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Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies. [ABSTRACT FROM AUTHOR]

One of the greatest challenges of our time is monitoring the rapid environmental changes taking place worldwide at both local and global scales. This requires easy-to-use and ready-to-implement tools and services to monitor and quantify aspects of bio- and geodiversity change and the impact of land use intensification using freely available and global remotely sensed data, and to derive remotely sensed indicators. Currently, there are no services for quantifying both raster- and vector-based indicators in a "compact tool". Therefore, the main innovation of ESIS/Imalys is having a remote sensing (RS) tool that allows for RS data processing, data management, and continuous and discrete quantification and derivation of RS indicators in one tool. With the ESIS/Imalys project (Ecosystem Integrity Remote Sensing—Modelling and Service Tool), we try to present environmental indicators on a clearly defined and reproducible basis. The Imalys software library generates the RS indicators and remote sensing products defined for ESIS. This paper provides an overview of the functionality of the Imalys software library. An overview of the technical background of the implementation of the Imalys library, data formats and the user interfaces is given. Examples of RS-based indicators derived using the Imalys tool at pixel level and at zone level (vector level) are presented. Furthermore, the advantages and disadvantages of the Imalys tool are discussed in detail in order to better assess the value of Imalys for users and developers. The applicability of the indicators will be demonstrated through three ecological applications, namely: (1) monitoring landscape diversity, (2) monitoring landscape structure and landscape fragmentation, and (3) monitoring land use intensity and its impact on ecosystem functions. Despite the integration of large amounts of data, Imalys can run on any PC, as the processing and derivation of indicators has been greatly optimised. The Imalys source code is freely available and is hosted and maintained under an open source license. Complete documentation of all methods, functions and derived indicators can be found in the freely available Imalys manual. The user-friendliness of Imalys, despite the integration of a large amount of RS data, makes it another important tool for ecological research, modelling and application for the monitoring and derivation of ecosystem indicators from local to global scale. [ABSTRACT FROM AUTHOR]

Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows. [ABSTRACT FROM AUTHOR]

The sustainable provision of ecological products and services, both natural and man-made, faces a substantial threat emanating from invasive plant species (IPS), which inflict considerable economic and ecological harm on a global scale. They are widely recognized as one of the primary drivers of global biodiversity decline and have become the focal point of an increasing number of studies. The integration of remote sensing (RS) and geographic information systems (GIS) plays a pivotal role in their detection and classification across a diverse range of research endeavors, emphasizing the critical significance of accounting for the phenological stages of the targeted species when endeavoring to accurately delineate their distribution and occurrences. This study is centered on this fundamental premise, as it endeavors to amass terrestrial data encompassing the phenological stages and spectral attributes of the specified IPS, with the overarching objective of ascertaining the most opportune time frames for their detection. Moreover, it involves the development and validation of a detection and classification algorithm, harnessing a diverse array of RS datasets, including satellite and unmanned aerial vehicle (UAV) imagery spanning the spectrum from RGB to multispectral and near-infrared (NIR). Taken together, our investigation underscores the advantages of employing an array of RS datasets in conjunction with the phenological stages, offering an economically efficient and adaptable solution for the detection and monitoring of invasive plant species. Such insights hold the potential to inform both present and future policymaking pertaining to the management of invasive species in agricultural and natural ecosystems. [ABSTRACT FROM AUTHOR]

Simple Summary: The environmental issue posed by the distribution of plastic is largely due to their fragmentation into micro- and nanoplastics (MNPs), which are spread across aquatic, atmospheric, and terrestrial ecosystems. The discovery that these particles can easily accumulate in aquatic organisms and edible plants has rapidly drawn global attention to their potential impact on human health. Therefore, based on the One Health approach, this paper will first provide an overview of soil contamination as well as human exposure routes and toxicity pathways of MNPs. It then describes the significant role of livestock animals as a critical link between soil and human exposure, focusing on the lack of the available literature and on the role of livestock animals as reservoirs and carriers of MNP contamination along the food chain. The lack of a standardized method to detect, quantify, and characterize plastic particles in different matrices prevents the determination of their realistic load. For this reason, the development of a database where researchers can document data, both on MNP characteristics and assessment procedures, is also suggested with the perspective of training, in the future, artificial intelligence (AI) tools capable of predicting the most abundant/dangerous polymer(s) and help in guiding decisions to reduce risks to global health. Plastic pollution is a global diffuse threat, especially considering its fragmentation into microplastics (MPs) and nanoplastics (NPs). Since the contamination of the aquatic environment is already well studied, most studies have now focused on the soil. Moreover, the number of studies on the exposure routes and toxic effects of MNPs in humans is continuously increasing. Although MNPs can cause inflammation, cytotoxicity, genotoxicity and immune toxicity in livestock animals, which can accumulate ingested/inhaled plastic particles and transfer them to humans through the food chain, research on this topic is still lacking. In considering farm animals as the missing link between soil/plant contamination and human health effects, this paper aims to describe their importance as carriers and vectors of MNP contamination. As research on this topic is in its early stages, there is no standard method to quantify the amount and the characteristics of MNPs in different matrices. Therefore, the creation of a common database where researchers can report data on MNP characteristics and quantification methods could be helpful for both method standardization and the future training of an AI tool for predicting the most abundant/dangerous polymer(s), thus supporting policy decisions to reduce plastic pollution and perfectly fitting with One Health principles. [ABSTRACT FROM AUTHOR]

Simple Summary: Neuroblastoma (NB) is the most frequent solid extracranial tumor in children and the most frequently diagnosed cancer during infancy. A genetic modification, a gain of 17q chromosome arm, is the most common modification in neuroblastoma. Substantial research has been performed on 17q's role in neuroblastoma development and its clinical significance. This paper aims to make a comprehensive review of this evidence. The main findings of the review are: (1) current knowledge supports that 17q gain is involved in the development of neuroblastoma and (2) 17q gain is an important clinical marker independently and in association with other genetic modifications. Neuroblastoma (NB) is the most frequent extracranial solid childhood tumor. Despite advances in the understanding and treatment of this disease, the prognosis in cases of high-risk NB is still poor. 17q gain has been shown to be the most frequent genomic alteration in NB. However, the significance of this remains unclear because of its high frequency and association with other genetic modifications, particularly segmental chromosomal aberrations, 1p and 11q deletions, and MYCN amplification, all of which are also associated with a poor clinical prognosis. This work reviewed the evidence on the clinical and biological significance of 17q gain. It strongly supports the significance of 17q gain in the development of NB and its importance as a clinically relevant marker. However, it is crucial to distinguish between whole and partial chromosome 17q gains. The most important breakpoints appear to be at 17q12 and 17q21. The former distinguishes between whole and partial chromosome 17q gain; the latter is a site of IGF2BP1 and NME1 genes that appear to be the main oncogenes responsible for the functional effects of 17q gain. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to its multiple properties, such as stability, hardness and economic prices, the application of plastics has gradually increased, becoming essential in every industry. Since 1950, the worldwide plastic distribution has progressively created a serious pollution issue caused by difficulties in proper recycling, which has led to the presence of plastic fragments, called microplastics and nanoplastics (MPs/NPs), in the environment. The majority of the research has focused on the aquatic pollution, while studies regarding soil contamination are still poor, with the necessity to better understand how MPs/NPs can enter the food chain and reach humans passing through both crops and animals. Therefore, there is a need for evaluation, and the present work will provide an overview of the sources and distribution of MPs/NPs in farms; different mammalian exposure (digestion, inhalation and dermal contact) and associated risks and health problems caused by these fragments. In particular, this review aims to provide information on the effects, mainly from additives (such as Bisphenol A-BPA), on livestock reproduction and fertility. Pollution due to microplastics and nanoplastics is one of the major environmental issues of the last decade and represents a growing threat to human and animal health. In aquatic species, there is a large amount of information regarding the perturbation of marine organisms; instead, there are only a few studies focusing on the pathophysiological consequences of an acute and chronic exposure to micro- and nanoplastics in mammalian systems, especially on the reproductive system. There are several studies that have described the damage caused by plastic particles, including oxidative stress, apoptosis, inflammatory response, dysregulation of the endocrine system and accumulation in various organs. In addition to this, microplastics have recently been found to influence the evolution of microbial communities and increase the gene exchange, including antibiotic and metal resistance genes. Special attention must be paid to farm animals, because they produce food such as milk, eggs and meat, with the consequent risk of biological amplification along the food chain. The results of several studies indicate that there is an accumulation of microplastics and nanoplastics in human and animal tissues, with several negative effects, but all the effects in the body have not been ascertained, especially considering the long-term consequences. This review provides an overview of the possible adverse effects of the exposure of livestock to micro- and nanoplastics and assesses the potential risks for the disruption of reproductive physiological functions. [ABSTRACT FROM AUTHOR]

Simple Summary: Bull fertility is an important contributor of the herd reproductive success, especially in natural service mating systems. Even if the BBSE (Bulls Breeding Soundness Evaluation) is a rapid and cost-effective screening, it is unable to evaluate all the parameters involved in fertility decline, especially those related to genetics and environment. Based on this, the objectives of the study were (i) to define the semen characteristics of young Italian Simmental bulls, (ii) to evaluate the relationship between scrotal circumference (SC), age, and semen characteristics and (iii) to relate the climatic conditions during the entire spermatogenesis to variations in spermatozoa motility and morphology. Concerning the evaluation of SC, this is the first report on Italian Simmental breed; its increase was linked with the concentration and number of total spermatozoa. Considering the effect of the weather changes, the study confirmed the impact of the increasing temperature and humidity on spermatozoa motility and morphology, accordingly with the moment in which the heat stress occurred. Therefore, considering the importance of fertility, wider knowledge of the factors influencing these parameters will help maintain/improve the reproductive capability of the animals and finally, for the producer, the profitability of his herd. The objectives of the study aimed to evaluate the effect of weather conditions and scrotal circumference (SC) on standard semen characteristics of Italian Simmental young bulls (n = 577), all raised in the same performance station and sampled by the artificial vagina (AV) method. Considering the increasing SC, the results showed a significant increase in quantitative semen parameters (p < 0.05 and p < 0.0001, for sperm concentration and total number of spermatozoa, respectively); for every extra centimeter of SC, 17.5 × 106 spermatozoa/mL and 0.102 × 109 of total spermatozoa were produced. The age of the animal at semen collection (395 and 465 days) had similar positive effects. The effect of the average temperature humidity index (THI limits ≤ 40 and ≥70) in the previous 60, 30, and 10 days before the semen collection was also considered. Sixty days before the semen collection, the increasing THI increased both primary (p < 0.0001) and secondary (p < 0.0001) abnormalities while the percentage of morphologically normal spermatozoa decreased (p < 0.0001). Thirty and ten days before collection, the same effect on morphological traits was maintained, but total and progressive motility was also influenced (p < 0.01) with an unexpected increasing pattern. Thus, environmental conditions can influence semen quality during the entire spermatogenesis and results can guide future research on this breed. [ABSTRACT FROM AUTHOR]

A growing body of literature suggests a close link between olfaction and social expertise. The current study examines whether mentalizing skills are related to olfactory discrimination performance. In order to assess their mentalizing ability, 21 women and 20 men completed the "Reading the Mind in the Eyes" test (RMET). Here, the participants have to infer other persons' mental state from photographs of eye regions. Odor discrimination was assessed using the "Düsseldorf Odour Discrimination Test" (DODT). The DODT consists of 15 items, each containing mixtures of four monomolecular substances. Within each item, two bottles contain the same mixing ratio, while the third contains the reversed mixing ratio. The participants had to identify the deviating odor. Women's expertise in mentalizing (RMET score) is strongly related to olfactory discrimination performance (DODT score): The better women are in mentalizing, the better their performance in olfactory discrimination (rho = 0.572, p = 0.042, Bonferroni-corrected). Men's mentalizing capability was unrelated to olfactory discrimination (rho = −0.117, p > 0.999, Bonferroni-corrected). The current results suggest that the social skill of mentalizing might underly the link between olfaction and social integration, at least in women, and are discussed with regard to the social nature of human olfaction. [ABSTRACT FROM AUTHOR]

There is little information on the actual use of early synthetic organic pigments (SOPs) in art objects, especially those from before 1950. Their presence can, however, pose a challenge to conservation because their chemical composition, as well as their lightfastness and sensitivity to solvents, are often unknown. Here, a study on the non-invasive identification of SOPs in historic pre-1950 varnished paint-outs from artists' materials manufacturer Royal Talens is presented. The paints were analysed using a handheld Raman device. Spectra were evaluated by recording the spectra of the same samples with a benchtop instrument. This study demonstrated that the identification of SOPs in varnished oil paints with a non-invasive approach is possible and rather straightforward. The handheld Raman device allowed us to identify fourteen SOPs from eight pigment classes. Besides the occurrence of expected and the known SOPs of this time period, there were also some surprising results, like the detection of the triarylcarbonium pigments PG2 and PB8, and the monoazo Mordant Yellow 1. [ABSTRACT FROM AUTHOR]

TOR (target of rapamycin), an evolutionarily-conserved serine/threonine kinase, acts as a central regulator of cell growth, proliferation and survival in response to nutritional status, growth factor, and stress signals. It plays a crucial role in coordinating the balance between cell growth and cell death, depending on cellular conditions and needs. As such, TOR has been identified as a key modulator of autophagy for more than a decade, and several deregulations of this pathway have been implicated in a variety of pathological disorders, including cancer. At the molecular level, autophagy regulates several survival or death signaling pathways that may decide the fate of cancer cells; however, the relationship between autophagy pathways and cancer are still nascent. In this review, we discuss the recent cellular signaling pathways regulated by TOR, their interconnections to autophagy, and the clinical implications of TOR inhibitors in cancer. [ABSTRACT FROM AUTHOR]

For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted. [ABSTRACT FROM AUTHOR]

Understanding the relationship between land use and land cover and thermal environment has recently become an emerging issue for urban planners and policy makers. We chose Belgrade, as a case study, to present a cost- and time-effective framework for monitoring spatiotemporal changes of green spaces in relation to the land surface temperature (LST). Time series analysis was performed using Landsat 5 TM and Landsat 8 OLI/TIRS imagery from 1991 to 2019 with an approximate 5-year interval (18 images in total). Spectral vegetation indices and supervised land cover classifications were used to examine changes of green spaces. The results showed a fluctuating trend of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). The highest values were recorded in 2019, indicating vegetation recovery in the last decade. A significant positive correlation was determined between the spectral vegetation indices and the amount of precipitation during growing season. The land cover classification showed that the share of vegetated and bare land decreased by 11.74% during the study period. The most intensive conversion of green and bare land into built-up land cover occurred in the first decade (1991–2000). To assess spatiotemporal changes in the LST, Landsat Collection 2 Surface Temperature products were used. We found a negative correlation between change in the spectral vegetation indices and change in the LST. This indicates that the reduction in vegetation was associated with an increase in the LST. The municipalities that were the most affected in each decade were also identified with our framework. The findings of this study are of great relevance for actions targeting an improvement in urban thermal comfort and climate resilience. [ABSTRACT FROM AUTHOR]

The current study examines neural responses to satiety- and fasting-related volatiles and their effect on the processing of body shapes. Axillary sweat was sampled with cotton pads from 10 individuals after 12 h of fasting, and after having consumed a standard breakfast. Pure cotton pads served as the control. The chemosensory stimuli were presented to 20 participants (via a constant-flow olfactometer) exclusively, and additionally as context to images of overweight and underweight avatars. EEG was recorded (61 electrodes), and chemosensory (CSERPs; P1, N1, P2, P3) and visual event-related potentials (VERPs; N1, P2, P3a, P3b) were analyzed. The amplitudes of all positive CSERP components differed more strongly from cotton in response to chemosensory satiety cues as compared to fasting cues (P1: p = 0.023, P2: p = 0.083, P3: p = 0.031), paralleled by activity within the middle frontal and temporal gyrus. Overweight compared to underweight body shapes tended to elicit larger VERP P2 amplitudes (p = 0.068), and chemosensory satiety cues amplified the VERP amplitudes in response to any body shape (P2, P3a, P3b; all ps ≤ 0.017) as compared to the cotton control. The results indicate that chemosensory satiety cues transmit complex social information, overriding the processing of analogous visual input. [ABSTRACT FROM AUTHOR]

E-learning is widely used in academic education, and currently, the COVID-19 pandemic is increasing the demand for e-learning resources. This report describes the results achieved and the experiences gained in the Erasmus+ CBHE (Capacity Building in Higher Education) project "Innovation on Remote Sensing Education and Learning (IRSEL)". European and Asian universities created an innovative open source e-learning platform in the field of remote sensing. Twenty modules tailored to remote sensing study programs at the four Asian partner universities were developed. Principles of remote sensing as well as specific thematic applications are part of the modules, and a knowledge pool of e-learning teaching and learning materials was created. The focus was given to case studies covering a broad range of applications. Piloting with students gave evidence about the usefulness and quality of the developed modules. In particular, teachers and students who tested the modules appreciated the balance of theory and practice. Currently, the modules are being integrated into the curricula of the participating Asian universities. The content will be available to a broader public. [ABSTRACT FROM AUTHOR]

Climate change education (CCE) fosters the skills and behavioral patterns of students in regards to climate-related challenges and risks. Despite its importance, the integration of CCE in schools is challenging due to the interdisciplinary nature of climate science and the obstacles and demands of everyday school reality. Here, we examine the case of satellite Remote Sensing (RS) for Earth Observation (EO) as an innovative tool for facilitating CCE. We focus on Greece, a country that, despite being a hot spot for climate change, shows a low level of CCE integration in schools and awareness for EO-based educational resources. Based on interviews with in-service teachers, our research reveals the following: (a) there is a high interest in how satellites depict environmental phenomena; (b) EO is considered an efficient vehicle for promoting CCE in schools because it illustrates climate change impacts most effectively; (c) local natural disasters, such as intense forest fires and floods, are more familiar to students and, thus, preferable for teaching when compared to global issues, such as the greenhouse effect and sea level rise; and (d) educators are in favor of short, hands-on, EO-based activities (also known as "activity-shots"), as the most useful material format for integrating climate change topics in their everyday teaching practice. [ABSTRACT FROM AUTHOR]

Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results ( R ¯ ) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum R ¯ of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 104 km2 and 667.44 × 104 km2 with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management. [ABSTRACT FROM AUTHOR]

The thermography as a methodology to quantitative data acquisition is not usually addressed in the degrees of university programs. The present manuscript proposes a novel approach for the acquisition of advanced competences in engineering courses associated with the use of thermographic images via free/open-source software solutions. This strategy is established from a research based on the statistical and three-dimensional visualization techniques over thermographic imagery to improve the interpretation and comprehension of the different sources of error affecting the measurements and, thereby, the conclusions and analysis arising from them. The novelty is focused on the detection of non-normalities in thermographic images, which is illustrates in the experimental section. Additionally, the specific workflow for the generation of learning material related with this aim is raised for asynchronous and e-learning programs. These virtual materials can be easily deployed in an institutional learning management system, allowing the students to work with the models by means of free/open-source solutions easily. Subsequently, the present approach will give new tools to improve the application of professional techniques, will improve the students' critical sense to know how to interpret the uncertainties in thermography using a single thermographic image, therefore they will be better prepared to face future challenges with more critical thinking. [ABSTRACT FROM AUTHOR]

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring. [ABSTRACT FROM AUTHOR]

Environmental remote sensing has faced increasing satellite data availability, advanced algorithms for thematic analysis, and novel concepts of ground truth. For that reason, contents and concepts of learning and teaching remote sensing are constantly evolving. This eventually leads to the intuition of methodologically linking academic learning assignments with case-related scopes of application. In order to render case-related learning possible, smart teaching and interactive learning contexts are appreciated and required for remote sensing. That is due to the fact that those contexts are considered promising to trigger and gradually foster students' comprehensive interdisciplinary thinking. To this end, the following contribution introduces the case-related concept of applying simulation games as a promising didactic format in teaching/learning assignments of remote sensing. As to methodology, participating students have been invited to take on individual roles bound to technology-related profiles (e.g., satellite-mission planning, irrigation, etc.) Based on the scenario, stakeholder teams have been requested to elaborate, analyze and negotiate viable solutions for soil moisture monitoring in a defined context. Collaboration has been encouraged by providing the protected, specifically designed remoSSoil-incubator environment. This letter-type paper aims to introduce the simulation game technique in the context of remote sensing as a type of scholarly teaching; it evaluates learning outcomes by adopting certain techniques of scholarship of teaching and learning (SoTL); and it provides food for thought of replicating, adapting and enhancing simulation games as an innovative, disruptive next-generation learning environment in remote sensing. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes. [ABSTRACT FROM AUTHOR]

In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring. [ABSTRACT FROM AUTHOR]

The hepatitis C virus NS2/3 protein is a highly hydrophobic protease responsible for the cleavage of the viral polypeptide between non-structural proteins NS2 and NS3. However, many aspects of the NS2/3 protease's role in the viral life cycle and mechanism of action remain unknown. Based on the recently elucidated crystal structure of NS2, NS2/3 has been proposed to function as a cysteine protease despite its lack of sequence homology to proteases of known function. In addition, although shown to be required for HCV genome replication and persistent infection in a chimpanzee, the role of NS2/3 cleavage in the viral life cycle has not yet been fully investigated. However, several recent studies are beginning to clarify possible roles of the cleaved NS2 protein in modulation of host cell gene expression and apoptosis.