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Schirdewahn, Frederik, Hartmut H. K. Lentz, Vittoria Colizza, Andreas Koher, Philipp Hövel et Beatriz Vidondo. « Early warning of infectious disease outbreaks on cattle-transport networks ». PLOS ONE 16, no 1 (6 janvier 2021) : e0244999. http://dx.doi.org/10.1371/journal.pone.0244999.
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Surveillance of infectious diseases in livestock is traditionally carried out at the farms, which are the typical units of epidemiological investigations and interventions. In Central and Western Europe, high-quality, long-term time series of animal transports have become available and this opens the possibility to new approaches like sentinel surveillance. By comparing a sentinel surveillance scheme based on markets to one based on farms, the primary aim of this paper is to identify the smallest set of sentinel holdings that would reliably and timely detect emergent disease outbreaks in Swiss cattle. Using a data-driven approach, we simulate the spread of infectious diseases according to the reported or available daily cattle transport data in Switzerland over a four year period. Investigating the efficiency of surveillance at either market or farm level, we find that the most efficient early warning surveillance system [the smallest set of sentinels that timely and reliably detect outbreaks (small outbreaks at detection, short detection delays)] would be based on the former, rather than the latter. We show that a detection probability of 86% can be achieved by monitoring all 137 markets in the network. Additional 250 farm sentinels—selected according to their risk—need to be placed under surveillance so that the probability of first hitting one of these farm sentinels is at least as high as the probability of first hitting a market. Combining all markets and 1000 farms with highest risk of infection, these two levels together will lead to a detection probability of 99%. We conclude that the design of animal surveillance systems greatly benefits from the use of the existing abundant and detailed animal transport data especially in the case of highly dynamic cattle transport networks. Sentinel surveillance approaches can be tailored to complement existing farm risk-based and syndromic surveillance approaches.
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Račič, M., K. Oštir, D. Peressutti, A. Zupanc et L. Čehovin Zajc. « APPLICATION OF TEMPORAL CONVOLUTIONAL NEURAL NETWORK FOR THE CLASSIFICATION OF CROPS ON SENTINEL-2 TIME SERIES ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (14 août 2020) : 1337–42. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1337-2020.
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Abstract. The recent development of Earth observation systems – like the Copernicus Sentinels – has provided access to satellite data with high spatial and temporal resolution. This is a key component for the accurate monitoring of state and changes in land use and land cover. In this research, the crops classification was performed by implementing two deep neural networks based on structured data. Despite the wide availability of optical satellite imagery, such as Landsat and Sentinel-2, the limitations of high quality tagged data make the training of machine learning methods very difficult. For this purpose, we have created and labeled a dataset of the crops in Slovenia for the year 2017. With the selected methods we are able to correctly classify 87% of all cultures. Similar studies have already been carried out in the past, but are limited to smaller regions or a smaller number of crop types.
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Tarpanelli, Angelica, Alessandro C. Mondini et Stefania Camici. « Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe ». Natural Hazards and Earth System Sciences 22, no 8 (2 août 2022) : 2473–89. http://dx.doi.org/10.5194/nhess-22-2473-2022.
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Abstract. Inundation is one of the major natural hazards in Europe. The evaluation of the flood hazard and risk is not straightforward mainly due to the monitoring system that is poor or not uniformly distributed in the territory. The ESA Earth Observation Program, including a series of satellites, Sentinels, for the operative observation of the natural phenomenon, e.g. the inundations, can potentially reduce the gap. Sentinel-1 (SAR: synthetic aperture radar) and Sentinel-2 (optical) have been demonstrated to be suitable for mapping flooded areas, but despite the medium–high spatial and temporal resolution of the sensors, the mapping of inundated territories is often partial or missing. The objective of this study is to evaluate through a synthetic study the effectiveness of Sentinel-1 and Sentinel-2 in the systematic assessment of floods in Europe, where the flood events have durations ranging from some hours to a few days. To reach the target, we analysed 10 years of river discharge data over almost 2000 sites in Europe, and we extracted flood events over some established thresholds as proxies of riverine inundations. Based on the revisit time of the satellite constellations and cloud coverage, we derived the percentage of potential inundation events that Sentinel-1 and Sentinel-2 could be able to observe. Results show that assuming the configuration of a constellation of two satellites for each mission and considering the ascending and descending orbit, on average 58 % of flood events are potentially observable by Sentinel-1 and only 28 % by Sentinel-2 due to the cloud coverage.
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Kuntla, Sai Kiran. « An era of Sentinels in flood management : Potential of Sentinel-1, -2, and -3 satellites for effective flood management ». Open Geosciences 13, no 1 (1 janvier 2021) : 1616–42. http://dx.doi.org/10.1515/geo-2020-0325.
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Abstract The repetitive and destructive nature of floods across the globe causes significant economic damage, loss of human lives, and leaves the people living in flood-prone areas with fear and insecurity. With enough literature projecting an increase in flood frequency, severity, and magnitude in the future, there is a clear need for effective flood management strategies and timely implementation. The earth observatory satellites of the European Space Agency’s Sentinel series, Sentinel-1, Sentinel-2, and Sentinel-3, have a great potential to combat these disastrous floods by their peerless surveillance capabilities that could assist in various phases of flood management. In this article, the technical specifications and operations of the microwave synthetic aperture radar (SAR) onboard Sentinel-1, optical sensors onboard Sentinel-2 (Multispectral Instrument) and Sentinel-3 (Ocean and Land Color Instrument), and SAR altimeter onboard Sentinel-3 are described. Moreover, the observational capabilities of these three satellites and how these observations can meet the needs of researchers and flood disaster managers are discussed in detail. Furthermore, we reviewed how these satellites carrying a range of technologies that provide a broad spectrum of earth observations stand out among their predecessors and have bought a step-change in flood monitoring, understanding, and management to mitigate their adverse effects. Finally, the study is concluded by highlighting the revolution this fleet of Sentinel satellites has brought in the flood management studies and applications.
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Winder, Monika, et James E. Cloern. « The annual cycles of phytoplankton biomass ». Philosophical Transactions of the Royal Society B : Biological Sciences 365, no 1555 (12 octobre 2010) : 3215–26. http://dx.doi.org/10.1098/rstb.2010.0125.
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Terrestrial plants are powerful climate sentinels because their annual cycles of growth, reproduction and senescence are finely tuned to the annual climate cycle having a period of one year. Consistency in the seasonal phasing of terrestrial plant activity provides a relatively low-noise background from which phenological shifts can be detected and attributed to climate change. Here, we ask whether phytoplankton biomass also fluctuates over a consistent annual cycle in lake, estuarine–coastal and ocean ecosystems and whether there is a characteristic phenology of phytoplankton as a consistent phase and amplitude of variability. We compiled 125 time series of phytoplankton biomass (chlorophyll a concentration) from temperate and subtropical zones and used wavelet analysis to extract their dominant periods of variability and the recurrence strength at those periods. Fewer than half (48%) of the series had a dominant 12-month period of variability, commonly expressed as the canonical spring-bloom pattern. About 20 per cent had a dominant six-month period of variability, commonly expressed as the spring and autumn or winter and summer blooms of temperate lakes and oceans. These annual patterns varied in recurrence strength across sites, and did not persist over the full series duration at some sites. About a third of the series had no component of variability at either the six- or 12-month period, reflecting a series of irregular pulses of biomass. These findings show that there is high variability of annual phytoplankton cycles across ecosystems, and that climate-driven annual cycles can be obscured by other drivers of population variability, including human disturbance, aperiodic weather events and strong trophic coupling between phytoplankton and their consumers. Regulation of phytoplankton biomass by multiple processes operating at multiple time scales adds complexity to the challenge of detecting climate-driven trends in aquatic ecosystems where the noise to signal ratio is high.
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Crescio, Maria Ines, Giuseppe Ru, Luca Aresu, Elena Bozzetta, Maria Giovanna Cancedda, Katia Capello, Massimo Castagnaro et al. « The Italian Network of Laboratories for Veterinary Oncology (NILOV) 2.0 : Improving Knowledge on Canine Tumours ». Veterinary Sciences 9, no 8 (30 juillet 2022) : 394. http://dx.doi.org/10.3390/vetsci9080394.
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Advances in tumour research are crucial, and comparative oncology can improve the knowledge in several ways. Dogs are not only models of specific naturally occurring tumours but can also be sentinels of environmental exposures to carcinogens, as they share the same environment with their owners. The purpose of this work was to describe the data collected by The Italian Network of Laboratories for Veterinary Oncology in the first 9 years of activity (2013–2021) and to evaluate their potential epidemiological significance. Frequencies of tumour topographies and main morphologies in dogs were described, analysed and compared, calculating age-adjusted proportional morbidity ratios and considering several risk factors (breed, sex, period and region of residence). These observations allowed us to highlight differences not only in morphology and topography of some tumours but also to formulate hypotheses on the potential role of some risk factors, e.g., neutering/spaying or geographical location. In our opinion, the results of this case series confirm the importance of initiating and consolidating animal cancer registration initiatives that would facilitate the possibility of conducting multicentric collaborative studies to deepen the knowledge of the epidemiology of tumours in dogs from a comparative perspective.
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Bañón, Manuel, Ana Justel, David Velázquez et Antonio Quesada. « Regional weather survey on Byers Peninsula, Livingston Island, South Shetland Islands, Antarctica ». Antarctic Science 25, no 2 (20 mars 2013) : 146–56. http://dx.doi.org/10.1017/s0954102012001046.
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AbstractIn 2001 the LIMNOPOLAR Project was launched with the aim of addressing the suitability of freshwater ecosystems as useful sentinels of climate change. In this project, an automatic weather station was deployed on Byers Peninsula (Livingston Island, South Shetland Islands) near several freshwater ecosystems under research. Here the multi-year data recorded are presented and compared with meteorological time series from the observatories at the Spanish Juan Carlos I Station, Deception Island and Bellingshausen Station. Lake freezing and thawing periods and snow cover are also investigated. The main results indicate that Byers Peninsula is affected by the very cloudy and wet Antarctic maritime climate. Mean annual temperature is -2.8°C and summer mean temperatures are above freezing. The region shows moderate winds over the year and with moderate, mostly liquid precipitation during the summer. There is a significant linear relationship with meteorological records obtained from Juan Carlos I Station located on the east of Livingston Island. Correlations between meteorological data from both sites are high but with colder and much windier conditions on Byers Peninsula. Therefore, the usefulness and accuracy of meteorological records in the interpretation of ecosystem dynamics are presented.
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Papa, Rey Donne, et Jonathan Carlo Briones. « Climate and Human-induced Changes to Lake Ecosystems : What We Can Learn From Monitoring Zooplankton Ecology ». Journal of Environmental Science and Management 17, no 1 (30 juin 2014) : 60–67. http://dx.doi.org/10.47125/jesam/2014_1/07.
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Long-term time-series data have been proven useful in analyzing the adaptability of zooplankton communities as a response to environmental change. The unique life history and importance of zooplankton in aquatic ecosystems, coupled with the capability of lakes to integrate changes in the surrounding watershed, has given each the recognition as “beacons and sentinels of climate change,” respectively. Aside from this, many lakes have undergone pollution through human-induced eutrophication attributed to extensive lake-shore town development, agricultural waste runoffs, and intensive aquaculture. Implementation of holistic lake management plans in many countries has resulted to the rehabilitation and even reversal of lake eutrophication, and this is, in part, due to regular monitoring and careful analysis of temporal zooplankton community data that came with implemented rehabilitation efforts. As such, monitoring lake zooplankton populations may give us clues as to how changes in the environment, either from human or climate induced changes have already affected lake ecosystems. It is unfortunate however, that such analysis is presently not available in our country due to lack of routine zooplankton monitoring programs. The paper reviewed several successfully implemented lake/zooplankton monitoring programs, highlighted their strong points. The researchers also suggest integrative feasible concepts that are applicable to the country.
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Lunt, R. A., L. Melville, N. Hunt, S. Davis, C. L. Rootes, K. M. Newberry, L. I. Pritchard et al. « Cultured skin fibroblast cells derived from bluetongue virus-inoculated sheep and field-infected cattle are not a source of late and protracted recoverable virus ». Journal of General Virology 87, no 12 (1 décembre 2006) : 3661–66. http://dx.doi.org/10.1099/vir.0.81653-0.
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A recent hypothesis to explain the recurrence of bluetongue disease after winter seasonal absences of the vector has suggested a role for persistent infection of sheep. This report presents combined independent work from two laboratories investigating the possible recovery of Bluetongue virus (BTV) over a protracted period after infection of both sheep and cattle. Prior to infection with either cell-culture-adapted or non-culture-adapted BTV, sheep were subjected to a preliminary exposure to Culicoides sp. insects, which reportedly facilitates recovery of virus from infected sheep several months post-infection (p.i.). A series of skin biopsies at different intervals p.i. was used to establish skin fibroblast (SF) cultures from which attempts were made to detect virus by isolation and by molecular and immunological methods. Also examined was the effect on virus recovery of additional exposure to Culicoides sp. prior to skin biopsy during the post-inoculation period. A herd of cattle sentinels for surveillance of natural BTV infection in northern Australia was monitored prospectively for seroconversion. Evidence of infection initiated attempted virus recovery by establishing SF cultures. It was found that in both cattle and sheep there was not a protracted period over which BTV could be recovered from SF cultures. The data do not support a general hypothesis that BTV persists in either sheep or cattle.
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Bonisoli-Alquati, Andrea. « Avian genetic ecotoxicology : DNA of the canary in a coalmine ». Current Zoology 60, no 2 (1 avril 2014) : 285–98. http://dx.doi.org/10.1093/czoolo/60.2.285.
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Abstract Genotoxic chemicals, through damage and alteration of the genetic material of wild organisms, pose significant threats to the persistence of wild animal populations. Their damaging effects can ultimately impair the health of the ecosystem and its provision of services to human society. Bird species are good candidates for the role of sentinels of the effects of genotox-ins, thanks to (i) the diversity of their ecological niches, (ii) their ubiquity across environments, (iii) their conspicuousness, abundance and approachability, together with (iv) their well-known life histories and the availability of historical data series. Avian diversity increases the likelihood that adequate model species be available for monitoring genotoxicants and assessing their impact. This paper reviews the methods utilized by genetic ecotoxicological studies of wild birds, highlighting their benefits and shortcomings. It also summarizes the genetic ecotoxicological studies so far conducted. In spite of a paucity of studies, several classes of genotoxicants have already been investigated across a variety of species and environments, thus supporting the versatility of birds as monitors of genotoxic contamination. Future technical advancements and applications are suggested, with particular reference to the analysis of mutational events, gene expression and methylation patterns. Finally, I argue that the development of avian genetic ecotoxicology will contribute to the understanding of natural variation in the underlying machinery for coping with DNA damage and oxidative stress, both of which are increasingly recognized as proximate factors in the evolution of life history adaptations.
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Peng, Yuzhuo, Anmin Duan, Wenting Hu, Bin Tang, Xinyu Li et Xianyi Yang. « Observational constraint on the future projection of temperature in winter over the Tibetan Plateau in CMIP6 models ». Environmental Research Letters 17, no 3 (24 février 2022) : 034023. http://dx.doi.org/10.1088/1748-9326/ac541c.
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Abstract The Tibetan Plateau (TP) is known as one of the sentinels of global climate change. Substantial winter warming over the TP will likely lead, directly or indirectly, to a series of geological disasters such as snow and glacial avalanches. Hence, for better adaptation to climate change, it is vital to project the future change in winter temperature over the TP. However, the current state-of-the-art climate models involved in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) still produce strong cold biases over most parts of the TP in their historical simulations. On the basis of selecting the optimal models, here we use the statistical downscaling method to constrain the projected winter temperature in CMIP6 models. The results show that the regions with the strongest winter warming over the TP will be near the Himalayas and the densely populated eastern regions. The constrained warming magnitude is much greater than that in the ensemble mean of the original 32 CMIP6 models or six best models over these regions. Therefore, early warning and forecasting services should be strengthened for the future temperature over these regions. Moreover, the long-term spatial warming varies greatly under four different future emission scenarios. Under the most severe scenario, the increase in winter temperature near the Himalayas exceeds 10 °C, which will greatly destabilize glaciers in the region, while the increase is only 4 °C–6 °C under the weakest scenario. Therefore, it is urgent to reduce greenhouse gas emissions to control the future temperature increase at hotspots of climate vulnerability such as the TP.
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Morán, Xosé Anxelu G., Laura Alonso-Sáez, Enrique Nogueira, Hugh W. Ducklow, Natalia González, Ángel López-Urrutia, Laura Díaz-Pérez, Alejandra Calvo-Díaz, Nestor Arandia-Gorostidi et Tamara M. Huete-Stauffer. « More, smaller bacteria in response to ocean's warming ? » Proceedings of the Royal Society B : Biological Sciences 282, no 1810 (7 juillet 2015) : 20150371. http://dx.doi.org/10.1098/rspb.2015.0371.
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Heterotrophic bacteria play a major role in organic matter cycling in the ocean. Although the high abundances and relatively fast growth rates of coastal surface bacterioplankton make them suitable sentinels of global change, past analyses have largely overlooked this functional group. Here, time series analysis of a decade of monthly observations in temperate Atlantic coastal waters revealed strong seasonal patterns in the abundance, size and biomass of the ubiquitous flow-cytometric groups of low (LNA) and high nucleic acid (HNA) content bacteria. Over this relatively short period, we also found that bacterioplankton cells were significantly smaller, a trend that is consistent with the hypothesized temperature-driven decrease in body size. Although decadal cell shrinking was observed for both groups, it was only LNA cells that were strongly coherent, with ecological theories linking temperature, abundance and individual size on both the seasonal and interannual scale. We explain this finding because, relative to their HNA counterparts, marine LNA bacteria are less diverse, dominated by members of the SAR11 clade. Temperature manipulation experiments in 2012 confirmed a direct effect of warming on bacterial size. Concurrent with rising temperatures in spring, significant decadal trends of increasing standing stocks (3% per year) accompanied by decreasing mean cell size (−1% per year) suggest a major shift in community structure, with a larger contribution of LNA bacteria to total biomass. The increasing prevalence of these typically oligotrophic taxa may severely impact marine food webs and carbon fluxes by an overall decrease in the efficiency of the biological pump.
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Mattingly, Cameron, Jennifer L. Elliott, Jeronay K. Thomas, Jenna L. Lobby, Sarah E. Michalets et Jacob E. Kohlmeier. « Examining effector functions of lung CD8+ tissue resident memory T cells in humans. » Journal of Immunology 208, no 1_Supplement (1 mai 2022) : 182.23. http://dx.doi.org/10.4049/jimmunol.208.supp.182.23.
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Abstract Due to their position in the lung tissue, CD8+ tissue resident memory T cells (TRM) act as sentinels of the respiratory tract that rapidly respond to, and mediate protection against, respiratory viruses. In mice, TRM have been shown to mediate protection at barrier sites by producing cytokines, chemokines, and performing cell lysis. In the lungs specifically, our lab has shown that influenza-specific CD8+ TRM rapidly produce IFNγ, but airway TRM are poorly cytolytic in mice. In humans, less is known about the effector functions of virus-specific CD8+ TRM in the lungs, and thus this study seeks to fill that gap in knowledge. Using cells from healthy human lungs, we first identified and quantified the frequency of antigen-specific cells in our lung donors by performing intracellular cytokine staining (IFNγ+) and activation induced marker assays (CD137+ CD25+). Then, by performing a series of in vitro peptide stimulation and cytokine neutralization experiments, we investigated which cytokines are produced by lung CD8+ TRM, and how those cytokines impact local innate and epithelial cells. Initial results show that, when stimulated with their cognate antigen, lung CD8+ TRM produce cytokines that directly activate innate immune cells. Results of this study suggests that human CD8+ TRM in the lungs act to rapidly reprogram local immune cells, and these data will ultimately help us understand how CD8+ TRM fit into the overall immune response to respiratory viruses. Supported by grants from the NIH/NHLBI (R35 HL150803) and by NIH/NIAD through the Centers of Excellence for Influenza Research and Response (75N93019R00028).
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Cantonati, Marco, Kurt Lichtenwöhrer, Gabi Leonhardt, Linda Seifert, Andrea Mustoni, Ralf Hotzy, Eva Schubert et al. « Using Springs as Sentinels of Climate Change in Nature Parks North and South of the Alps : A Critical Evaluation of Methodological Aspects and Recommendations for Long-Term Monitoring ». Water 14, no 18 (12 septembre 2022) : 2843. http://dx.doi.org/10.3390/w14182843.
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Spring ecosystems are diverse transition zones between ground- and surface-water habitats. Due to their characteristics and vulnerable species assemblages, springs are considered indicator systems for monitoring environmental change. In particular, climate change is expected to alter spring-ecosystem features, such as water temperature and discharge, affecting otherwise typically stable biotic and abiotic conditions. However, reliable trend-development recognition and analysis require a uniform methodology and comparable data series over long periods of time. Spring research findings in the Berchtesgaden National Park and the Adamello-Brenta Nature Park have been consolidated to develop methodological recommendations to create lasting societal-added value. The successful transfer of the methodology to the Bavarian Forest National Park and the experienced contribution of the Bavarian Association for the Protection of Nature (Bavarian Climate Alliance) strongly improved method validations. Our resulting, newly developed recommendations for long-term spring monitoring have a focus on climate change impacts and aim at providing a decision-making basis for establishing programs in similar ecological and climatic zones. Uniform site-selection criteria and selected climate-sensitive parameters are indicated. This includes documenting the spring’s environment and structure, measuring abiotic parameters, and determining selected floristic and faunistic groups. We recommend measurement and sampling-survey intervals ranging from 3(4) times yearly to every 5 years, depending on the parameter. We further suggest a database system that integrates all monitoring parameters to ensure consistent data management and storage. Analysing the data resulting from our new holistic spring monitoring methodology should provide critical knowledge about putatively changing ecosystems that can then be used as evidence of climate-change impact on spring ecosystems.
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Lani-Louzada, Rafael, Carolina do Val Ferreira Ramos, Ricardo Mello Cordeiro et Alfredo A. Sadun. « Retinal changes in COVID-19 hospitalized cases ». PLOS ONE 15, no 12 (3 décembre 2020) : e0243346. http://dx.doi.org/10.1371/journal.pone.0243346.
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The main objective of this study was to evaluate the retinas of severely or critically ill COVID-19 patients during their hospital stay, at varying time points after symptoms onset. This was a case series observed during May 2020 in two referral centers for COVID-19 treatment in Rio de Janeiro, Brazil. 47 eyes from 25 hospitalized patients with severe or critical confirmed illness were evaluated. A handheld retinal camera was used to acquire bilateral fundus images at several time points after symptoms onset. Electronic health records were retrospectively analyzed and clinical data collected. Severe and critical diseases were noticed in 52% (13/25) and 48% (12/25) of enrolled patients, respectively. Retinal changes were present in 12% (3/25) of patients: a 35 year-old male demonstrated bilateral nerve fiber layer infarcts and microhemorrhages in the papillomacular bundle, but required mechanical ventilation and developed severe anemia and systemic hypotension, acute kidney injury and neurologic symptoms during the course of the disease (critical illness); a 56 year-old male, who required full enoxaparin anticoagulation due to particularly elevated D-dimer (>5.0 mcg/mL), demonstrated unilateral and isolated flame-shaped hemorrhages; and a 49 year-old hypertensive male showed bilateral and discrete retinal dot and blot microhemorrhages. The other 22 patients evaluated did not demonstrate convincing retinal changes upon examination. There was no correlation between disease severity and admission serum levels of CRP, D-dimer and ferritin. This was the first study to show that vascular retinal changes may be present in not insignificant numbers of severe or critical COVID-19 inpatients. These retinal changes, only seen after morbid developments, were likely secondary to clinical intercurrences or comorbidities instead of a direct damage by SARS-CoV-2, and may be important and easily accessible outcome measures of therapeutic interventions and sentinels of neurologic and systemic diseases during COVID-19 pandemic.
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Mattingly, Cameron, Jennifer Elliott, Jeronay Thomas, Jenna Lobby, Sarah Michalets, Kirsten N. Kost et Jacob E. Kohlmeier. « CD8 +T RM-derived IFNγ is a critical activator of human lung epithelial cells. » Journal of Immunology 210, no 1_Supplement (1 mai 2023) : 156.22. http://dx.doi.org/10.4049/jimmunol.210.supp.156.22.
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Abstract Due to their position in the pulmonary mucosa, CD8 +tissue resident memory T cells (T RM) act as sentinels that rapidly respond to, and mediate protection against, respiratory viruses. In mice, T RMhave been shown to mediate protection at barrier sites by producing cytokines, chemokines, and performing cell lysis. However, in humans, less is known about the effector functions of virus-specific lung CD8 +T RM. Using cells from healthy human lung donors, we first identified and quantified the frequency of antigen-specific cells against common respiratory viruses by performing intracellular cytokine staining. We next investigated the polyfunctionality and residency profiles of responding antigen-specific CD8 +T cells and found that that CD69 +CD103 +T RMcomprise a larger portion of the antigen-specific CD8 +T cells for viruses largely restricted to the respiratory epithelium when compared to cells specific for viruses with a broader tissue tropism. Next, by performing a series of in vitro peptide stimulation and cytokine neutralization experiments, we investigated how antigen-specific CD8 +T cells impact local innate and epithelial cells. When stimulated with their cognate antigen, lung CD8 +T RM-derived IFNγ strongly correlated with epithelial cell activation. Finally, we stimulated lung CD8 +T RMwith cognate antigen +/− anti-IFNγ and performed RNAseq on responding antigen-specific CD8 +T RM(CD25 +CD137 +) and epithelial cells to determine exactly how T RM-derived IFNγ alters the cellular program of these cells. Results of this study suggest that human lung CD8 +T RMact to rapidly reprogram local immune cells, and these data will ultimately help us understand how CD8 +T RMfit into the overall immune response to respiratory viruses. This project is supported by R35 HL150803 and the Emory Center of Excellence for Influenza Research and Response (CEIRR) 75N93019R0028.
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Burthe, Sarah J., Stefanie M. Schäfer, Festus A. Asaaga, Natrajan Balakrishnan, Mohammed Mudasssar Chanda, Narayanaswamy Darshan, Subhash L. Hoti et al. « Reviewing the ecological evidence base for management of emerging tropical zoonoses : Kyasanur Forest Disease in India as a case study ». PLOS Neglected Tropical Diseases 15, no 4 (1 avril 2021) : e0009243. http://dx.doi.org/10.1371/journal.pntd.0009243.
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Zoonoses disproportionately affect tropical communities and are associated with human modification and use of ecosystems. Effective management is hampered by poor ecological understanding of disease transmission and often focuses on human vaccination or treatment. Better ecological understanding of multi-vector and multi-host transmission, social and environmental factors altering human exposure, might enable a broader suite of management options. Options may include “ecological interventions” that target vectors or hosts and require good knowledge of underlying transmission processes, which may be more effective, economical, and long lasting than conventional approaches. New frameworks identify the hierarchical series of barriers that a pathogen needs to overcome before human spillover occurs and demonstrate how ecological interventions may strengthen these barriers and complement human-focused disease control. We extend these frameworks for vector-borne zoonoses, focusing on Kyasanur Forest Disease Virus (KFDV), a tick-borne, neglected zoonosis affecting poor forest communities in India, involving complex communities of tick and host species. We identify the hierarchical barriers to pathogen transmission targeted by existing management. We show that existing interventions mainly focus on human barriers (via personal protection and vaccination) or at barriers relating to Kyasanur Forest Disease (KFD) vectors (tick control on cattle and at the sites of host (monkey) deaths). We review the validity of existing management guidance for KFD through literature review and interviews with disease managers. Efficacy of interventions was difficult to quantify due to poor empirical understanding of KFDV–vector–host ecology, particularly the role of cattle and monkeys in the disease transmission cycle. Cattle are hypothesised to amplify tick populations. Monkeys may act as sentinels of human infection or are hypothesised to act as amplifying hosts for KFDV, but the spatial scale of risk arising from ticks infected via monkeys versus small mammal reservoirs is unclear. We identified 19 urgent research priorities for refinement of current management strategies or development of ecological interventions targeting vectors and host barriers to prevent disease spillover in the future.
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Biskaborn, Boris K., Larisa Nazarova, Lyudmila A. Pestryakova, Liudmila Syrykh, Kim Funck, Hanno Meyer, Bernhard Chapligin et al. « Spatial distribution of environmental indicators in surface sediments of Lake Bolshoe Toko, Yakutia, Russia ». Biogeosciences 16, no 20 (18 octobre 2019) : 4023–49. http://dx.doi.org/10.5194/bg-16-4023-2019.
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Abstract. Rapidly changing climate in the Northern Hemisphere and associated socio-economic impacts require reliable understanding of lake systems as important freshwater resources and sensitive sentinels of environmental change. To better understand time-series data in lake sediment cores, it is necessary to gain information on within-lake spatial variabilities of environmental indicator data. Therefore, we retrieved a set of 38 samples from the sediment surface along spatial habitat gradients in the boreal, deep, and yet pristine Lake Bolshoe Toko in southern Yakutia, Russia. Our methods comprise laboratory analyses of the sediments for multiple proxy parameters, including diatom and chironomid taxonomy, oxygen isotopes from diatom silica, grain-size distributions, elemental compositions (XRF), organic carbon content, and mineralogy (XRD). We analysed the lake water for cations, anions, and isotopes. Our results show that the diatom assemblages are strongly influenced by water depth and dominated by planktonic species, i.e. Pliocaenicus bolshetokoensis. Species richness and diversity are higher in the northern part of the lake basin, associated with the availability of benthic, i.e. periphytic, niches in shallower waters. δ18Odiatom values are higher in the deeper south-western part of the lake, probably related to water temperature differences. The highest amount of the chironomid taxa underrepresented in the training set used for palaeoclimate inference was found close to the Utuk River and at southern littoral and profundal sites. Abiotic sediment components are not symmetrically distributed in the lake basin, but vary along restricted areas of differential environmental forcing. Grain size and organic matter are mainly controlled by both river input and water depth. Mineral (XRD) data distributions are influenced by the methamorphic lithology of the Stanovoy mountain range, while elements (XRF) are intermingled due to catchment and diagenetic differences. We conclude that the lake represents a valuable archive for multiproxy environmental reconstruction based on diatoms (including oxygen isotopes), chironomids, and sediment–geochemical parameters. Our analyses suggest multiple coring locations preferably at intermediate depth in the northern basin and the deep part in the central basin, to account for representative bioindicator distributions and higher temporal resolution, respectively.
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NUGENT, G., J. WHITFORD, I. J. YOCKNEY et M. L. CROSS. « Reduced spillover transmission ofMycobacterium bovisto feral pigs (Sus scofa) following population control of brushtail possums (Trichosurus vulpecula) ». Epidemiology and Infection 140, no 6 (18 août 2011) : 1036–47. http://dx.doi.org/10.1017/s0950268811001579.
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SUMMARYIn New Zealand, bovine tuberculosis (bTB) is present in domestic cattle and deer herds primarily as the result of on-going disease transmission from the primary wildlife host, the brushtail possum (Trichosurus vulpecula). However, bTB is also present in other introduced free-ranging mammalian species. Between 1996 and 2007, we conducted a series of studies to determine whether poison control of possum populations would have any effect on the prevalence ofMycobacterium bovisinfection in sympatric feral pigs (Sus scrofa). We compared trends in the prevalence of bTB infection in feral pigs in six study areas: possum numbers were reduced in three areas, but not in the other three, effectively providing a thrice-replicated before-after-control-intervention design. Before possum control, the overall prevalence of culture-confirmedM. bovisinfection in feral pigs was 16·7–94·4%, depending on area. Infection prevalence varied little between genders but did vary with age, increasing during the first 2–3 years of life but then declining in older pigs. In the areas in which possum control was applied,M. bovisprevalence in feral pigs fell to near zero within 2–3 years, provided control was applied successfully at the whole-landscape scale. In contrast, prevalence changed much less or not at all in the areas with no possum control. We conclude that feral pigs in New Zealand acquireM. bovisinfection mainly by inter-species transmission from possums, but then rarely pass the disease on to other pigs and are end hosts. This is in contrast to the purported role of pigs as bTB maintenance hosts in other countries, and we suggest the difference in host status may reflect differences in the relative importance of the oral route of infection in different environments. Despite harbouringM. bovisinfection for a number of years, pigs in New Zealand do not sustain bTB independently, but are good sentinels for disease prevalence in possum populations.
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Zhang Dongyan, 张东彦, 戴震 Dai Zhen, 徐新刚 Xu Xingang, 杨贵军 Yang Guijun, 孟炀 Meng Yang, 冯海宽 Feng Haikuan, 洪琪 Hong Qi et 姜飞 Jiang Fei. « 基于时序Sentinel-2影像的现代农业园区作物分类研究 ». Infrared and Laser Engineering 50, no 5 (2021) : 20200318. http://dx.doi.org/10.3788/irla20200318.
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Chen, Lin, Yeqiao Wang, Chunying Ren, Bai Zhang et Zongming Wang. « Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data ». Remote Sensing 11, no 4 (18 février 2019) : 414. http://dx.doi.org/10.3390/rs11040414.
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Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR); machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mg·ha−1 and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.
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Rabiei, Saman, Ehsan Jalilvand et Massoud Tajrishy. « A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data ». Sustainability 13, no 20 (14 octobre 2021) : 11355. http://dx.doi.org/10.3390/su132011355.
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Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%.
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Nyberg, Reita H., Pasi Korkola et Johanna U. Mäenpää. « Sentinel Node and Ovarian Tumors : A Series of 20 Patients ». International Journal of Gynecologic Cancer 27, no 4 (mai 2017) : 684–89. http://dx.doi.org/10.1097/igc.0000000000000948.
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ObjectiveIntraoperative detection of ovarian sentinel nodes has been shown to be feasible. We examined the detection rate and locations of sentinel nodes in patients with ovarian tumors. We also aimed to assess the reliability of sentinel node method in predicting regional lymph node metastasis.MethodsTwenty patients scheduled for laparotomy because of a pelvic mass were recruited to the study. In the beginning of the laparotomy, radioisotope and blue dye were injected under the serosa next to the junction of the ovarian tumor and suspensory ligament. The number and locations of the hot and/or blue nodes/spots were recorded during the operation. If the tumor was malignant according to the frozen section, systematic lymphadenectomies were performed, the sentinel nodes sampled separately, and their status compared with other regional lymph nodes.ResultsEleven patients had a right-sided ovarian tumor, 7 patients a left-sided tumor, and 2 patients had bilateral tumors. A median of 2 sentinel nodes/locations per patient (range, 1–3) were found. Sixty percent of all sentinel nodes were located in the para-aortic region only, compared with 30% in both para-aortic and pelvic areas and 10% in pelvic area only. Both unilateral and bilateral locations were found. In 83% of the cases with more than 1 sentinel node location, they were located in separate anatomical regions. In 3 patients, systematic lymphadenectomies were performed. One of them had nodal metastases in 2 regions and also a metastasis in 1 of her 2 sentinel nodes in 1 of those regions.ConclusionsIn patients with ovarian tumor(s), the detection of sentinel nodes is feasible. They are located in different anatomic areas both ipsilaterally and contralaterally, although most of them are found in the para-aortic region. The reliability of the sentinel node concept should be evaluated in the framework of a multicenter trial.
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Xing, L., X. Tang, H. Wang, W. Fan et X. Gao. « MAPPING WETLANDS OF DONGTING LAKE IN CHINA USING LANDSAT AND SENTINEL-1 TIME SERIES AT 30M ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (30 avril 2018) : 1971–76. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1971-2018.
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Mapping and monitoring wetlands of Dongting lake using optical sensor data has been limited by cloud cover, and open access Sentinal-1 C-band data could provide cloud-free SAR images with both have high spatial and temporal resolution, which offer new opportunities for monitoring wetlands. In this study, we combined optical data and SAR data to map wetland of Dongting Lake reserves in 2016. Firstly, we generated two monthly composited Landsat land surface reflectance, NDVI, NDWI, TC-Wetness time series and Sentinel-1 (backscattering coefficient for VH and VV) time series. Secondly, we derived surface water body with two monthly frequencies based on the threshold method using the Sentinel-1 time series. Then the permanent water and seasonal water were separated by the submergence ratio. Other land cover types were identified based on SVM classifier using Landsat time series. Results showed that (1) the overall accuracies and kappa coefficients were above 86.6 % and 0.8. (3) Natural wetlands including permanent water body (14.8 %), seasonal water body (34.6 %), and permanent marshes (10.9 %) were the main land cover types, accounting for 60.3 % of the three wetland reserves. Human-made wetlands, such as rice fields, accounted 34.3 % of the total area. Generally, this study proposed a new flowchart for wetlands mapping in Dongting lake by combining multi-source remote sensing data, and the use of the two-monthly composited optical time series effectively made up the missing data due to the clouds and increased the possibility of precise wetlands classification.
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Vári, Minnette. « Sentinel, a series of videographs, 2000 ». Rethinking Marxism 15, no 3 (juillet 2003) : 416–17. http://dx.doi.org/10.1080/0893569032000131956.
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Senty, Paul, Radoslaw Guzinski, Kenneth Grogan, Robert Buitenwerf, Jonas Ardö, Lars Eklundh, Alkiviadis Koukos, Torbern Tagesson et Michael Munk. « Fast Fusion of Sentinel-2 and Sentinel-3 Time Series over Rangelands ». Remote Sensing 16, no 11 (21 mai 2024) : 1833. http://dx.doi.org/10.3390/rs16111833.
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Monitoring ecosystems at regional or continental scales is paramount for biodiversity conservation, climate change mitigation, and sustainable land management. Effective monitoring requires satellite imagery with both high spatial resolution and high temporal resolution. However, there is currently no single, freely available data source that fulfills these needs. A seamless fusion of data from the Sentinel-3 and Sentinel-2 optical sensors could meet these monitoring requirements as Sentinel-2 observes at the required spatial resolution (10 m) while Sentinel-3 observes at the required temporal resolution (daily). We introduce the Efficient Fusion Algorithm across Spatio-Temporal scales (EFAST), which interpolates Sentinel-2 data into smooth time series (both spatially and temporally). This interpolation is informed by Sentinel-3’s temporal profile such that the phenological changes occurring between two Sentinel-2 acquisitions at a 10 m resolution are assumed to mirror those observed at Sentinel-3’s resolution. The EFAST consists of a weighted sum of Sentinel-2 images (weighted by a distance-to-clouds score) coupled with a phenological correction derived from Sentinel-3. We validate the capacity of our method to reconstruct the phenological profile at a 10 m resolution over one rangeland area and one irrigated cropland area. The EFAST outperforms classical interpolation techniques over both rangeland (−72% in the mean absolute error, MAE) and agricultural areas (−43% MAE); it presents a performance comparable to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (+5% MAE in both test areas) while being 140 times faster. The computational efficiency of our approach and its temporal smoothing enable the creation of seamless and high-resolution phenology products on a regional to continental scale.
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Magalhães, Ivo Augusto Lopes, Osmar Abílio de Carvalho Junior, Renato Fontes Guimarães et Roberto Arnaldo Trancoso Gomes. « SENTINEL-1 TIME SERIES ANALYSIS ON CENTRAL AMAZON FLOODS ». Mercator 21, no 1 (15 juin 2022) : 1–19. http://dx.doi.org/10.4215/rm2022.e21019.
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This study aimed to analyze the dynamics of the flooded areas of the Sentinel 1-SAR time series in a section of the Central Amazon between September 26, 2016, and February 8, 2020. The total of images was 59 for each polarization. In addition, the study calculated the average ordinary flood line (ALOF) from the heights of the fluviometric rulers between the years 1967 to 2020 and compared it with the values present in the radar time series. The pre-processing of the Sentinel-1 time series in the VV and VH polarizations used the following methodological sequence: Apply Orbit File, Radiometric Calibration (σ0), Range-Doppler Terrain Correction, Speckle Filter, and conversion to decibels (dB). The previous analysis of the adaptive filters showed different results for the two polarizations, obtaining the best result for the VV polarization using the Frost filter with 3x3 and the VH polarization with the Lee filter 3x3. The extraction of water bodies and wetlands used a threshold value, making masks for the entire period. The most considerable extent of the floodable area occurred on June 17, 2019, with 6,611.86 km2, representing 16.42% of the SAR scene in the VH polarization and 6,443.19 km2, representing 16.10% of the SAR scene in the VV polarization. The relationship between the VH and VV wetlands to the ruler's height was satisfactory, with coefficients of determination (R2) of 0.79 in the VH polarization and of 0.64 in the VV polarization and a p-value less than 0.05. Keywords: Remote sensing; Radar; Mapping of Water Bodies.
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Zhang, Hebing, Hongyi Yuan, Weibing Du et Xiaoxuan Lyu. « Crop Identification Based on Multi-Temporal Active and Passive Remote Sensing Images ». ISPRS International Journal of Geo-Information 11, no 7 (11 juillet 2022) : 388. http://dx.doi.org/10.3390/ijgi11070388.
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Although vegetation index time series from optical images are widely used for crop mapping, it remains difficult to obtain sufficient time-series data because of satellite revisit time and weather in some areas. To address this situation, this paper considered Wen County, Henan Province, Central China as the research area and fused multi-source features such as backscatter coefficient, vegetation index, and time series based on Sentinel-1 and -2 data to identify crops. Through comparative experiments, this paper studied the feasibility of identifying crops with multi-temporal data and fused data. The results showed that the accuracy of multi-temporal Sentinel-2 data increased by 9.2% compared with single-temporal Sentinel-2 data, and the accuracy of multi-temporal fusion data improved by 17.1% and 2.9%, respectively, compared with multi-temporal Sentinel-1 and Sentinel-2 data. Multi-temporal data well-characterizes the phenological stages of crop growth, thereby improving the classification accuracy. The fusion of Sentinel-1 synthetic aperture radar data and Sentinel-2 optical data provide sufficient time-series data for crop identification. This research can provide a reference for crop recognition in precision agriculture.
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Stankevich, Sergey, Iryna Piestova, Olga Titarenko, Volodymyr Filipovych, Andre Samberg, Tamara Dudar et Mykhailo Svideniuk. « Urban Area Geodynamic Risk Mapping Using Long-Term Time Series of Sentinel-1 Satellite Radar Interferometry ». Information & ; Security : An International Journal 40, no 1 (2018) : 39–50. http://dx.doi.org/10.11610/isij.4003.
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Denize, Julien, Laurence Hubert-Moy, Julie Betbeder, Samuel Corgne, Jacques Baudry et Eric Pottier. « Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes ». Remote Sensing 11, no 1 (27 décembre 2018) : 37. http://dx.doi.org/10.3390/rs11010037.
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Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using Support Vector Machine and Random Forest algorithms in a 130 km² agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices.
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Grabska, Ewa, Patrick Hostert, Dirk Pflugmacher et Katarzyna Ostapowicz. « Forest Stand Species Mapping Using the Sentinel-2 Time Series ». Remote Sensing 11, no 10 (20 mai 2019) : 1197. http://dx.doi.org/10.3390/rs11101197.
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Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.
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Lu, Yi, Changbao Yang et Qigang Jiang. « Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units ». Remote Sensing 13, no 23 (27 novembre 2021) : 4824. http://dx.doi.org/10.3390/rs13234824.
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The potential use of time-series Sentinel-1 synthetic aperture radar (SAR) data for rock unit discrimination has never been explored in previous studies. Here, we employed time-series Sentinel-1 data to discriminate Dananhu formation, Xinjiang group, Granite, Wusu group, Xishanyao formation, and Diorite in Xinjiang, China. Firstly, the temporal variation of the backscatter metrics (backscatter coefficient and coherence) from April to October derived from Sentinel-1, was analyzed. Then, the significant differences of the time-series SAR metrics among different rock units were checked using the Kruskal–Wallis rank sum test and Tukey’s honest significant difference test. Finally, random forest models were used to discriminate rock units. As for the input features, there were four groups: (1) time-series backscatter metrics, (2) single-date backscatter metrics, (3) time-series backscatter metrics at VV, and (4) VH channel. In each feature group, there were three sub-groups: backscatter coefficient, coherence, and combined use of backscatter coefficient and coherence. Our results showed that time-series Sentinel-1 data could improve the discrimination accuracy by roughly 9% (from 55.4% to 64.4%), compared to single-date Sentinel-1 data. Both VV and VH polarization provided comparable results. Coherence complements the backscatter coefficient when discriminating rock units. Among the six rock units, the Granite and Xinjiang group can be better differentiated than the other four rock units. Though the result still leaves space for improvement, this study further demonstrates the great potential of time-series Sentinel-1 data for rock unit discrimination.
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Descals, Adrià, Aleixandre Verger, Gaofei Yin et Josep Peñuelas. « Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series ». Remote Sensing 12, no 22 (13 novembre 2020) : 3738. http://dx.doi.org/10.3390/rs12223738.
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The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.
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Yang, Kaixiang, Youming Luo, Mengyao Li, Shouyi Zhong, Qiang Liu et Xiuhong Li. « Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine ». Remote Sensing 14, no 17 (4 septembre 2022) : 4395. http://dx.doi.org/10.3390/rs14174395.
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Sentinel-2 NDVI and surface reflectance time series have been widely used in various geoscience research, but the data is deteriorated or missing due to the cloud contamination, so it is necessary to reconstruct the Sentinel-2 NDVI and surface reflectance time series. At present, there are few studies on reconstructing the Sentinel-2 NDVI or surface reflectance time series, and these existing reconstruction methods have some shortcomings. We proposed a new method to reconstruct the Sentinel-2 NDVI and surface reflectance time series using the penalized least-square regression based on discrete cosine transform (DCT-PLS) method. This method iteratively identifies cloud-contaminated NDVI over NDVI time series from the Sentinel-2 surface reflectance data by adjusting the weights. The NDVI and surface reflectance time series are then reconstructed from cloud-free NDVI and surface reflectance using the adjusted weights as constraints. We have made some improvements to the DCT-PLS method. First, the traditional discrete cosine transformation (DCT) in the DCT-PLS method is matrix generated from discrete and equally spaced data, we reconfigured the DCT formulas to adapt for irregular interval time series, and optimized the control parameters N and s according to the typical vegetation samples in China. Second, the DCT-PLS method was deployed in the Google Earth Engine (GEE) platform for the efficiency and convenience of data users. We used the DCT-PLS method to reconstruct the Sentinel-2 NDVI time series and surface reflectance time series in the blue, green, red, and near infrared (NIR) bands in typical vegetation samples and the Zhangjiakou and Hangzhou study area. We found that this method performed better than the SG filter method in reconstructing the NDVI time series, and can identify and reconstruct the contaminated NDVI as well as surface reflectance with low root mean square error (RMSE) and high coefficient of determination (R2). However, in cases of a long range of cloud contamination, or above water surface, it may be necessary to increase the control parameter s for a more stable performance. The GEE code is freely available online and the link is in the conclusions of this article, researchers are welcome to use this method to generate cloudless Sentinel-2 NDVI and surface reflectance time series with 10 m spatial resolution, which is convenient for landcover classification and many other types of research.
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Tran, Khuong H., Massimo Menenti et Li Jia. « Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold ». Remote Sensing 14, no 22 (12 novembre 2022) : 5721. http://dx.doi.org/10.3390/rs14225721.
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The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time t is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time t−1 to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.
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Zhou, Fuqun, Detang Zhong et Rihana Peiman. « Reconstruction of Cloud-free Sentinel-2 Image Time-series Using an Extended Spatiotemporal Image Fusion Approach ». Remote Sensing 12, no 16 (12 août 2020) : 2595. http://dx.doi.org/10.3390/rs12162595.
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Time-series for medium spatial resolution satellite imagery are a valuable resource for environmental assessment and monitoring at regional and local scales. Sentinel-2 satellites from the European Space Agency (ESA) feature a multispectral instrument (MSI) with 13 spectral bands and spatial resolutions from 10 m to 60 m, offering a revisit range from 5 days at the equator to a daily approach of the poles. Since their launch, the Sentinel-2 MSI image time-series from satellites have been used widely in various environmental studies. However, the values of Sentinel-2 image time-series have not been fully realized and their usage is impeded by cloud contamination on images, especially in cloudy regions. To increase cloud-free image availability and usage of the time-series, this study attempted to reconstruct a Sentinel-2 cloud-free image time-series using an extended spatiotemporal image fusion approach. First, a spatiotemporal image fusion model was applied to predict synthetic Sentinel-2 images when clear-sky images were not available. Second, the cloudy and cloud shadow pixels of the cloud contaminated images were identified based on analysis of the differences of the synthetic and observation image pairs. Third, the cloudy and cloud shadow pixels were replaced by the corresponding pixels of its synthetic image. Lastly, the pixels from the synthetic image were radiometrically calibrated to the observation image via a normalization process. With these processes, we can reconstruct a full length cloud-free Sentinel-2 MSI image time-series to maximize the values of observation information by keeping observed cloud-free pixels and calibrating the synthetized images by using the observed cloud-free pixels as references for better quality.
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Stendardi, Laura, Stein Karlsen, Georg Niedrist, Renato Gerdol, Marc Zebisch, Mattia Rossi et Claudia Notarnicola. « Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions ». Remote Sensing 11, no 5 (6 mars 2019) : 542. http://dx.doi.org/10.3390/rs11050542.
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A synergic integration of Synthetic Aperture Radar (SAR) and optical time series offers an unprecedented opportunity in vegetation phenology monitoring for mountain agriculture management. In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South Tyrol region (Italy) from October 2014 to September 2016. Together with Sentinel-1 images, we exploited corresponding Sentinel-2 images and ground measurements. Results show that Sentinel-1 cross-polarized VH backscattering coefficients have a strong vegetation contribution and are well correlated with the Normalized Difference Vegetation Index (NDVI) values retrieved from optical sensors, thus allowing the extraction of meadow phenological phases. Particularly for the Start Of Season (SOS) at low altitudes, the mean difference in days between Sentinel-1 and ground sensors is compatible with the acquisition time of the SAR sensor. However, the results show a decrease in accuracy with increasing altitude. The same trend is observed for senescence. The main outcomes of our investigations in terms of inter-satellite comparison show that Sentinel-1 is less effective than Sentinel-2 in detecting the SOS. At the same time, Sentinel-1 is as robust as Sentinel-2 in defining mowing events. Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.
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Mouret, Florian, Mohanad Albughdadi, Sylvie Duthoit, Denis Kouamé, Guillaume Rieu et Jean-Yves Tourneret. « Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series ». Remote Sensing 13, no 5 (4 mars 2021) : 956. http://dx.doi.org/10.3390/rs13050956.
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This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.
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Tsyganskaya, Viktoriya, Sandro Martinis, Philip Marzahn et Ralf Ludwig. « Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data ». Remote Sensing 10, no 8 (15 août 2018) : 1286. http://dx.doi.org/10.3390/rs10081286.
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The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth’s surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.
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Shimizu, Katsuto, Tetsuji Ota et Nobuya Mizoue. « Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests ». Remote Sensing 11, no 16 (14 août 2019) : 1899. http://dx.doi.org/10.3390/rs11161899.
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The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.
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Qu, Yang, Wenzhi Zhao, Zhanliang Yuan et Jiage Chen. « Crop Mapping from Sentinel-1 Polarimetric Time-Series with a Deep Neural Network ». Remote Sensing 12, no 15 (3 août 2020) : 2493. http://dx.doi.org/10.3390/rs12152493.
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Timely and accurate agricultural information is essential for food security assessment and agricultural management. Synthetic aperture radar (SAR) systems are increasingly available in crop mapping, as they provide all-weather imagery. In particular, the Sentinel-1 sensor provides dense time-series data, thus offering a unique opportunity for crop mapping. However, in most studies, the Sentinel-1 complex backscatter coefficient was used directly which limits the potential of the Sentinel-1 in crop mapping. Meanwhile, most of the existing methods may not be tailored for the task of crop classification in time-series polarimetric SAR data. To solve the above problem, we present a novel deep learning strategy in this research. To be specific, we collected Sentinel-1 time-series data in two study areas. The Sentinel-1 image covariance matrix is used as an input to maintain the integrity of polarimetric information. Then, a depthwise separable convolution recurrent neural network (DSCRNN) architecture is proposed to characterize crop types from multiple perspectives and achieve better classification results. The experimental results indicate that the proposed method achieves better accuracy in complex agricultural areas than other classical methods. Additionally, the variable importance provided by the random forest (RF) illustrated that the covariance vector has a far greater influence than the backscatter coefficient. Consequently, the strategy proposed in this research is effective and promising for crop mapping.
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Heckel, Kai, Marcel Urban, Patrick Schratz, Miguel Mahecha et Christiane Schmullius. « Predicting Forest Cover in Distinct Ecosystems : The Potential of Multi-Source Sentinel-1 and -2 Data Fusion ». Remote Sensing 12, no 2 (17 janvier 2020) : 302. http://dx.doi.org/10.3390/rs12020302.
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The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution.
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Aslam, Tahira, et R. Cochrane. « Sentinel node and sampling a single surgeon series ». European Journal of Surgical Oncology (EJSO) 35, no 11 (novembre 2009) : 1213. http://dx.doi.org/10.1016/j.ejso.2009.07.037.
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Meng, Haoran, Cunjun Li, Yu Liu, Yusheng Gong, Wanying He et Mengxi Zou. « Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images ». Land 12, no 2 (1 février 2023) : 398. http://dx.doi.org/10.3390/land12020398.
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Corn is an important food crop worldwide, and its yield is directly related to Chinese food security. Accurate remote sensing extraction of corn can realize the rational application of land resources, which is of great significance to the sustainable development of modern agriculture. In the field of large-scale crop remote sensing classification, single-period optical remote sensing images often cannot achieve high-precision classification. To improve classification accuracy, multiple time series image combinations have gradually been adopted. However, due to the influence of cloudy and rainy weather, it is often difficult to obtain complete time series optical images. Synthetic aperture radar (SAR) data are imaged by microwaves, which have strong penetrating power and are not affected by clouds. A critical way to solve this problem is to use SAR images to compensate for the lack of optical images and obtain a complete time series image in the corn-growing season. However, SAR images have limited wavelengths and cannot provide important wavelengths, such as visible light bands and near-infrared information. To solve this problem, this study took Zhaodong City, a vital corn-planting base in China, as the research area; took GF-6/GF-3 and Sentinel-1/Sentinel-2 as remote sensing data sources; designed12 classification scenarios; analyzed the best classification period and the best time series combination of corn classification; studied the influence of SAR images on the classification results of time series images; and compared the classification differences between GF-6/GF-3 and Sentinel-1/Sentinel-2. The results show that the classification accuracy of time series combinations is much higher than that of single-period images. The polarization characteristics of SAR images can improve the classification accuracy with time series images. The classification accuracy of GF series images from China is obviously higher than that of Sentinel series images. The research performed in this paper can provide a reference for agricultural classification by using remote sensing data.
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Shen, Guozhuang, Wenxue Fu, Huadong Guo et Jingjuan Liao. « Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake ». Water 14, no 12 (13 juin 2022) : 1902. http://dx.doi.org/10.3390/w14121902.
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Mapping water bodies with a high accuracy is necessary for water resource assessment, and mapping them rapidly is necessary for flood monitoring. Poyang Lake is the largest freshwater lake in China, and its wetland is one of the most important in the world. Poyang Lake is affected by floods from the Yangtze River basin every year, and the fluctuation of the water area and water level directly or indirectly affects the ecological environment of Poyang Lake. Synthetic Aperture Radar (SAR) is particularly suitable for large-scale water body mapping, as SAR allows data acquisition regardless of illumination and weather conditions. The two-satellite Sentinel-1 constellation, providing C-Band SAR data, passes over the Poyang Lake about five times a month. With its high temporal-spatial resolution, the Sentinel-1 SAR data can be used to accurately monitor the water body. After acquiring all the Sentinel-1 (1A and 1B) SAR data, to ensure the consistency of data processing, we propose the use of a Python and SeNtinel Application Platform (SNAP)-based engine (SARProcMod) to process the data and construct a Poyang Lake Sentinel-1 SAR dataset with a 10 m resolution. To extract water body information from Sentinel-1 SAR data, we propose an automatic classification engine based on a modified U-Net convolutional neural network (WaterUNet), which classifies all data using artificial sample datasets with a high validation accuracy. The results show that the maximum and minimum water areas in our study area were 2714.08 km2 on 20 July 2020, and 634.44 km2 on 4 January 2020. Compared to the water level data from the Poyang gauging station, the water area was highly correlated with the water level, with the correlation coefficient being up to 0.92 and the R2 from quadratic polynomial fitting up to 0.88; thus, the resulting relationship results can be used to estimate the water area or water level of Poyang Lake. According to the results, we can conclude that Sentinel-1 SAR and WaterUNet are very suitable for water body monitoring as well as emergency flood mapping.
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Rehman, Touseef Ur, Maaz Alam, Nasru Minallah, Waleed Khan, Jaroslav Frnda, Shawal Mushtaq et Muhammad Ajmal. « Long short term memory deep net performance on fused Planet-Scope and Sentinel-2 imagery for detection of agricultural crop ». PLOS ONE 18, no 2 (3 février 2023) : e0271897. http://dx.doi.org/10.1371/journal.pone.0271897.
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In view of the challenges faced by organizations and departments concerned with agricultural capacity observations, we collected In-Situ data consisting of diverse crops (More than 11 consumable vegetation types) in our pilot region of Harichand Charsadda, Khyber Pakhtunkhwa (KP), Pakistan. Our proposed Long Short-Term Memory based Deep Neural network model was trained for land cover land use statistics generation using the acquired ground truth data, for a synergy between Planet-Scope Dove and European Space Agency’s Sentinel-2. Total of 4 bands from both sentinel-2 and planet scope including Red, Green, Near-Infrared (NIR) and Normalised Difference Vegetation Index (NDVI) were used for classification purpose. Using short temporal frame of Sentinel-2 comprising 5 date images, we propose an realistic and implementable procedure for generating accurate crop statistics using remote sensing. Our self collected data-set consists of a total number of 107,899 pixels which was further split into 70% and 30% for training and testing purpose of the model respectively. The collected data is in the shape of field parcels, which has been further split for training, validation and test sets, to avoid spatial auto-correlation. To ensure the quality and accuracy 15% of the training data was left out for validation purpose, and 15% for testing. Prediction was also performed on our trained model and visual analysis of the area from the image showed significant results. Further more a comparison between Sentinel-2 time series is performed separately from the fused Planet-Scope and Sentinel-2 time-series data sets. The results achieved shows a weighted average of 93% for Sentinel-2 time series and 97% for fused Planet-Scope and Sentinel-2 time series.
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De Vroey, Mathilde, Julien Radoux et Pierre Defourny. « Grassland Mowing Detection Using Sentinel-1 Time Series : Potential and Limitations ». Remote Sensing 13, no 3 (20 janvier 2021) : 348. http://dx.doi.org/10.3390/rs13030348.
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Grasslands encompass vast and diverse ecosystems that provide food, wildlife habitat and carbon storage. Their large range in land use intensity significantly impacts their ecological value and the balance between these goods and services. Mowing dates and frequencies are major aspects of grassland use intensity, which have an impact on their ecological value as habitats. Previous studies highlighted the feasibility of detecting mowing events based on remote sensing time series, a few of which using synthetic aperture radar (SAR) imagery. Although providing encouraging results, research on grassland mowing detection often lacks sufficient precise reference data for corroboration. The goal of the present study is to quantitatively and statistically assess the potential of Sentinel-1 C-band SAR for detecting mowing events in various agricultural grasslands, using a large and diverse reference data set collected in situ. Several mowing detection methods, based on SAR backscattering and interferometric coherence time series, were thoroughly evaluated. Results show that 54% of mowing events could be detected in hay meadows, based on coherence jumps. Grazing events were identified as a major confounding factor, as most false detections were made in pastures. Parcels with one mowing event in the summer were identified with the highest accuracy (71%). Overall, this study demonstrates that mowing events can be detected through Sentinel-1 coherence. However, the performances could probably be further enhanced by discriminating pastures beforehand and combining Sentinel-1 and Sentinel-2 data for mowing detection.
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Narin, O. G., C. Bayik, S. Abdikan et F. Balik Sanli. « USING RVI AND NDVI TIME SERIES FOR CROPLAND MAPPING WITH TIME-WEIGHTED DYNAMIC TIME WARPING ». International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (2 décembre 2022) : 97–101. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-97-2022.
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Abstract. Monitoring and management of agricultural lands are essential due to reasons affecting agriculture, such as increasing population and global climate. With the increase in the temporal resolution of satellite systems, time-series classifications have become popular in cropland mapping. Because annual plants can give similar spectral reflectance values on the same date. In this context, agricultural land (∼500 km2) was selected in the south of South Dakota in the United States. The area includes alfalfa, corn, soybeans, winter wheat plants, developed, grassland/pasture, herbaceous wetlands, and open water areas. The study aims to map croplands with vegetation indices produced by annual Sentinel-1 and Sentinel-2 satellites. In this context, Radar Vegetation Index (RVI) produced from 25 Sentinel-1, and the Normalized Difference Vegetation Index (NDVI) produced from 26 Sentinel-2 satellites were used for 2020. We used the Time-Weighted Dynamic Time Warping (TWDTW) algorithm, which separates and classifies the similarities between two time series with variable speeds with time constraints. For mapping, the indices were classified both individually and combined. The highest overall accuracy (77.2%) was obtained with the combined use of NDVI and RVI. Among the plant classes, the lowest accuracy (83.71%) was found, and it was determined that the plant classes did not mix much. Sentinel-2 satellite is not available before April due to weather conditions in the region. For this reason, since the Sentinel-1 satellite is not affected by weather conditions, it is thought that the use of two satellites together will be beneficial in time series analysis.
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Ma, Chunfeng, Kasper Johansen et Matthew F. McCabe. « Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series ». Remote Sensing 14, no 5 (1 mars 2022) : 1205. http://dx.doi.org/10.3390/rs14051205.
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Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions.
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Frantz, David, Franz Schug, Akpona Okujeni, Claudio Navacchi, Wolfgang Wagner, Sebastian van der Linden et Patrick Hostert. « National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series ». Remote Sensing of Environment 252 (janvier 2021) : 112128. http://dx.doi.org/10.1016/j.rse.2020.112128.
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