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Forest degradation is a major issue in ecosystem monitoring, and to take reformative measures, it is important to detect, map, and quantify the losses of forests. Synthetic Aperture Radar (SAR) time-series data have the potential to detect forest loss. However, its sensitivity is influenced by the ecoregion, forest type, and site conditions. In this work, we assessed the accuracy of open-source C-band time-series data from Sentinel-1 SAR for detecting deforestation across forests in Africa, South Asia, and Southeast Asia. The statistical Cumulative Sums of Change (CuSUM) algorithm was applied to determine the point of change in the time-series data. The algorithm's robustness was assessed for different forest site conditions, SAR polarizations, resolutions, and under varying moisture conditions. We observed that the change detection algorithm was affected by the site- and forest-management activities, and also by the precipitation. The forest type and eco-region affected the detection performance, which varied for the co- and cross-pol backscattering components. The cross-pol channel showed better deforested region delineation with less spurious detection. The results for Kalimantan showed a better accuracy at a 100 m spatial resolution, with a 25.1% increase in the average Kappa coefficient for the VH polarization channel in comparison with a 25 m spatial resolution. To avoid false detection due to the high impact of soil moisture in the case of Haldwani, a seasonal analysis was carried out based on dry and wet seasons. For the seasonal analysis, the cross-pol channel showed good accuracy, with an average Kappa coefficient of 0.85 at the 25 m spatial resolution. This work was carried out in support of the upcoming NISAR mission. The datasets were repackaged to the NISAR-like HDF5 format and processing was carried out with methods similar to NISAR ATBDs. [ABSTRACT FROM AUTHOR]

We are already experiencing the rapid pace of environmental perturbation in the Anthropocene, necessitating the development of new tools and techniques for measuring changes in ecosystem dynamics. Sentinel species, from birds to invertebrates, have been used to provide insights into ecosystem function, as leading indicators of risk to human health and as harbingers of future change, with implications for ecosystem structure and function. Here, we offer an update to previous research identifying marine top predators as indicators of ecosystem shifts and examine terrestrial sentinels and the latest research on sentinels of pollution and human health. Using ecosystem sentinels enables rapid response and adaptation to ecosystem variability and environmental change in part because they may be easier to observe and in part because they may serve as leading indicators of ecosystem disruption. While there may not be a given taxon that is best suited as sentinels, we highlight how to select the most effective sentinels, including examples of when sentinel species have been incorporated into management. Choosing a suite of appropriate sentinels both will give insight into ecosystem processes and can help manage changing ecosystems into the future. [ABSTRACT FROM AUTHOR]

The Gedeo zone agroforestry systems are the main source of Ethiopia's coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km2, the mapped coffee coverage is 583 km2. The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km2), sub-suitable (596.1 km2), and suitable (347.1 km2) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity. [ABSTRACT FROM AUTHOR]

Plant species diversity is key to ecosystem functioning, but in recent decades anthropogenic activities have prompted an alarming decline in this community trait. Thus, developing strategies to understand diversity dynamics based on affordable and efficient remote sensing monitoring is essential, as well as examining the relevance of spatial scale and vegetation structural complexity to these dynamics. Here, we used two mathematical approaches to assess the relationship between tropical woody species diversity and spectral diversity in a human-modified landscape in two vegetation types differing in their degree of complexity. Vegetation complexity was measured through the fraction of species that concentrate different proportions of the cumulative importance value index. Species diversity was assessed using Hill numbers at three spatial scales, and metrics of spectral heterogeneity, vegetation indices, as well as raw data from Landsat 9 and Sentinel-2 sensors were calculated and analysed through general linear models (GLM) and Random Forest. Vegetation complexity emerged as an important variable in modelling species from remote sensing metrics, indicating the need to model species diversity by vegetation type rather than region. Hill numbers showed different relationships with remotely sensed metrics, in consistency with the scale-dependency of ecological processes on species diversity. Contrary to multiple previous reports, in our study, GLMs produced the best fits between Hill numbers of all orders and remotely sensed metrics. If we are to meet the need of conducting efficient and speedy woody species diversity monitoring globally, we propose modelling this diversity from remotely-sensed variables as an attractive strategy, so long as the intrinsic properties of each vegetation type are acknowledged to avoid under- or overestimation biases. [ABSTRACT FROM AUTHOR]

Purpose: The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query‐ready, quality‐checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real‐world data for regulatory decision‐making. Methods: In this report, we describe the resulting network, the Real‐World Evidence Data Enterprise (RWE‐DE), including data from two commercial EHR‐claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives. Results: We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR‐claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free‐text notes in the Development Network for efficient retrieval as needed. Conclusions: Finally, we outline typical use cases for the RWE‐DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system. [ABSTRACT FROM AUTHOR]

The study aimed to determine how changes in land cover and surface water are being made using stratified objectoriented analysis based on the interpretation of remote sensing images. It is the first step toward managing the region’s annual land-use inventories projects. The study used Sentinel-2 images from 2019 through 2021 to delineate the changing urban land cover in the Ninh Kieu District, Can Tho City, Viet Nam. The study used QGIS software to interpret the images and eCognition software to classify the objects based on the NDBI, NDVI, and NDWI indices. The interpretation results were checked for the accuracy, and the land cover was changed over the years. The results show that urban land cover changes with the increase of urban land and the decrease of vegetation land used for urban land, while water surface area inwards decreased from 2019 to 2020 but increased in 2021. Maps of the current state of the urban land covers in the study area were delineated. The interpretation results contribute to the preliminary method by using satellite images for the annual land use inventory project in the region, even though some difficulties still exist and need to be modified. [ABSTRACT FROM AUTHOR]

Accurate estimation of atmospheric parameters is crucial for accurate atmospheric correction as well as aerosol retrieval. While spectral responsivity within a spectral band is much higher at central wavelength, the sensor is sensitive across the entire bandwidth, thereby increasing the possibility of differences in atmospheric parameters estimated using only the central wavelength as compared to that using the relative spectral response (RSR). In the present study, an attempt has been made to investigate the differences in the values of different atmospheric parameters and the consequent impact it has on surface reflectance (SR) estimation and aerosol optical depth (AOD) retrieval from Operational Land Imager (OLI) sensor onboard Landsat 8 and Multispectral Imager (MSI) onboard Sentinel 2. The SR has been estimated from the top-of-atmosphere signals using the Simplified and Robust Surface Reflectance Estimation Method (SREM). AOD has been thereafter, retrieved using a simplistic physics-based approach in single scattering approximation. The results suggest that though the difference in RSR and central wavelength derived parameters is quite small in absolute terms, the effect of RSR is more evident in case of green spectral band for Landsat 8 and in blue band for Sentinel 2. The difference in retrieved AOD is more pronounced in case of OLI as compared to MSI, with difference being more in green band for the former and in blue band for the latter. [ABSTRACT FROM AUTHOR]

Monitoring agricultural land over vast geographical areas presents challenges due to the absence of accurate, comprehensive and precise data, which has become a complex process that is difficult to do in terms of both timespans and consistency. Hence, this study presents an improved approach for the identification of agricultural land by utilizing the capabilities of Sentinel-1 and Sentinel-2 satellites with a variety of vegetation and non-vegetation indices and machine learning algorithms. The Multispectral Correlation Mapper (MCM) and Random Forest (RF) algorithms are adopted to train different agricultural lands, crop types and sowing and cultivation seasons. The 45-bands mega-file data cube (MFDC) fusion for each season incorporates essential indices and features derived from the Sentinel-1 and Sentinel-2 datasets for both seasons, i.e. Rabi (winter-spring season) and Kharif (summer-autumn season). The proposed method demonstrated resilience when applied to satellite datasets while effectively reducing the impact of non-agricultural elements such as shrubs, grass, bare soil and orchards. The results demonstrate a notable ability to differentiate between the Rabi and Kharif seasons, resulting in a high level of precision in measuring the extent of cultivated land during the Rabi and Kharif seasons with an area of 626,947 acres and 590,858 acres, respectively. The total land area, ascertained from the observation of the comprehensive cropping pattern and agricultural modifications during the entire year (June 2021–May 2022) is 635,655 acres. The validation exercise shows the higher accuracy of this method for cropland, with an overall accuracy of 98.8%, kappa of 0.97, user accuracy of 98.69% and producer accuracy of 99.13%. Additionally, it was spatially compared with the ESRI, ESA and MODIS cropland layers and government statistical data. Furthermore, the research investigates the temporal dynamics of agricultural growth phases using spectral bands and indices. This approach improves the accuracy of precise cropland identification and provides useful insights into crop phenology. [ABSTRACT FROM AUTHOR]

Long revisit intervals and cloud susceptibility have restricted the applicability of earth observation satellites in surface water studies. Integrating multiple satellites offers potential for more frequent observations, yet combining different satellite sources, particularly optical and SAR satellites, presents complexities. This research explores the data-fusion potential and limitations of Landsat-8/9 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture (SAR) satellites to enhance surface water monitoring. By focusing on segmented surface water images, we demonstrate that combining optical and SAR data is generally effective and straightforward using a simple statistical thresholding algorithm. Kappa coefficients(κ) ranging from 0.80 to 0.95 indicate very strong harmony for integration across reservoirs, lakes, and river environments. In vegetative environments, integration with S1SAR shows weak harmony, with κ values ranging from 0.27 to 0.45, indicating the need for further studies. Global revisit interval maps reveal significant improvement in median revisit intervals from 15.87 to 22.81 days using L8/9 alone, to 4.51 to 7.77 days after incorporating S2, and further to 3.48 to 4.62 days after adding S1SAR. Even during wet season months, multi-satellite fusion maintained the median revisit intervals to less than a week. Maximizing all available open-source earth observation satellites is integral for advancing studies requiring more frequent surface water observations, such as flood, inundation, and hydrological modeling. [ABSTRACT FROM AUTHOR]

Lipids interfere with absorbance measurements conducted using colorimetric methods. To monitor lipemia, some systems measure absorbance using an analyzer. This report describes a novel case of interference with the lipemia index without lipemia. A 64-year-old woman with giant basal cell carcinoma underwent resection and sentinel lymph node biopsy. The patient had been subcutaneously injected with patent blue during sentinel lymph node resection. After surgery, her serum and urine were yellow-green, and the lipemia index, calculated by measuring absorbance at 658 nm (main wavelength) and 694 nm (secondary wavelength) using a JCA-BM8040 chemistry analyzer, was high. The absorbance spectrum of the patient's serum and patent blue solution were compared to determine the cause of the high lipemia index. The patient's serum and the patent blue solution showed absorption at wavelengths between 540 and 698 nm. Moreover, the absorbance was concentration-dependent for patent blue. These results thus indicated that the patient's serum contained patent blue. Here, we report a case wherein patent blue affected the lipemia index. Thus, it must be noted that patent blue injection may yield inaccurate results when evaluating lipemia index. [ABSTRACT FROM AUTHOR]

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Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government to strengthen food security. The proper monitoring of crop production is vital to government agencies, as well as insurance and banking structures. These organizations offer subsidies through different levels support. Some farmers already use farmland soil monitoring combined with adaptive combinations of different crops. These include winter–spring plowing crop programs. Winter wheat crops are generally more adaptive and may survive summer droughts. Kazakhstan is a large country with large plots of farmland, which are complicated to monitor. Therefore, it would be reasonable to adapt more efficient technologies and methodologies, such as remote sensing. This research work presents a method for identifying winter wheat crops in the foothills of South Kazakhstan by employing multi-temporal Sentinel-2 data. Here, the researchers adapted and applied a Plowed Land Index, derived from the Brightness Index. The methodology encompasses satellite data processing, the computation of Plowed Land Index values for the swift recognition of plowed fields and the demarcation of winter wheat crop sowing regions, along with a comparative analysis of the acquired data with ground surveys. [ABSTRACT FROM AUTHOR]

O processo de mudança do uso e cobertura do solo tem atraído grande atenção para seu melhor entendimento e planejamento para evitar impactos negativos como perda de biodiversidade, erosão hídrica do solo, arenização, entre outros. Frente a isto, este estudo objetivou realizar uma análise espaço-temporal, da cobertura vegetal do município de Alegrete localizado no estado do Rio Grande do Sul, através dos Índices de Vegetação pela Razão Simples (RS) e Diferença Normalizada (NDVI) no período compreendido entre os anos 2016 e 2020. Para a determinação dos Índices de Vegetação pela Razão Simples (RS) e Diferença Normalizada (NDVI) foram utilizadas imagens do satélite Sentinel 2A nível 1C para o dia 17/01/2016 e 23/08/2020 (folhas 21JWH, 21JXH, 21JWG e 21 JXG). Foi possível observar que no ano de 2016, há visualmente uma maior quantidade de valores de RS baixos, indicando coloração mais avermelhada na porção noroeste do município que se estende até o centro da imagem e no ano de 2020, houve uma redução na quantidade de vegetação mais desenvolvida e ou mais densa na qual os valores de RS mostraram-se mais uniformes pela área de estudo. Com relação ao NDVI no ano de 2016 não apresentou valores para as classes -1.0 a -0.8 e -0.8 a -0.6, o que demonstra que no ano de 2016 havia mais áreas com vegetação rasteira. Nas áreas onde o NDVI apresentou valores entre -0.6 a -0.4 no ano de 2016, no ano de 2020 estes valores passaram para -1.0 a -0.8, o que pode ser justificado pelo aumento das áreas de solo exposto. Conclui-se que, através da aplicação de conceitos radiométricos e dos índices de vegetação razão simples e NDVI, foi possível realizar uma análise espaço-temporal da vegetação no município de Alegrete para os anos de 2016 e 2020.

The study aims to determine how changes in land cover and surface water are being made using stratified object-oriented analysis based on the interpretation of remote sensing images. It is the first step toward managing the region's annual land-use inventories. The study uses Sentinel-2 images from 2019 through 2021 to delineate the changing urban land cover in Ninh Kieu District, Can Tho City, Viet Nam. The study used QGIS software to interpret the images and eCognition software to classify the objects based on the NDBI, NDVI, and NDWI indices. The interpretation results were checked for reliability, and the land cover was changed over the years. The results show that urban land cover changes with the increase of urban land and the decrease of vegetation land used for urban land, while water surface area inwards decreased from 2019 to 2020 but increased in 2021. Maps of the current state of the urban land covers in the study area were delineated. The interpretation results contribute to the premilitary method by using satellite images for the annual land use inventory in the region, even though some difficulties still exist and need to be modified.

In the post-pandemic period, an endemic circulation of respiratory viruses has been re-established. Respiratory viruses are co-circulating with SARS-CoV-2. We performed a retrospective analysis of co-infections in primary care patients with medically attended acute respiratory infections (MAARI) who consulted from week 40/2022 to week 39/2023 and were tested for a panel of respiratory viruses. Out of 2099 samples tested, 1260 (60.0%) were positive for one virus. In 340 samples, co-infection was detected: two viruses in 281 (13.4%), three viruses in 51 (2.4%), and four viruses in eight (0.4%) samples. Respiratory viruses co-infected the patients with MAARI at very different rates. The lowest rates of co-infections were confirmed for influenza B (13.8%) and influenza A (22.9%) and the highest for human bocaviruses (84.0%) and human parechoviruses (82.1%). Co-infections were detected in 28.2% of SARS-CoV-2 positive samples. SARS-CoV-2 has never been co-infected with influenza B virus, enterovirus or adenovirus, although the latter was found as a co-infecting virus with all other respiratory viruses tested. The rate of co-infections decreased significantly with increasing age (p-value 0.000), and no difference was found regarding gender (p-value 0.672). It is important to understand the epidemiology of respiratory co-infections for prevention and management decisions in patients with MAARI. [ABSTRACT FROM AUTHOR]

The accurate extraction and monitoring of offshore aquaculture areas are crucial for the marine economy, environmental management, and sustainable development. Existing methods relying on unimodal remote sensing images are limited by natural conditions and sensor characteristics. To address this issue, we integrated multispectral imaging (MSI) and synthetic aperture radar imaging (SAR) to overcome the limitations of single-modal images. We propose a cross-modal multidimensional frequency perception network (CMFPNet) to enhance classification and extraction accuracy. CMFPNet includes a local–global perception block (LGPB) for combining local and global semantic information and a multidimensional adaptive frequency filtering attention block (MAFFAB) that dynamically filters frequency-domain information that is beneficial for aquaculture area recognition. We constructed six typical offshore aquaculture datasets and compared CMFPNet with other models. The quantitative results showed that CMFPNet outperformed the existing methods in terms of classifying and extracting floating raft aquaculture (FRA) and cage aquaculture (CA), achieving mean intersection over union (mIoU), mean F1 score (mF1), and mean Kappa coefficient (mKappa) values of 87.66%, 93.41%, and 92.59%, respectively. Moreover, CMFPNet has low model complexity and successfully achieves a good balance between performance and the number of required parameters. Qualitative results indicate significant reductions in missed detections, false detections, and adhesion phenomena. Overall, CMFPNet demonstrates great potential for accurately extracting large-scale offshore aquaculture areas, providing effective data support for marine planning and environmental protection. Our code is available at Data Availability Statement section. [ABSTRACT FROM AUTHOR]

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However, broad-scale DSM remains challenging because of non-purposively sampled soil data, large data volumes for processing extensive soil covariates, and high model complexities due to spatially varying soil–landscape relationships. This study presents a three-dimensional DSM framework for Switzerland, targeting the soil properties of clay content (Clay), organic carbon content (SOC), pH value (pH), and potential cation exchange capacity (CECpot). The DSM approach is based on machine learning and a comprehensive exploitation of soil and remote sensing data archives. Quantile Regression Forest was applied to link the soil sample data from a national soil data base with covariates derived from a LiDAR-based elevation model, from climate raster data, and from multispectral raster time series based on satellite imagery. The covariate set comprises spatially multiscale terrain attributes, climate patterns and their temporal variation, temporarily multiscale land use features, and spectral bare soil signatures. Soil data and predictions were evaluated with respect to different landcovers and depth intervals. All reference soil data sets were found to be spatially clustered towards croplands, showing an increasing sample density from lower to upper depth intervals. According to the R2 value derived from independent data, the overall model accuracy amounts to 0.69 for Clay, 0.64 for SOC, 0.76 for pH, and 0.72 for CECpot. Reduced model accuracies were found to be accompanied by soil data sets showing limited sample sizes (e.g., CECpot), uneven statistical distributions (e.g., SOC), and low spatial sample densities (e.g., woodland subsoils). Multiscale terrain covariates were highly influential for all models; climate covariates were particularly important for the Clay model; multiscale land use covariates showed enhanced importance for modeling pH; and bare soil reflectance was a major driver in the SOC and CECpot models. [ABSTRACT FROM AUTHOR]

Simple Summary: A sentinel lymph node (SLN) biopsy is recommended for melanoma patients with a Breslow thickness of at least 1 mm. This procedure is crucial for staging and determining the extent of melanoma metastasis. To improve the accuracy of SLN identification and reduce false negatives, the use of the Sentinella gamma camera is introduced intraoperatively. This tool enhances the identification rate of SLNs as compared to the traditional gamma hand-held probe. Surgeons perform the ex vivo dissection of the resected SLN clusters to differentiate sentinel from non-sentinel lymph nodes, which are then examined pathologically. At the Center for Melanoma Research and Treatment at the California Pacific Medical Center, a comprehensive multidisciplinary approach is implemented to discuss the diagnosis and treatment of melanoma patients at the weekly Melanoma Tumor Board. Pathologists evaluate surgical specimens to ensure precise diagnosis. Radiologists discuss imaging studies. A consensus-driven approach is taken by dermatologists, surgeons, medical oncologists, and radiation oncologists at the Melanoma Tumor Board to tailor treatment plans for each melanoma patient. This multi-disciplinary program ensures personalized and high-quality care for melanoma patients and can serve as a model for treating other types of cancer. According to the American Joint Commission on Cancer (AJCC) 8th edition guidelines, SLN biopsy is recommended for primary melanomas with a Breslow thickness of at least 1 mm. Additionally, the National Comprehensive Cancer Network (NCCN) recommends that a SLN biopsy may be considered for melanoma patients with T1b lesions, which are 0.8–1 mm thick or less than 0.8 mm thick with ulceration. It can also be considered for T1a lesions that are less than 0.8 mm thick but have other adverse features, such as a high mitotic rate, lymphovascular invasion, or a positive deep margin. To reduce the false negative rate of melanoma SLN biopsy, we have introduced the intraoperative use of Sentinella, a gamma camera, to enhance the identification rate of SLNs beyond that of the traditional gamma hand-held probe. At the Center for Melanoma Research and Treatment at the California Pacific Medical Center, a multidisciplinary approach has been established to treat melanoma patients when the diagnosis of primary melanoma is made with a referral to our melanoma center. This comprehensive approach at the melanoma tumor board, including the efforts of pathologists, radiologists, dermatologists, surgical, medical and radiation oncologists, results in a consensus to deliver personalized and high-quality care for our melanoma patients. This multidisciplinary program for the management of melanoma can be duplicated for other types of cancer. This article consists of current knowledge to document the published methods of identification of sentinel lymph nodes. In addition, we have included new data as developed in our melanoma center as newly published materials in this article to demonstrate the utility of these methods in melanoma sentinel lymph node surgery. Informed consent has been waived by our IRB regarding the acquisition of clinical data as presented in this study. [ABSTRACT FROM AUTHOR]

The 2013–2016 Ebola virus outbreak in West Africa galvanized a quest for more knowledge with regards to the ecology of the disease. In its immediate aftermath, research initiatives, at the junction of biosecurity and One Health, were mounted to elucidate the circulation of the Ebola virus and other emergent pathogens through sampling local wildlife, in particular bats. The article investigates the knowledge, affects, and practices mitigating care and risk in encounters between human animals and potentially contaminated nonhuman animals. Grounded in an ethnography of the labor of wildlife sampling by Guinean veterinarians, it adopts an interspecies perspective on the One Health laboratory, a place where relations between animals and humans are inflected by a postcolonial, gendered, and anthropocentric imbalance of power. It argues that, rather than blurring interspecies boundaries, scientific care for sampled bats may cement hierarchies, with consequences for samplers and animals. [ABSTRACT FROM AUTHOR]

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa's anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous "developed" or "built-up" area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. [ABSTRACT FROM AUTHOR]

This paper quantifies the annual dynamics of the shortwave radiation reflected from bare arable land as a result of smoothing previously plowed land located in three different agricultural subregions of the European Union and associated countries. This estimate takes into account the annual variation of the bare arable land area, obtained from Sentinel 2 satellite imagery; the spatial variability of soil units within croplands, obtained from digital soil and land-cover maps; and the laboratory spectral reflectance characteristics of these units, obtained from soil samples stored in the LUCAS soil database. The properties of the soil units, which cover an area of at least 4% of each subregion, were characterized. The highest amounts of shortwave radiation reflected under clear-sky conditions from air-dried, bare arable land surfaces—approximately 850 PJ day−1 and 1.10 EJ day−1 for land shaped by a plow (Pd) and smoothing harrow (Hs), respectively—were found in the summer around 8 August in the western subregion. However, the lowest radiation occurred in the spring on 10 April at 340 PJ day−1 for Pd and 430 PJ day−1 for Hs in the central subregion. The largest and the smallest amounts of this radiation throughout the year—only as a result of smoothing, by Hs, land that was previously treated by Pd—was estimated at 42 EJ for the western and southern subregions and 19 EJ for the central subregion, respectively. [ABSTRACT FROM AUTHOR]

The water diversion project in Central Yunnan Province (WDP-YN) is the largest water diversion project under construction in China. However, the ecological effects of this water diversion project are still unclear. This study utilized Sentinel-2 remote sensing data to estimate fractional vegetation cover (FVC), maps spatiotemporal variations of FVC in construction areas from 2017 to 2022, and evaluates the impact of the WDP-YN on regional vegetation coverage using buffer analysis and vegetation type transition matrix methods. The study led to the following findings: (1) From 2017 to 2022, FVC within 10 km of the tunnel construction route showed a slightly downward trend or remained relatively stable with no significant changes in the spatial pattern of FVC. (2) Before and after the construction of WDP-YN, over 60% of the area within 10 km of the tunnel construction route showed no change in FVC. On Construction Route Section I (CRS-I), vegetation improved and/or degraded within 12.90% (14.10%) of the area and the regions with degraded FVC concentrated in the northern CRS-I. For Construction Route Section II (CRS-II), 11.96% and 27.51% of the regions were dominated by improved and/or degraded FVC. Vegetation changes near Groundwater Monitoring Point a (GMPa) were relatively stable. (3) The WDP-YN degraded vegetation within 2 km of both sides of CRS-I, slowing down the increase in FVC, while the WDP-YN improved vegetation within 2–6 km of both sides of CRS-II, the closer the distance to CRS-II, the faster the increase in FVC and the decrease in FVC slowed down within 0–2 km of both sides of CRS-II. This study sheds light on the impacts of water diversion infrastructure on vegetation coverage and provides practical guidance and reference for eco-environment protection and ecological restoration given water conservancy projects in China and other regions of the world. [ABSTRACT FROM AUTHOR]

Introduction: The current standard treatment for endometrial cancer is a laparoscopic hysterectomy with adnexectomies and bilateral sentinel node resection. A retroperitoneal vNOTES sentinel node resection has several theoretical potential advantages. These include being less invasive, leaving no visible scars, operating without Trendelenburg, and therefore offering the anesthetic advantage of easier ventilation in obese patients and following the natural lymph node trajectory from caudally to cranially and therefore a lower risk of missing the sentinel node. The aim of this study is to determine the feasibility of a retroperitoneal vNOTES approach to sentinel lymph node dissection for staging of endometrial cancer. Material and methods: A prospective multicenter case series was performed in four hospitals. A total of 64 women with early‐stage endometrial carcinoma suitable for surgical staging with sentinel lymph node removal were operated via a transvaginal retroperitoneal vNOTES approach. The paravesical space was entered through a vaginal incision after injecting the cervix with indocyanine green. A vNOTES port was placed into this space and insufflation of the retroperitoneum was performed. Sentinel lymph nodes were identified bilaterally using near‐infrared light followed by endoscopic removal of these nodes. Results: A total of 64 women with early‐stage endometrial cancer underwent sentinel lymph node removal by retroperitoneal vNOTES technique. All patients also underwent subsequent vNOTES hysterectomy and bilateral salpingo‐oophorectomy. The median age was 69.5 years, median total operative time was 126 min and the median estimated blood loss was 80 mL. In 97% of the cases bilateral sentinel nodes could be identified. A total of 60 patients had negative sentinel nodes, three had isolated tumor cells and one had macroscopically positive sentinel nodes. No complications with sequel occurred. Conclusions: This prospective multicenter case series demonstrates the feasibility of the vNOTES approach for identifying and removing sentinel lymph nodes in women with endometrial carcinoma successfully and safely. vNOTES allows sole transvaginal access with exposure of the entire retroperitoneal space, following the natural lymph trajectory caudally to cranially, and without the need for a Trendelenburg position. [ABSTRACT FROM AUTHOR]

Soil moisture is one of the least monitored of all the hydrologic variables. It is greatly influenced by unpredictable and intermittent precipitation, varying evapotranspiration rates, heterogeneous soils, land cover, topography, and is extremely changeable in both space and time. The aim of this study is to retrieve surface and rootzone soil moisture in fallow land at a field scale using Sentinel-1A SAR data. The study explores the potential of obtaining surface soil moisture over fallow land at two different soil types from C-band SAR data. The study area consists of two plots having different soil types. The study area was divided into 80 grids, each measuring 10 × 10 m, to collect soil samples which are synchronized with Sentinel-1A passes. The soil moisture which are retrieved from plot 1 were used to develop the model. The developed model was validated in plot 2. In order to study the impact of soil moisture and dielectric constant on backscattering coefficients, a multiple regression analysis was used to create a semi-empirical model. Rootzone soil moisture retrieval model was developed by considering the backscattered coefficient, volumetric surface soil moisture as an independent variable and volumetric rootzone soil moisture as dependent variable. The predicted surface soil moisture using the regression model were identical to in-situ observed surface soil moisture, with R2 of 0.77, RMSE of 1.31 m3/m3, and NSE of 0.75. The estimated rootzone soil moisture matches the in-situ observed rootzone soil moisture identically with R2 = 0.74, RMSE = 1.23 m3/m3, NSE = 0.73. This study aids local farmers in their irrigation water management. [ABSTRACT FROM AUTHOR]

Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation models. By solving the aforementioned issues, a deeply fused super resolution guided semantic segmentation network using 30 m Landsat images is proposed. A large-scale dataset comprising 10 m Sentinel-2, 30 m Landsat-8 images, and 10 m European Space Agency (ESA) Land Cover Product is introduced, facilitating model training and evaluation across diverse real-world scenarios. The proposed Deeply Fused Super Resolution Guided Semantic Segmentation Network (DFSRSSN) combines a Super Resolution Module (SRResNet) and a Semantic Segmentation Module (CRFFNet). SRResNet enhances spatial resolution, while CRFFNet leverages super-resolution information for finer-grained land cover classification. Experimental results demonstrate the superior performance of the proposed method in five different testing datasets, achieving 68.17–83.29% and 39.55–75.92% for overall accuracy and kappa, respectively. When compared to ResUnet with up-sampling block, increases of 2.16–34.27% and 8.32–43.97% were observed for overall accuracy and kappa, respectively. Moreover, we proposed a relative drop rate of accuracy metrics to evaluate the transferability. The model exhibits improved spatial transferability, demonstrating its effectiveness in generating accurate land cover maps for different cities. Multi-temporal analysis reveals the potential of the proposed method for studying land cover and land use changes over time. In addition, a comparison of the state-of-the-art full semantic segmentation models indicates that spatial details are fully exploited and presented in semantic segmentation results by the proposed method. [ABSTRACT FROM AUTHOR]

Artificial corner reflectors are widely used in the vegetated landslide for time series InSAR monitoring due to their permanent scattering features. This paper investigated the RCS features of a novel dihedral CR under multi-resource SAR datasets. An RCS reduction model for the novel dihedral corner reflector has been proposed to evaluate the energy loss caused by the deviation between the SAR incident angle and the CR's axis. On the Huangtupo slope, Badong county, Hubei province, tens of dihedral CRs had been installed and the TSX–spotlight and Sentinel-TOPS data had been collected. Based on the observation results of CRs with more than ten deviation angles, the proposed reduction model was tested with preferable consistency under a real dataset, while 2 dBsm of systematic bias was verified in those datasets. The maximum incident angle deviation in the Sentinel data overlapping area is over 12°, which leads to a 2.4 dBsm RCS decrease for horizontally placed dihedral CRs estimated by the proposed model, which has also been testified by the observed results. The testing results from the Sentinel data show that in high, vegetation-covered mountain areas like the Huangtupo slope, the dihedral CRs with a 0.4 m slide length can be achieve 1 mm precision accuracy, while a side length of 0.2 m can achieve the same accuracy under TSX–spotlight data. [ABSTRACT FROM AUTHOR]

Vineyards and olive groves are two of the most important Mediterranean crops, not only for their economic value but also for their cultural and environmental significance, playing a crucial role in global agriculture. This systematic review, based on an adaptation of the 2020 PRISMA statement, focuses on the use of satellite remote sensing tools for the detection of drought in vineyards and olive groves. This methodology follows several key steps, such as defining the approach, selecting keywords and databases, and applying exclusion criteria. The bibliometric analysis revealed that the most frequently used terms included "Google Earth Engine" "remote sensing" "leaf area index" "Sentinel-2", and "evapotranspiration". The research included a total of 81 articles published. The temporal distribution shows an increase in scientific production starting in 2018, with a peak in 2021. Geographically, the United States, Italy, Spain, France, Tunisia, Chile, and Portugal lead research in this field. The studies were classified into four categories: aridity and drought monitoring (ADM), agricultural water management (AWM), land use management (LUM), and water stress (WST). Research trends were analysed in each category, highlighting the use of satellite platforms and sensors. Several case studies illustrate applications in vineyards and olive groves, especially in semi-arid regions, focusing on the estimation of evapotranspiration, crop coefficients, and water use efficiency. This article provides a comprehensive overview of the current state of research on the use of satellite remote sensing for drought assessment in grapevines and olive trees, identifying trends, methodological approaches, and opportunities for future research in this field. [ABSTRACT FROM AUTHOR]

An algorithm for classifying crop areas using multi-frequency Synthetic Aperture Radar (SAR) and optical data is evaluated for the upcoming NASA ISRO SAR (NISAR) mission and its active crop area products. Two time-series of L-band ALOS-2 and C-band Sentinel-1A images over an agricultural region in the Southern United States are used as the input, as well as high-resolution Planet optical data. To overcome the delay by at least one year of existing landcover maps, training and validation sets of crop/non-crop polygons are derived with the contemporary Planet images. The classification results show that the 80% requirement on the NISAR science accuracy is achievable only with L-band HV input and with a resolution of 100 m. In comparison, HH polarized images do not meet this target. The spatial resolution is a key factor: 100 m is necessary to accomplish the 80% goal, while 10 m do not produce the desired accuracy. Unlike the previous study reporting that C-band performs better than L-band, we found otherwise in this study. This suggests that the performance likely depends on the site of interest and crop types. Alternative to the SAR images, the Normalized Difference Vegetation Index (NDVI) from the Planet data is not effective either as an input to the classification algorithm or as ground truth for training the algorithm. The reason is that NDVI becomes saturated and temporally static, thus rendering crop pixels to be misclassified as non-crop. [ABSTRACT FROM AUTHOR]

Remote sensing datasets offer robust approaches for gaining reliable insights into forest ecosystems. Despite numerous studies reviewing forest aboveground biomass estimation using remote sensing approaches, a comprehensive synthesis of synergetic integration methods to map and estimate forest AGB is still needed. This article reviews the integrated remote sensing approaches and discusses significant advances in estimating the AGB from space- and airborne sensors. This review covers the research articles published during 2015–2023 to ascertain recent developments. A total of 98 peer-reviewed journal articles were selected under the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Among the scrutinized studies, 54 were relevant to spaceborne, 22 to airborne, and 22 to space- and airborne datasets. Among the empirical models used, random forest regression model accounted for the most articles (32). The highest number of articles utilizing integrated dataset approaches originated from China (24), followed by the USA (15). Among the space- and airborne datasets, Sentinel-1 and 2, Landsat, GEDI, and Airborne LiDAR datasets were widely employed with parameters that encompassed tree height, canopy cover, and vegetation indices. The results of co-citation analysis were also determined to be relevant to the objectives of this review. This review focuses on dataset integration with empirical models and provides insights into the accuracy and reliability of studies on AGB estimation modeling. [ABSTRACT FROM AUTHOR]

Introduction Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level (Sanchez et al., 2008). The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods (Alizade and Kamali, 2007). Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations (Chihda et al., 2010). Examples of such algorithms include SEBAL (Bastiaanssen et al., 1998 Bastiaanssen, 2000;), METRIC (Allen et al., 2007), SEBS (Su, 2002). Among the above mentioned algorithms, energy billing algorithms have been used (Bagheriharooni et al., 2013; Teixeira et al., 2009). Among the factors of superiority of the SEBAL algorithm, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models (Bastiaanssen et al., 2002). Methodology In this research, 24 images from Landsat 8 satellite and 60 Sentinel 2 satellite images were used during growth period of sugarcane in 2016 and 2017, respectively (from May 28 to October 17 of each mentioned year). The study area is located in the Amir Kabir unit of Sugarcane Industry in the southern province of Khuzestan, one of the seven sugar cane cultivars and industries with local coordinates of 48 ° 16'49'E and 31 ° 2' 2'N. Amir Kabir Cultivation & Industry is located at Km 45 of Ahvaz - Khorramshahr Road, which is located south of Mirza Kuchak Khan's cultivation and is located in the east of Karoon's River. The total area of this farm was 15,000 hectares and its net area was 12,000 hectares, divided into several 25 hectares. The required meteorological information was extracted from the Amir Kabir Crop Production and Meteorological Station. This information includes: wind speed, sunshine hours, maximum and minimum temperatures, and rainfall. Results and Discussion The main objective of this study was to estimate the actual evapotranspiration of the sugarcane by using Landsat 8, Sentinel 2 and SEBAL algorithm. Finally, the results were compared with lysimetric data and analyzed. For statistical analysis of the results, the absolute difference indices and relative differences were used. In order to estimate evapotranspiration, as mentioned earlier, it was necessary to obtain the values of pure radiation and the heat flux for hot and cold pixels. The results are presented in Table 1, by separation of the evapotranspiration estimation method and the date of the images. Table (1) shows the potential evapotranspiration using the Taylor Presley method and the maximum values estimated by the SEBAL algorithm for Amir Kabir cultivation and industry. According to Table 1 the evapotranspiration rate calculated using the Taylor-Presley method is not significantly different from the evapotranspiration calculated by the SEBAL algorithm. In general, it can be stated that the method of using Landsat 8 and Sentinel 2 satellite images can calculate the amount of canopies evapotranspiration and transpiration with a small error value. the lysimeter gives the actual amount of evaporation and transpiration, by comparing the values of evapotranspiration calculated by the SEBAL algorithm and the values provided by the Lysimeter, the rate of error estimation indices represents less values. Therefore, according to Table (1), Sahbal algorithm is a suitable method for estimating the amount of evapotranspiration of cane sugar. ... The results showed that, despite the fact that the Sentinel 2 satellite does not have thermal bands, it is possible to calculate the actual evapotranspiration using the SEBAL algorithm by combining the satellite images with the Landsat satellite. Landsat satellite images are also challenging to estimate the water requirement, but according to the calculated indices, there is no significant difference with the lysimetric data. Compared to the combination of images, which have a precise accuracy in order to cover the time lag of Landsat 8 and Sentinel 2. Conclusions The SEBAL algorithm solves the energy balance equation to calculate the actual evapotranspiration of the plant, and the calculated parameters such as surface temperature, NDVI are defined in a certain range and are acceptable. The calculations showed that the results were consistent with the acceptable limits stated in the sources and statistics, and they confirmed this algorithm. Also, in comparison with the Taylor-Presley method, it was observed that SEBAL has calculated the actual evapotranspiration with acceptable results, which is also the reason for this method in calculating evapotranspiration. Due to errors in collecting climatic data such as wind speed, air temperature, solar radiation, day time duration, humidity, and also lack of calibration of coefficients such as coefficient of evaporation pan, plant coefficients for estimating evapotranspiration in many common error methods has it. In this regard, it is possible to consider new methods, such as the use of satellite imagery, to calculate the evapotranspiration of the plant for a wide range of plains as well as a point scale. It is obvious that by using suitable spatial resolution with homogeneity of the field farms and at appropriate time intervals, it is possible to plot the actual evapotranspiration of the plant for each region during the growing season. [ABSTRACT FROM AUTHOR]

Background: Guided by the data from the surveillance system, public health efforts have contributed to reducing the burden of influenza in many countries. During the COVID-19 pandemic, many surveillance resources were directed at tracking the severe acute respiratory syndrome-Coronavirus 2. However, most countries have not reported surveillance evaluations during the COVID-19 pandemic. Methods: Using the U.S. CDC surveillance evaluation method, we evaluated the influenza-like illness (ILI) sentinel surveillance performance in South Korea between January 2017 and September 2023. For the timeliness, we measured the mean time lag between the reports from the sentinel sites to the Korea Disease Control and Prevention Agency (KDCA) and surveillance result dissemination from KDCA. For the completeness, we measured the submission rate of complete reports per overall number of reports from each sentinel site to the KDCA. For the sensitivity, we calculated the correlation coefficient between the monthly number of ILI reports and the patients with ILI from the Korea national reimbursement data by either Pearson’s or Spearman’s test. For the representativeness, we compared the age-specific distribution of ILI between the surveillance data and the national reimbursement data using a chi-squared test. Results: We found that the surveillance performance of timeliness (less than 2 weeks) and completeness (97 %−98 %) was stable during the study period. However, we found a reduced surveillance sensitivity (correlation coefficient: 0.73 in 2020, and 0.84 in 2021) compared to that of 2017–2019 (0.96–0.99), and it recovered in 2022–2023 (0.93–0.97). We found no statistical difference across the proportion of age groups between the surveillance and reimbursement data during the study period (all P-values > 0.05). Conclusions: Ongoing surveillance performance monitoring is necessary to maintain efficient policy decision-making for the control of the influenza epidemic. Additional research is needed to assess the overall influenza surveillance system including laboratory and hospital-based surveillance in the country.

This study analysed the changes in land use and land cover (LULC) in East Java Province by comparing two LULC maps interpreted from optical imagery. The images captured from 2015 to 2017 were selected to represent the initial LULC maps. Then, the images collected from 2020 to 2021 were considered the recent LULC maps. The input imagery was prepared using the Google Earth engine (GEE). The Random Forest algorithm was used for classification. In this study, eight significant LULC classes were categorised, i.e., built-up area (BU), heterogeneous-agricultural land (HAL), bare soil (BS), paddy field (PF), open water (OW), vegetation (VG), shrubland (SH), and wetland (WL). Next, the training samples were interpreted from Google Earth Pro. Then, the GEE satellite base map and the ground control points (GCPs) were collected. The collected GCPs were split into 70% training and 30% validation data. The results showed that significant LULC Change was more marked in the most urbanised areas (in and around the big cities), followed by LULC change in and around medium towns and rural areas. Four classes experienced an area increase, i.e., BU (+30.23%), HAL (+30.77%), BS (+24.52%), and PF (+14.36%). As a consequence, the other four classes compensated for the increase, i.e., OW (−32.79%), VG (−25.72%), SH (−6.59%), and WL (−25.53%). Regional development from 2015 to 2021 has increased built-up areas. Conversely, the development has reduced OW, VG, SH, and WL. The LULC changes have significantly changed the natural landscape to a human-dominated one.

West Nile virus (WNV) was first detected in the Netherlands in 2020, with circulation observed in birds, mosquitoes, and humans in two geographical areas. Usutu virus (USUV) has been circulating in the Netherlands since 2016. Following the detection of WNV in the Netherlands, we investigated the possible use of petting zoos as urban sentinel sites to examine the extent of WNV and USUV circulation around the two WNV outbreak locations. Chickens at petting zoos and in backyards were sampled within a 15-kilometer radius of the confirmed WNV circulation areas at three timepoints over one year (2021–2022). Sera were analysed using a protein microarray for binding antibodies to orthoflavivirus NS1 antigens and reactive samples were confirmed through micro-focus reduction neutralization tests (mFRNT). Furthermore, mosquitoes at sampling locations were collected to assess their blood feeding behaviour. This serosurvey detected the circulation of USUV and WNV in petting zoo and backyard chickens in 2021, both within and outside the 2020 outbreak areas. The WNV circulation was not detected by other existing surveillance schemes in mosquitoes, wild birds, horses and humans. In addition, the results show rapid decay of USUV antibodies in approximately 20 weeks. Our findings support the utility and the added value of petting zoo chickens as sentinels for monitoring USUV and WNV circulation compared to other available methods. Seroconversions observed in petting zoos and backyard chickens living in or near densely populated urban areas further highlighted potential public health risks that went undetected.

Marine mammals play a critical role as sentinels for tracking the spread of zoonotic diseases, with viruses being the primary causative factor behind infectious disease induced mortality events. A systematic review was conducted to document marine mammal mortality events attributed to zoonotic viral infections in published literature across the globe. This rigorous search strategy yielded 2883 studies with 88 meeting inclusion criteria. The studies spanned from 1989 to 2023, with a peak in publications observed in 2020. Most of the included studies were retrospective, providing valuable insights into historical trends. The United States (U.S.) reported the highest number of mortality events followed by Spain, Italy, Brazil and the United Kingdom. Harbor seals were the most impacted species, particularly in regions like Anholt, Denmark and the New England Coast, U.S. Analysis revealed six main viruses responsible for mortality events, with Morbillivirus causing the highest proportion of deaths. Notably, the occurrence of these viral events varied geographically, with distinct patterns observed in different regions. Immunohistochemistry emerged as the most employed detection method. This study underscores the importance of global surveillance efforts in understanding and mitigating the impact of viral infections on marine mammal populations, thereby emphasizing the necessity of collaborative One Health approaches to address emerging threats at the human-animal-environment interface. Additionally, the potential transfer of zoonotic viruses to aquatic organisms used in food production, such as fish and shellfish, highlights the broader implications for food safety, food security and public health.

Precise estimation of irrigated areas is essential for effective water management, increased production, environmental conservation, and conflict resolution. Nonetheless, discrepancies frequently exist between estimated and actual irrigated areas. To address the data gaps in actual irrigation areas within Ethiopia, we utilized high-resolution remote sensing imagery. However, the accuracy of these images under varying climatic and landscape conditions was not fully substantiated. We conducted a comparative analysis between global irrigation map and local irrigated region maps within two distinct watersheds. Field data was gathered to both train and assess by employing random forest supervised classification algorithm. This algorithm was then applied to create accurate irrigation maps using high-resolution Sentinel-1 data for the Bilate and Gumara watersheds. During the irrigation seasons, maps of irrigated regions were produced using time-series imagery. Additionally, we employed maps indicating lands suitable for surface irrigation and applied post-processing techniques to refine the actual irrigated areas. The resulting accuracy was comparably high for both watersheds, with values of 88% and 87%. The kappa coefficients were 0.74 and 0.73, respectively, indicating a very good level of agreement. However, there were significant discrepancies between the global irrigation map and the local irrigated regions map in terms of spatial distribution and the extent of irrigation. This discrepancy necessitates further analysis of both products to decipher the underlying causes of their differences. We recommend for additional studies encompassing diverse watershed characteristics to improve irrigation area mapping via remote sensing. Our findings also validate the effectiveness of post-processing techniques in remote sensing applications.

This article focuses on developing models that estimate suspended sediment concentrations (SSCs) for the Lower Brazos River, Texas, U.S. Historical samples of SSCs from gauge stations and satellite imagery from Landsat Missions and Sentinel Mission 2 were utilized to develop models to estimate SSCs for the Lower Brazos River. The models used in this study to accomplish this goal include support vector machines (SVMs), artificial neural networks (ANNs), extreme learning machines (ELMs), and exponential relationships. In addition, flow measurements were used to develop rating curves to estimate SSCs for the Brazos River as a baseline comparison of the models that used satellite imagery to estimate SSCs. The models were evaluated using a Taylor Diagram analysis on the test data set developed for the Brazos River data. Fifteen of the models developed using satellite imagery as inputs performed with a coefficient of determination R2 above 0.69, with the three best performing models having an R2 of 0.83 to 0.85. One of the best performing models was then utilized to estimate the SSCs before, during, and after Hurricane Harvey to evaluate the impact of this storm on the sediment dynamics along the Lower Brazos River and the model's ability to estimate SSCs. [ABSTRACT FROM AUTHOR]

To create the landslide activity map, we implemented and tested the procedure to fully utilise the 6-day repeatability of the Sentinel-1 constellation in three pilot areas in Slovenia for the observation period from 2017 to 2021. The interferometric processing of the Sentinel-1 images was carried out with ENVI SARScape, while the interpretation of the persistent scatterers InSAR data was done in three steps. In the first step, a preliminary interpretation of the landslide areas was performed by integrating the PS InSAR data into a GIS environment with information that could be relevant to explain the movement patterns of the PS InSAR points. In the second step, a field validation was performed to check the PS InSAR in the field and record the potential damage to the objects indicating the slope mass movements. In the third step, the deformations were identified, and areas of significant movement were determined, consisting of clusters of at least 3 persistent scatterers (PS) with a maximum spacing of 10 m. The landslide activity map was created based on the landslide areas categorised into four classes based on the geotechnical analyses, yearly velocity data obtained by PS InSAR, and validation of annual velocity data obtained by in-situ and GNSS monitoring and field observation. A total of 21 polygons with different landslide activities were identified in three study areas. The overall methodology will help stakeholders in the early mapping and monitoring of landslides to increase the urban resilience. [ABSTRACT FROM AUTHOR]

Reliable and up-to-date training reference samples are imperative for land cover (LC) classification. However, such training datasets are not always available in practice. The sample migration method has shown remarkable success in addressing this challenge in recent years. This work investigated the application of Sentinel-1 (S1) and Sentinel-2 (S2) data in training sample migration. In addition, the impact of various spectral bands and polarizations on the accuracy of the migrated training samples was also assessed. Subsequently, combined S1 and S2 images were classified using the Support Vector Machines (SVM) and Random Forest (RF) classifiers to produce annual LC maps from 2017 to 2021. The results showed a higher accuracy (98.25%) in training sample migrations using both images in comparison to using S1 (87.68%) and S2 (96.82%) data independently. Among the LC classes, the highest accuracy in migrated training samples was found for water, built-up, bare land, grassland, cropland, and wetland. Inquiries on the efficiency of different spectral bands and polarization used in training sample migration showed that bands 4 and 8 and VV polarization in the water class were more important, while for the wetland class, bands 5, 6, 7, 8, and 8A together with VV polarization showed superior performance. The results showed that the RF classifier provided better performance than the SVM (higher overall, producer, and user accuracy). Overall, our findings suggested that shared use of S1 and S2 data can be used as a suitable means for producing up-to-date and high-quality training samples. [ABSTRACT FROM AUTHOR]

Background: The emergence of SARS‐CoV‐2 in late 2019 saw the implementation of public health and social measures (PHSM) by countries across Europe to reduce its transmission and impact on populations. Consequently, countries reported changes in influenza circulation and extensive disruptions to routine surveillance systems. Methods: We describe the epidemiology of influenza in Europe between Weeks 40/2020 and 39/2022 compared to the 2016/17 to 2019/20 seasons, to assess the impact of the COVID‐19 pandemic and PHSM on surveillance systems and influenza circulation. Results: Low detections of influenza were observed through primary care sentinel sources during seasonal influenza periods (Week 40 to 20); 56 (of 39,457 specimens tested; < 1% positivity) in 2020/21 and 7261 (of 64,153 specimens tested; 11% positivity) detections in 2021/22 were observed, compared to an average of 18,383 (of 50,544 specimens tested; 36% positivity) detections in 2016/17 to 2019/20. Similarly, 11 (of 19,989 specimens tested; < 1% positivity) and 1488 (of 23,636 specimens tested; 6% positivity) detections were reported through SARI surveillance sources in 2020/21 and 2021/22, respectively, compared to an average of 2850 (of 10,389 specimens tested; 27% positivity) detections in 2016/17 to 2019/20. However, the 2021/22 interseasonal period saw unusual increases in influenza detections across surveillance site types when PHSM were easing. Conclusion: In conclusion, findings suggest that the restriction and easing of PHSM measures were associated with variations in influenza detections. Our observations of out‐of‐season influenza activity highlight the importance of an integrated respiratory surveillance strategy to monitor circulating respiratory viruses throughout the year to inform optimal prevention and control strategies. [ABSTRACT FROM AUTHOR]

Background: Understanding the factors influencing nodal status in breast cancer is vital for axillary staging, therapy, and patient survival. The nodal stage remains a crucial factor in prognostication indices. This study investigates the relationship between tumour-to-skin distance (in T1–T3 tumours where the skin is not clinically involved) and the risk of nodal metastasis. Methods: We retrospectively reviewed data from 100 patients who underwent neoadjuvant chemotherapy (NACT). Besides patient demographics and tumour variables, a radiologist retrospectively reviewed pre-operative MRI to measure tumour-to-skin distance. R core packages were used for univariate (χ2 and T-Wilcoxon tests) and bivariate logistic regression statistical analysis. Results: Of 95 analysable datasets, patients' median age was 51 years (IQR: 42–61), 97% were symptomatic (rest screen detected), and the median tumour size was 43 mm (IQR, 26–52). On multivariate analysis, increasing invasive tumour size (p = 0.02), ER positivity (p = 0.007) and shorter tumour-to-skin distance (p = 0.05) correlated with nodal metastasis. HER2 was not included in multivariate analysis as there was no association with nodal status on univariate analysis. In node-positive tumours, as tumour size increased, the tumour-to-skin distance decreased (r = − 0.34, p = 0.026). In node-negative tumours, there was no correlation (r = + 0.18, p = 0.23). Conclusion: This study shows that non-locally advanced cancers closer to the skin (and consequent proximity to subdermal lymphatics) are associated with a greater risk of nodal metastasis. Pre-operative identification of those more likely to be node positive may suggest the need for a second-look USS since a higher nodal stage may lead to a change in therapeutic strategies, such as upfront systemic therapy, node marking, and axillary clearance without the need to return to theatre following sentinel node biopsy. [ABSTRACT FROM AUTHOR]

The Norway spruce is one of the most important tree species in Europe. This tree species has been put under considerable pressure due to the ongoing impacts of climate change. Meanwhile, frequent droughts and pest outbreaks are reported as the main reason for its dieback, resulting in severe forest cover loss. Such was the case with Norway spruce forests within the Kopaonik National Park (NP) in Serbia. This study aims to quantify, spatially and temporally, forest cover loss and to evaluate the sensitivity of various vegetation indices (VIs) in detecting drought-induced response and predicting the dieback of Norway spruce due to long-lasting drought effects in the Kopaonik NP. For this purpose, we downloaded and processed a large number of Landsat 7 (ETM+), Landsat 8 (OLI), and Sentinel 2 (MSI) satellite imagery acquired from 2009 to 2022. Our results revealed that forest cover loss was mainly driven by severe drought in 2011 and 2012, which was later significantly influenced by bark beetle outbreaks. Furthermore, various VIs proved to be very useful in monitoring and predicting forest health status. In summary, the drought-induced response detected using various VIs provides valuable insights into the dynamics of forest cover change, with implications for monitoring and conservation efforts of Norway spruce forests in the Kopaonik NP. [ABSTRACT FROM AUTHOR]

Acute ischemic stroke remains a serious complication of transcatheter aortic valve replacement (TAVR). Cerebral embolic protection devices (CEPD) were developed to mitigate the risk of acute ischemic stroke complicating TAVR (AISCT). However, the existing body of evidence does not clearly support CEPD efficacy in AISCT prevention. In a cohort of patients with AISCT, we aimed to compare the characteristics and outcomes of patients who have had unprotected TAVR (CEPD−) vs CEPD-protected TAVR (CEPD+). Data were derived from an international multicenter registry focusing on AISCT. We included all patients who experienced ischemic stroke within 72 hours of TAVR. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS). Primary outcomes were neurologic disability status according to the modified Rankin Score at 30 days, and 6-month all-cause death. Propensity score matched analysis was used to control for differences between groups. In 18,725 TAVR procedures, 416 AISCT (2.2%) within 72 hours were documented, of which 376 were in the CEPD− TAVR group and 40 in the CEPD+ TAVR group. Although the middle cerebral artery stroke rate was similar in both groups (29.7% CEPD− vs 33.3% CEPD+; P = 0.71), AISCT in the CEPD+ group was characterized by a lower rate of internal carotid artery occlusion (0% vs 4.7%) and higher rate of vertebrobasilar system strokes (15.4% vs 5.7%; P = 0.04). AISCT was severe (NIHSS ≥15) in 21.6% CEPD− and 23.3% CEPD+ AISCT (P = 0.20). Disabling stroke rates (modified Rankin Score >1 at 30 days) were 47.3% vs 42.5% (P = 0.62), and 6-month mortality was 31.3% vs 23.3% (P = 0.61), in the CEPD− and CEPD+ groups, respectively. In the propensity score matched cohort, disabling stroke rates were 56.5% vs 41.6% (P = 0.16), and 6-month mortality was 33% vs 19.5% (P = 0.35), in the CEPD− and CEPD+ groups, respectively. In a large cohort of patients with AISCT, the use of CEPD had little effect on stroke distribution, severity, and outcomes. [Display omitted] [ABSTRACT FROM AUTHOR]

Wetlands are amongst Earth's most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination ( R 2 ) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies. [ABSTRACT FROM AUTHOR]