which might pave the way to a better understanding of alterations in the pain matrix and help to develop future prevention and therapeutic concepts
The genesis of chronic pain is explained by a biopsychosocial model. It hypothesizes an interdependency between environmental and genetic factors provoking aberrant long-term changes in biological and psychological regulatory systems. Physiological effects of psychological and physical stressors may play a crucial role in these maladaptive processes. Specifically, long-term demands on the stress response system may moderate central pain processing and influence descending serotonergic and noradrenergic signals from the brainstem, regulating nociceptive processing at the spinal level. However, the underlying mechanisms of this pathophysiological interplay still remain unclear. This paper aims to shed light on possible pathways between physical (exercise) and psychological stress and the potential neurobiological consequences in the genesis and treatment of chronic pain, highlighting evolving concepts and promising research directions in the treatment of chronic pain. Two treatment forms (exercise and mindfulness-based stress reduction as exemplary therapies), their interaction, and the dose-response will be discussed in more detail, which might pave the way to a better understanding of alterations in the pain matrix and help to develop future prevention and therapeutic concepts