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Artykuły w czasopismach na temat "The Sentinels series"
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Schirdewahn, Frederik, Hartmut H. K. Lentz, Vittoria Colizza, Andreas Koher, Philipp Hövel i Beatriz Vidondo. "Early warning of infectious disease outbreaks on cattle-transport networks". PLOS ONE 16, nr 1 (6.01.2021): 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 i 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 (14.08.2020): 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 i Stefania Camici. "Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe". Natural Hazards and Earth System Sciences 22, nr 8 (2.08.2022): 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, nr 1 (1.01.2021): 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, i James E. Cloern. "The annual cycles of phytoplankton biomass". Philosophical Transactions of the Royal Society B: Biological Sciences 365, nr 1555 (12.10.2010): 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 i in. "The Italian Network of Laboratories for Veterinary Oncology (NILOV) 2.0: Improving Knowledge on Canine Tumours". Veterinary Sciences 9, nr 8 (30.07.2022): 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 i Antonio Quesada. "Regional weather survey on Byers Peninsula, Livingston Island, South Shetland Islands, Antarctica". Antarctic Science 25, nr 2 (20.03.2013): 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, i 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, nr 1 (30.06.2014): 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 i in. "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, nr 12 (1.12.2006): 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, nr 2 (1.04.2014): 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.
Rozprawy doktorskie na temat "The Sentinels series"
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Castro, alvarado Enzo. "Exploiting multi-year high-resolution Sentinel-2 image time series for mapping fallow practice in West Africa". Electronic Thesis or Diss., Paris, AgroParisTech, 2023. http://www.theses.fr/2023AGPT0015.
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En Afrique de l'Ouest, la cartographie des jachères est essentielle pour caractériser les systèmes agricoles et évaluer avec précision la durabilité agroécologique des pratiques actuelles et leur contribution à la sécurité alimentaire. Cependant, les méthodes actuellement disponibles pour la cartographie des surfaces cultivées ne sont pas adaptées aux conditions environnementales et culturales rencontrées en contexte d'agriculture familiale en Afrique sub-saharienne. Dans cette thèse de Doctorat, plusieurs stratégies de cartographie des jachères basées sur des approches de classification supervisée ont été explorées. Pour ce faire, des séries temporelles d'images Sentinel-2 ainsi que des données pluviométriques ont été confrontées à une importante base de données collectées sur le terrain entre 2016 et 2021 sur le site de Koumbia, localisé en zone soudanienne au Burkina Faso. Les résultats de ce travail de thèse ont montré que les méthodes d'apprentissage automatique, dites "traditionnelles", ne permettent pas de détecter les surfaces en jachère pour les conditions pédoclimatiques de la zone d'étude. En effet, les précisions obtenues sont très faibles, avec des F1-scores inférieurs à 0.20. Les résultats les plus prometteurs ont été obtenus à partir d'une approche basée sur l'analyse des trajectoires. Une série d'adaptations méthodologiques a alors dû être effectuée afin de pouvoir exploiter l'orientation pluri-annuelle de données annuelles. Pour ce dernier cas, cette thèse s'est concentrée sur la classe "terres agricoles non actives" (NAAL en anglais) pour laquelle des valeurs de F1-scores entre 0.75 et 0.92 ont été obtenus, en considérant un jeu de données de référence complet. Par ailleurs, nos résultats ont mis en évidence que les stratégies de cartographie intégrant des informations spectrales pluri-annuelles dans le processus d'apprentissage constituent une approche viable, permettant de décrire les surfaces en jachère non pas par leur état actuel (c'est-à-dire par l'occupation du sol), mais par les changements à l'œuvre au cours de la période qui jouxte la mise au repos des cultures. Cependant, nos résultats ont également montré que le domaine de validité spatiale de l'approche pouvait être limité, en raison de l'augmentation de l'incertitude du modèle dans les zones où aucune donnée de terrain n'est disponible. Cela souligne l'importance d'incorporer des approches non supervisées au processus de classification afin de permettre plus de fiabilité dans le processus d'extrapolation spatiale. Des stratégies pluri-annuelles plus explicites, où le processus d'analyse temporelle est délégué aux algorithmes de classification ont également été testées et ont montré des résultats légèrement améliorés par rapport aux stratégies de cartographie annuelles directes. Toutefois les performances obtenues restent modérées avec un F1-score moyen de 0.44. Des développements méthodologiques sont encore nécessaires pour (a) exploiter de manière plus efficiente et directe les données pluri-annuelles, et (b) mettre en place des approches non-supervisées plus efficaces pouvant être testées dans les environnements pauvres en données de terrainFallow mapping in West Africa is essential to accurately assess agricultural systems and its contribution to food security and agro-ecological sustainability of current practices, and yet the available mapping methodologies are not adapted to the environmental and cropping conditions encountered when addressing tropical smallholder agriculture. In this doctoral thesis, we explore different mapping strategies based on supervised classification techniques and making use of Sentinel-2 imagery and rainfall data as input, as well as multiple years of in-situ data to map fallow land at local scale in a Soudanian site in Burkina Faso (Koumbia) between the years 2016 and 2021. Results show that "traditional" machine learning based mapping approaches are not sufficient for detecting fallow land under the given pedoclimatic conditions, resulting in very low accuracy figures (e.g., F1-scores below the 0.2 mark). Most promising results were obtained when following a trajectory analysis approach, where a series of methodological adaptations had to be done to exploit annual data in a multi-year oriented manner. In this last case we reformulate the mapping problem to target non-active agricultural land (NAAL) as whole, obtaining F1-score ranging from 0.75 to 0.92 values when validating against complete (no data gaps) reference data set.Our results show that strategies that incorporate multiple years of spectral data in their learning process as a potential viable approach, where fallow land is not described by current status of land surface (i.e. land cover) but rather by the changes of it along the period that encircles the moment in which crop inactivity begins. However, results also indicate that the spatial application scope might be limited, with an augmentation of model uncertainty in areas where no ground truth data is available, highlighting the need to incorporate unsupervised approaches for enhanced extrapolation. On the other hand more explicit multi-year strategies, where temporal analysis is delegated to model classifiers yielded marginally better results than annual direct mapping strategies, yet performances obtained do not reach satisfying results, with top average F1-score reaching the 0.44 mark. Methodological development is still required for both (a) exploiting more efficiently and direct manner multi-year data, and (b) building more cost-efficient unsupervised solutions that could be tested in areas with a reduce amount of ground truth data
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Denize, Julien. "Evaluation of time-series SAR and optical images for the study of winter land-use". Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S062.
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L'étude de l'utilisation hivernale du sol représente un enjeu majeur afin de préserver et d'améliorer la qualité des sols et des eaux de surfaces. Cependant la connaissance des dynamiques spatio-temporelles associées à l'utilisation du sol en période hivernale demeure aujourd'hui encore un défi pour la communauté scientifique. C'est dans ce contexte que s'inscrivent ces travaux de thèse dont l'objectif est d'évaluer le potentiel de séries temporelles d'images optiques et RSO à haute résolution spatiale pour l'étude de l'utilisation des sols en période hivernale à une échelle locale et régionale. Pour se faire, une méthodologie a été établie afin : (i) de déterminer la méthode de classification la plus adaptée pour identifier l'usage des sols en hiver; (ii) de comparer des images RSO Sentinel-1 et optiques Sentinel-2; (iii) de définir la configuration RSO la plus adaptée en comparant trois séries temporelles d'images (Alos-2, Radarsat-2 et Sentinel-1).Les résultats ont tout d'abord mis en évidence l'intérêt de l'algorithme de classification Random Forest pour discriminer à une échelle fine les types d'usage des sols en hiver qui sont très variés. Dans un second temps, ils ont souligné l'intérêt des données Sentinel-2 pour cartographier l'utilisation hivernale des sols à une échelle locale et régionale. Enfin, ils ont permis de déterminer qu'une série temporelle dense d'images Sentinel-1 était la configuration RSO la plus adaptée afin d'identifier l'utilisation hivernale du sol. De manière générale, si cette thèse a permis de montrer que les données Sentinel-2 sont les plus adaptées pour étudier l'utilisation du sol en période hivernale, les images RSO ont tout leur intérêt dans les régions où le couvert nuageux est important, les séries temporelles denses Sentinel- 1 ayant été définies comme les plus performantesThe study of winter land-use is a major challenge in order to preserve and improve the quality of soils and surface water. However, knowledge of the spatio-temporal dynamics associated with winter land-use remains a challenge for the scientific community. In this context, the objective of this study is to evaluate the potential of time series of high spatial resolution optical and SAR images for the study of winter land-use at a local and regional scale. For that purpose, a methodology has been established to: (i) determine the most suitable classification method for identifying winter land-use ; (ii) compare Sentinel-1 SAR and Sentinel-2 optical images; (iii) define the most suitable SAR configuration by comparing three image time-series (Alos-2, Radarsat-2 and Sentinel-1).The results first of all highlighted the interest of the Random Forest classification algorithm to discriminate at a fine scale the different types of land use in winter. Secondly, they showed the value of Sentinel-2 data for mapping winter land-use at a local and regional scale. Finally, they determined that a dense time series of Sentinel-1 images was the most appropriate SAR configuration to identify winter land-use. In general, while this thesis has shown that Sentinel-2 data are best suited to studying land use in winter, SAR images are of great interest in regions with significant cloud cover, dense Sentinel-1 time-series having being defined as the most efficient
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Bioresita, Filsa. "Exploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau". Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAH004/document.
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Les eaux de surface sont des ressources importantes pour la biosphère et l'anthroposphère. Elles favorisent la préservation des habitats, le développement de la biodiversité et le maintien des services écosystémiques en contrôlant le cycle des nutriments et le carbone à l’échelle mondiale. Elles sont essentielles à la vie quotidienne de l’homme, notamment pour l'irrigation, la consommation d’eau potable, la production hydro-électrique, etc. Par ailleurs, lors des inondations, elles peuvent présenter des dangers pour l'homme, les habitations et les infrastructures. La surveillance des changements dynamiques des eaux de surface a donc un rôle primordial pour guider les choix des gestionnaires dans le processus d’aide à la décision. L’imagerie satellitaire constitue une source de données adaptée permettant de fournir des informations sur les eaux de surface. De nos jours, la télédétection satellitaire a connu une révolution avec le lancement des satellites Sentinel-1 (Radar) et Sentinel-2 (Optique) qui disposent d’une haute fréquence de revisite et d’une résolution spatiale moyenne à élevée. Ces données peuvent fournir des séries temporelles essentielles pour apporter davantage d'informations afin d'améliorer la capacité d'observation des eaux de surface. L’exploitation de telles données massives et multi-sources pose des défis en termes d’extraction de connaissances et de processus de traitement d’images car les chaines de traitement doivent être le plus automatiques possibles. Dans ce contexte, l'objectif de ce travail de thèse est de proposer de nouvelles approches permettant de cartographier l’extension spatiales des eaux de surface et des inondations, en explorant l'utilisation unique et combinée des données Sentinel-1 et Sentinel-2Surface waters are important resources for the biosphere and the anthroposphere. Surface waters preserve diverse habitat, support biodiversity and provide ecosystem service by controlling nutrient cycles and global carbon. Surface waters are essential for human's everyday life, such as for irrigation, drinking-water and/or the production of energy (power plants, hydro-electricity). Further, surface waters through flooding can pose hazards to human, settlements and infrastructures. Monitoring the dynamic changes of surface waters is crucial for decision making process and policy. Remote sensing data can provide information on surface waters. Nowadays, satellite remote sensing has gone through a revolution with the launch of the Sentinel-1 SAR data and Sentinel-2 optical data with high revisit time at medium to high spatial resolution. Those data can provide time series and multi-source data which are essential in providing more information to upgrade ability in observing surface water. Analyzing such massive datasets is challenging in terms of knowledge extraction and processing as nearly fully automated processing chains are needed to enable systematic detection of water surfaces.In this context, the objectives of the work are to propose new (e.g. fully automated) approaches for surface water detection and flood extents detection by exploring the single and combined used of Sentinel-1 and Sentinel-2 data
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Rodes, Arnau Isabel. "Estimation de l'occupation des sols à grande échelle pour l'exploitation d'images d'observation de la Terre à hautes résolutions spatiale, spectrale et temporelle". Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30375/document.
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Les missions spatiales d'observation de la Terre de nouvelle génération telles que Sentinel-2 (préparé par l'Agence Spatiale Européenne ESA dans le cadre du programme Copernicus, auparavant appelé Global Monitoring for Environment and Security ou GMES) ou Venµs, conjointement développé par l'Agence Spatiale Française (Centre National d 'Études Spatiales CNES) et l'Agence Spatiale Israélienne (ISA), vont révolutionner la surveillance de l'environnement d' aujourd'hui avec le rendement de volumes inédits de données en termes de richesse spectrale, de revisite temporelle et de résolution spatiale. Venµs livrera des images dans 12 bandes spectrales de 412 à 910 nm, une répétitivité de 2 jours et une résolution spatiale de 10 m; les satellites jumeaux Sentinel-2 assureront une couverture dans 13 bandes spectrales de 443 à 2200 nm, avec une répétitivité de 5 jours, et des résolutions spatiales de 10 à 60m. La production efficace de cartes d'occupation des sols basée sur l'exploitation de tels volumes d'information pour grandes surfaces est un défi à la fois en termes de coûts de traitement mais aussi de variabilité des données. En général, les méthodes classiques font soit usage des approches surveillées (trop coûteux en termes de travaux manuels pour les grandes surfaces), ou soit ciblent des modèles locaux spécialisés pour des problématiques précises (ne s'appliquent pas à autres terrains ou applications), ou comprennent des modèles physiques complexes avec coûts de traitement rédhibitoires. Ces approches existantes actuelles sont donc inefficaces pour l'exploitation du nouveau type de données que les nouvelles missions fourniront, et un besoin se fait sentir pour la mise en œuvre de méthodes précises, rapides et peu supervisées qui permettent la généralisation à l'échelle de grandes zones avec des résolutions élevées. Afin de permettre l'exploitation des volumes de données précédemment décrits, l'objectif de ce travail est la conception et validation d'une approche entièrement automatique qui permet l'estimation de la couverture terrestre de grandes surfaces avec imagerie d'observation de la Terre de haute résolution spatiale, spectrale et temporelle, généralisable à des paysages différents, et offrant un temps de calcul opérationnel avec ensembles de données satellitaires simulés, en préparation des prochaines missions. Cette approche est basée sur l'intégration d'algorithmes de traitement de données, tels que les techniques d'apprentissage de modèles et de classification, et des connaissances liées à l'occupation des sols sur des questions écologiques et agricoles, telles que les variables avec un impact sur la croissance de la végétation ou les pratiques de production. Par exemple, la nouvelle introduction de température comme axe temporel pour un apprentissage des modèles ultérieurs intègre un facteur établi de la croissance de la végétation à des techniques d'apprentissage automatiques pour la caractérisation des paysages. Une attention particulière est accordée au traitement de différentes questions, telles que l'automatisation, les informations manquantes (déterminées par des passages satellitaires, des effets de réflexion des nuages, des ombres ou encore la présence de neige), l'apprentissage et les données de validation limitées, les échantillonnages temporels irréguliers (différent nombre d'images disponible pour chaque période et région, données inégalement réparties dans le temps), la variabilité des données, et enfin la possibilité de travailler avec différents ensembles de données et nomenclaturesThe new generation Earth observation missions such as Sentinel-2 (a twin-satellite initiative prepared by the European Space Agency, ESA, in the frame of the Copernicus programme, previously known as Global Monitoring for Environment and Security or GMES) and Venµs, jointly developed by the French Space Agency (Centre National d'Études Spatiales, CNES) and the Israeli Space Agency (ISA), will revolutionize present-day environmental monitoring with the yielding of unseen volumes of data in terms of spectral richness, temporal revisit and spatial resolution. Venµs will deliver images in 12 spectral bands from 412 to 910 nm, a repetitivity of 2 days, and a spatial resolution of 10 m; the twin Sentinel-2 satellites will provide coverage in 13 spectral bands from 443 to 2200 nm, with a repetitivity of 5 days, and spatial resolutions of 10 to 60m. The efficient production of land cover maps based on the exploitation of such volumes of information for large areas is challenging both in terms of processing costs and data variability. In general, conventional methods either make use of supervised approaches (too costly in terms of manual work for large areas), target specialised local models for precise problem areas (not applicable to other terrains or applications), or include complex physical models with inhibitory processing costs. These existent present-day approaches are thus inefficient for the exploitation of the new type of data that the new missions will provide, and a need arises for the implementation of accurate, fast and minimally supervised methods that allow for generalisation to large scale areas with high resolutions. In order to allow for the exploitation of the previously described volumes of data, the objective of this thesis is the conception, design, and validation of a fully automatic approach that allows the estimation of large-area land cover with high spatial, spectral and temporal resolution Earth observation imagery, being generalisable to different landscapes, and offering operational computation times with simulated satellite data sets, in preparation of the coming missions
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Shrestha, Anu Bhalu. "Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach". Master's thesis, 2021. http://hdl.handle.net/10362/113706.
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe free and open availability of satellite images covering global extent in recent days provides many novel opportunities for global monitoring of the earth’s surface. Sentinel-2 (S2) and Sentinel-3 (S3) satellite missions capture mid to high resolution imagery with frequent revisit and show data synergy as they both focus on land and ocean observational needs. Specifically, the high temporal resolution of S3 (1-2 day revisit) presents potential in filling the data gaps in S2 (5 day revisit) vegetation products. In this scenario, this study assesses the feasibility of using Sentinel-3 images for Sentinel-2 vegetation products estimation using machine learning (ML) and deep learning (DL) approaches. This study employs four state of the art ML regression algorithms, linear regression, ridge regression, Support Vector Regression (SVR) and Random Forest Regression (RFR) and two DL network architectures with different depth and complexities, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) to predict the S2 NDVI and SAVI maps from the S3 spectral bands information. A paired S2/S3 dataset is prepared for the study area covering one S2 tile in Extremadura, Spain. The results demonstrate that all the DL architectures except pixel-wise MLP outperformed the ML models with the 3D CNN performing the best. The best performing 3D CNN architecture obtained remarkable mean squared error (MSE) of 0.00198 for NDVI and 0.00282 for SAVI while the best performing ML algorithms were patch-wise RFR with MSE of 0.0035 in case of NDVI and patchwise SVR with MSE of 0.00586 for SAVI. The models and the dataset prepared for this study will be useful for further research that focus on capitalizing the free and open availability of Sentinel-2 and Sentinel-3 imagery as well as new and advanced technologies to provide better vegetation monitoring capabilities for our planet.
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Sequeira, Itzá Alejandra Hernández. "Landcover and crop type classification with intra-annual times series of sentinel-2 and machine learning at central Portugal". Master's thesis, 2020. http://hdl.handle.net/10362/93714.
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Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesLand cover and crop type mapping have benefited from a daily revisiting period of sensors such as MODIS, SPOT-VGT, NOAA-AVHRR that contains long time-series archive. However, they have low accuracy in an Area of Interest (ROI) due to their coarse spatial resolution (i.e., pixel size > 250m). The Copernicus Sentinel-2 mission from the European Spatial Agency (ESA) provides free data access for Sentinel 2-A(S2a) and B (S2b). This satellite constellation guarantees a high temporal (5-day revisit cycle) and high spatial resolution (10m), allowing frequent updates on land cover products through supervised classification. Nevertheless, this requires training samples that are traditionally collected manually via fieldwork or image interpretation. This thesis aims to implement an automatic workflow to classify land cover and crop types at 10m resolution in central Portugal using existing databases, intra-annual time series of S2a and S2b, and Random Forest, a supervised machine learning algorithm. The agricultural classes such as temporary and permanent crops as well as agricultural grasslands were extracted from the Portuguese Land Parcel Identification System (LPIS) of the Instituto de Financiamento da Agricultura e Pescas (IFAP); land cover classes like urban, forest and water were trained from the Carta de Ocupação do Solo (COS) that is the national Land Use and Land Cover (LULC) map of Portugal; and lastly, the burned areas are identified from the corresponding national map of the Instituto da Conservação da Natureza e das Florestas (ICNF). Also, a set of preprocessing steps were defined based on the implementation of ancillary data allowing to avoid the inclusion of mislabeled pixels to the classifier. Mislabeling of pixels can occur due to errors in digitalization, generalization, and differences in the Minimum Mapping Unit (MMU) between datasets. An inner buffer was applied to all datasets to reduce border overlap among classes; the mask from the ICNF was applied to remove burned areas, and NDVI rule based on Landsat 8 allowed to erase recent clear-cuts in the forest. Also, the Copernicus High-Resolution Layers (HRL) datasets from 2015 (latest available), namely Dominant Leaf Type (DLT) and Tree Cover Density (TCD) are used to distinguish between forest with more than 60% coverage (coniferous and broadleaf) such as Holm Oak and Stone Pine and between 10 and 60% (coniferous) for instance Open Maritime Pine. Next, temporal gap-filled monthly composites were created for the agricultural period in Portugal, ranging from October 2017 till September 2018. The composites provided data free of missing values in opposition to single date acquisition images. Finally, a pixel-based approach classification was carried out in the “Tejo and Sado” region of Portugal using Random Forest (RF). The resulting map achieves a 76% overall accuracy for 31 classes (17 land cover and 14 crop types). The RF algorithm captured the most relevant features for the classification from the cloud-free composites, mainly during the spring and summer and in the bands on the Red Edge, NIR and SWIR. Overall, the classification was more successful on the irrigated temporary crops whereas the grasslands presented the most complexity to classify as they were confused with other rainfed crops and burned areas.
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Silva, Nuno Alexandre Pereira da. "Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2". Master's thesis, 2021. http://hdl.handle.net/10362/122980.
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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe need of timely and accurate information for the territory has increased over the years, making Land Cover Land Use (LCLU) mapping one of the most common application of remote sensing. Recently, the advances in satellite technology and the open access policies for remote sensing data increased the interest in exploring satellite image time series. In addition, the attention of researchers has shifted from standard machine learning algorithms (e.g., Support Vector Machines and Random Forest) to Recurrent Neural Networks due to their ability of exploiting sequential information. However, acquiring reference data to train these algorithms is still a hurdle. This study aims to evaluate the capability of a Gated Recurrent Unit in performing pixel-level LCLU classification of a satellite image time series, using Sentinel-2 imagery and having the LUCAS survey as reference data. To assess the performance of our model we compared it to state-of-the-art classifiers (SVM and RF). Due to the unbalance nature of the LUCAS survey, we applied oversampling to this dataset to increase the performance of our models, testing three different oversampling techniques. The results attained showed that Recurrent Neural Networks did not outperform the other state-of-the-art algorithms, when trained with a limited number of sampling units, and that oversampling the LUCAS survey increased the performance of all the classifiers. Finally, we were able to demonstrate that it is possible to produce LCLU classification of satellite image time series using only open-source data by using Sentinel-2 imagery and the LUCAS survey as refence data.
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Di, Paolo Luciano Elías. "Clasificación de cultivos en la provincia de Buenos Aires mediante la utilización de imágenes SAR e imágenes ópticas". Master's thesis, 2017. http://hdl.handle.net/11086/5828.
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Tesis (Magister en Aplicaciones Espaciales de Alerta y Respuesta Temprana a Emergencias)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2017.Maestría conjunta entre FAMAF y el Instituto de Altos Estudios Espaciales "Mario Gulich" CONAE/UNC.
La tesis de maestría presenta tres aplicaciones obtenidas a partir de información satelital que son de interés de la administración fiscal de la Provincia de Buenos Aires: La detección remota de cultivos y estimación de su superficie cultivada, la clasificación supervisada de cultivos a través de imágenes satelitales ópticas y por último, la utilización de imágenes SAR (Radar de Apertura Sintética) para clasificar cultivos. Se utilizaron series temporales de imágenes SAR Cosmo SkyMed, Sentinel-1 A y Landsat 8 – OLI, para clasificar de manera supervisada cultivos de interés en la Provincia de Buenos Aires. Se probaron distintas combinaciones de imágenes SAR y Landsat 8 para clasificar cultivos. Se utilizaron los clasificadores de Máxima verosimilitud, Árboles de decisión (DT), “Random Forest”, “Gradient Boosted Tree”, “Support Vector Machine”, “Neural Network” para clasificar imágenes SAR con el objetivo de confeccionar mapas de cultivos en tres zonas de la provincia de Buenos Aires. Se obtuvieron precisiones de entre 89% y 92% en todas las zonas de estudio. Las clasificaciones sobre imágenes SAR obtuvieron mejores precisiones con clasificadores no paramétricos en dos de tres casos. El clasificador “Random Forest” presentó el mejor desempeño. Por último, se ha propuesto una metodología de trabajo para incorporar imágenes SAR a los productos cartográficos de la agencia de Recaudación de la provincia de Buenos Aires.
Fil: Di Paolo, Luciano Elías. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Di Paolo, Luciano Elías. Universidad Nacional de Córdoba - Comisión Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina.
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Laštovička, Josef. "Hodnocení lesní vegetace pomocí časových řad družicových snímků". Doctoral thesis, 2020. http://www.nusl.cz/ntk/nusl-435801.
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Příloha k disertační práci: Abstrakt v AJ (Mgr. Josef Laštovička) Abstract This dissertation thesis deals with the study of forest ecosystems in the central Europe with the time series of multispectral optical satellite data. These forest ecosystems have been influenced by biotic and abiotic disturbances for the last decade. The time series of the satellite data with high spatial resolution allow the detection and analysis of forest disturbances. This thesis is mainly focused primally on free available Landsat and Sentinel-2 data, these two data types were compared. From methods, the difference time series analyses / algorithms were used. The whole thesis can be divided into two main parts. The first one analyses usability of classifiers for detection of forest ecosystems with per-pixel and sub-pixel methods. Specifically, the Neural Network, the Support Vector Machine and the Maximum Likelihood per-pixel classifiers were used and compared for different types of data (for data with high spatial resolution - Landsat or Sentinel-2; very high spatial resolution - WorldView-2) and for classification of protected forest areas. The Support Vector Machine were selected as the most suitable method for forest classifications (with most accurate outputs) from the list of selected per-pixel classifiers. Also, Spectral...
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Kuthan, Tomáš. "Klasifikace vybraných zemědělských plodin v modelovém území Kutnohorska s využitím časové řady dat Sentinel-2 a PlanetScope". Master's thesis, 2019. http://www.nusl.cz/ntk/nusl-392614.
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Classification of selected agricultural crops from time series of Sentinel-2 and PlanetScope imagery in Kutnohorsko model area Abstract The thesis is focused on the analysis of spectral characteristics of selected agricultural crops druring agriculutural season from time series of Sentinel -2 (A and B) and PlanetScope sensors in the model area situated around the settlements of Kolín and Kutná Hora. It is based on the assumption that the use of multiple dates of image data acquired crops in different phenological phases of the crops allows better identification of crop species (Lu et al., 2004). The aim of the thesis was to analyse the characteristics of the seasonal course of spectral features of selected agricultural crops (sugar beet, spring barley, winter barley, maize, spring wheat, winter wheat, winter rape) and to determine the period of the year suitable for the differentiation of individual crops. Another aim of the thesis was to classify these crops in the model area from time series of two above-mentioned sensors and to compare the accuracy of the pixel and object-oriented classification approach for multitemporal composites and the accuracy for monotemporal image from the term when the individual crops are clearly distinguishable. The training and validation datasets and the classification mask...
Książki na temat "The Sentinels series"
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Seletz, Jules M. Sentinel event Southern style: A mystery/medical thriller novel, seventh in the Jake Stein Sentinel Event series. [Unted States]: BookSurge Publishing, 2008.
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Conn, L. C. Sentinels: Book 1 Part 1 of the One True Child Series. Independently Published, 2017.
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Conn, L. C. Sentinels: Book 1 Part 2 of the One True Child Series. Independently Published, 2017.
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Comics, Gwandanaland. Calling down the Thunderbolt: Gwandanaland Comics #537 --- the Classic Complete Charlton Series - Guest-Starring the Sentinels! Gwandanaland Comics, 2022.
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Calling Down The Thunderbolt : Gwandanaland Comics #537-HC: The Classic Complete Charlton Series - Guest-Starring The Sentinels! Gwandanaland Comics, 2022.
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Bostic, Yvette. Sentinel's Rise: Book 1 - the Watcher and the Sentinel Series. Independently Published, 2019.
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Comics, Gwandanaland. Calling Down The Thunderbolt : B&W Readers Collection - Gwandanaland Comics #537-A: The Classic Complete Charlton Series - Guest-Starring The Sentinels! Gwandanaland Comics, 2022.
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Group, Jane's Information. Jane's Sentinel (Jane's Sentinel Series). Jane's Information Group, 1995.
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Group, Jane's Information. Jane's Sentinel (Jane's Sentinel Series). Jane's Information Group, 1994.
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Jane's Sentinel Security Assessments (Jane's Sentinel Series). Wyd. 2. Jane's Information Group, 1995.
Znajdź pełny tekst źródłaCzęści książek na temat "The Sentinels series"
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Tang, Xiaojing. "Deforestation Viewed from Multiple Sensors". W Cloud-Based Remote Sensing with Google Earth Engine, 1093–120. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_50.
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AbstractCombining data from multiple sensors is the best way to increase data density and hence detect change faster. The purpose of this chapter is to demonstrate a simple method of combining Landsat, Sentinel-2, and Sentinel-1 data for monitoring tropical forest disturbance. You will learn how to import, preprocess, and fuse optical and synthetic aperture radar (SAR) remote sensing data. You will also learn how to monitor change using time-series models.
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Ackman, Robert G., i Shengying Zhou. "Natural and Waterborne Petroleum Hydrocarbons in Marine Sentinel Blue Mussels". W ACS Symposium Series, 69–82. Washington, DC: American Chemical Society, 2003. http://dx.doi.org/10.1021/bk-2003-0848.ch006.
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Gomes da Silva, Paula, Anne-Laure Beck, Jara Martinez Sanchez, Raúl Medina Santanmaria, Martin Jones i Amine Taji. "Advances on coastal erosion assessment from satellite earth observations: exploring the use of Sentinel products along with very high resolution sensors". W Proceedings e report, 412–21. Florence: Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-147-1.41.
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This work proposes the use of automatic co-registered satellite images to obtain large, high frequency and highly accurate shorelines time series. High resolution images are used to co-register Landsat and Sentinel-2 images. 90% of the co-registered images presented vertical and horizontal shift lower than 3 m. Satellite derived shorelines presented errors lower than mission’s precision. A discussion is presented on the applicability of those shorelines through an application to Tordera Delta (Spain).
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Sarzotti, Ettore, Gianmarco Pignocchino, Alessandro Pezzoli i Angelo Besana. "NO2 Concentrations and COVID-19 in Local Systems of Northwest Italy". W The Urban Book Series, 83–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33894-6_7.
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AbstractThis paper aims to further investigate the relationship between the concentrations of nitrogen dioxide (NO2) and the severity of COVID-19 by analyzing the data of three Italian Regions (Piedmont, Valle d’Aosta and Liguria) during the first wave of the pandemic (February–May 2020). The analyses were conducted at a local scale using Local Labor Systems of ISTAT. The annual average of NO2 concentrations, obtained from space satellite Sentinel-5P, was used to assess environmental data. While excess mortality data were used to estimate the severity of the pandemic, calculated as the percentage change in deaths recorded in 2020 compared to the average number of deaths of the previous five years (2015–2019). Using quasi-Poisson multivariate regression models, it was possible to estimate the correlation between the incidence rate of the pandemic and some risk factors, including in particular the concentration of NO2.
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Gou, Jisong, Xianlin Shi, Keren Dai, Leyin Hu i Peilian Ran. "Revealing Land Subsidence in Beijing by Sentinel-1 Time Series InSAR". W Advances in Intelligent Systems and Computing, 622–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2568-1_85.
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Deines, Jillian M., Stefania Di Tommaso, Nicholas Clinton i Noel Gorelick. "Scaling up in Earth Engine". W Cloud-Based Remote Sensing with Google Earth Engine, 575–602. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_29.
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AbstractCommonly, when Earth Engine users move from tutorials to developing their own processing scripts, they encounter the dreaded error messages, “computation timed out” or “user memory limit exceeded”. Computational resources are never unlimited, and the team at Earth Engine has designed a robust system with built-in checks to ensure that server capacity is available to everyone. This chapter will introduce general tips for creating efficient Earth Engine workflows that accomplish users’ ambitious research objectives within the constraints of the Earth Engine ecosystem. We use two example case studies: (1) extracting a daily climate time series for many locations across two decades and (2) generating a regional, cloud-free median composite from Sentinel-2 imagery.
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Revathy, R., R. Setia, Sandeep Jain, Sreeja Das, Sharad Gupta i Brijendra Pateriya. "Classification of Potato in Indian Punjab Using Time-Series Sentinel-2 Images". W Lecture Notes in Electrical Engineering, 193–201. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7698-8_20.
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Marghany, Maged. "Four-Dimensional Covid-19 Simulation in Slums Using Hologram Interferometry of Sentinel-1A—Satellite". W The Urban Book Series, 167–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71945-6_10.
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Poggi, Francesco, Roberto Montalti, Emanuele Intrieri, Alessandro Ferretti, Filippo Catani i Federico Raspini. "Spatial and Temporal Characterization of Landslide Deformation Pattern with Sentinel-1". W Progress in Landslide Research and Technology, Volume 2 Issue 1, 2023, 321–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39012-8_15.
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AbstractThe results of multi-interferometric processing applied to radar images acquired by the European Space Agency (ESA) Sentinel-1 constellation, obtained in the framework of the IPL (International Programme on Landslides) project n.221, are presented in this manuscript. The target area is the Hunza-Nagar River valley, a remote area in northern Pakistan. The Humarri slide, a massive landslide located along the left side of the valley, is the most active phenomenon in the area, and poses a very high risk to the Humarri village built in the lowest part of the landslide, and a threat of damming the Hispar River. The highest deformation rates (with peaks higher than 300 mm/yr) were recorded in the middle and low part of the landslide. Where the Humarri village is located, the velocity values are lower and range between −30 and −80 mm/yr. The analysis of displacement time series, spanning from February 2017 to January 2020, reveal different deformation behaviors and different onset of accelerating behaviors within the landslide body. The global coverage, the wide-area mapping capabilities, and the regular acquisitions planning ensured by the Sentinel-1 constellation, coupled with InSAR ability to deliver very precise and spatially dense information, make it possible to scan wide areas and spot unstable zones, especially where remoteness, vast areal extension, and climatic conditions make it difficult to undertake field activities.
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Hardy, Tom, Marston Domingues Franceschini, Lammert Kooistra, Marcello Novani i Sebastiaan Richter. "Automated Processing of Sentinel-2 Products for Time-Series Analysis in Grassland Monitoring". W IFIP Advances in Information and Communication Technology, 48–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39815-6_5.
Pełny tekst źródłaStreszczenia konferencji na temat "The Sentinels series"
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Bazzi, Hassan, Nicolas Baghdadi, Dino Ienco, Mehrez Zribi i Hatem Belhouchette. "Irrigation Mapping Using Sentinel-1 Time Series". W IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9324358.
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Bazzi, Hassan, Nicolas Baghdadi i Mehrez Zribi. "Operative Mapping of Irrigated Areas Using Sentinel-1 and Sentinel-2 Time Series". W IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884653.
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Mattia, Francesco, Anna Balenzano, Giuseppe Satalino, Davide Palmisano, Annarita D'Addabbo i Francesco Lovergine. "Field Scale Soil Moisture From Time Series Of Sentinel-1 & Sentinel-2". W 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS). IEEE, 2020. http://dx.doi.org/10.1109/m2garss47143.2020.9105290.
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Stasolla, Mattia, Sophie Petit, Coraline Wyard, Gerard Swinnen, Xavier Neyt i Eric Hallot. "Urban Sites Change Detection by Means of Sentinel-1 and Sentinel-2 Time Series". W IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9555060.
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Prats-Iraola, Pau, Matteo Nannini, Nestor Yague-Martinez, Rolf Scheiber, Federico Minati, Francesco Vecchioli, Mario Costantini i in. "Sentinel-1 tops interferometric time series results and validation". W IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730011.
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Ehret, T., A. De Truchis, M. Mazzolini, J. M. Morel i G. Facciolo. "Automatic Methane Plume Quantification Using Sentinel-2 Time Series". W IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884134.
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Chehata, Nesrine, Hedia Chakroun, Rania Youssfi, Mohamed Amine Maaoui, Anis Manai, Rami Werhani, Kamel Aloui, Nizar Kouki, Wafa Talhaoui i Thouraya Sahli. "Improving Forest Species Mapping Using Sentinel-2 Time Series". W 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS). IEEE, 2020. http://dx.doi.org/10.1109/m2garss47143.2020.9105159.
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Gao, Q., M. Zribi, M. J. Escorihuela, N. Baghdadi i P. Quintana-Segui. "Irrigation Mapping Using Statistics of Sentinel-1 Time Series". W IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8518609.
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Dostovalov, Mikhail, Roman Ermakov i Thomas Moussiniants. "Detection of Aircraft Using Sentinel-1 SAR Image Series". W 2018 19th International Radar Symposium (IRS). IEEE, 2018. http://dx.doi.org/10.23919/irs.2018.8448147.
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Dmitriev, A. V., T. N. Chimitdorzhiev, P. N. Dagurov i I. I. Kirbizhekova. "Optical and microwave observation of forest restoration after abnormal fires". W Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.12.58.008.
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The paper discusses the possibility of a forest undergrowth detecting with help of polarimetric decompositions and time series analysis of ALOS-2/PALSAR-2 and Sentinel-1 radar images. The presented results show principal possibility of confidently identifying the growth of forest stands.
Raporty organizacyjne na temat "The Sentinels series"
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Bingham, Sonia, i Craig Young. Sentinel wetlands in Cuyahoga Valley National Park: I. Ecological characterization and management insights, 2008–2018. Redaktor Tani Hubbard. National Park Service, luty 2023. http://dx.doi.org/10.36967/2296885.
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Sentinel wetlands at Cuyahoga Valley National Park (NP) comprise a set of twenty important management areas and reference sites. These wetlands are monitored more closely than other wetlands in the wetlands monitoring program and are the focus of the volunteer monitoring program for water levels. We used the Ohio Rapid Assessment Method (ORAM) to evaluate habitat in the sentinel wetlands. A total of 37 long-term sample plots have been established within these wetlands to monitor biological condition over time using vegetation as an indicator. Vegetation is intensively surveyed using the Vegetation Index of Biotic Integrity (VIBI), where all plant species within the plot are identified to the lowest taxonomic level possible (genus or species). Sample plots were surveyed twice from 2008 to 2018 and the vegetation data were evaluated using five metrics: VIBI, Floristic Quality Assessment Index (FQAI), percent sensitive plant species, percent invasive graminoids, and species richness. These metrics are discussed for each location. This report also highlights relevant land use histories, common native plant species, and invasive species of concern at each wetland. This is the first report in a two-part series, designed to summarize the results from intensive vegetation surveys completed at sentinel wetlands in 2008–2018. Boston Mills, Virginia Kendall Lake, Stumpy Basin, Columbia, and Beaver Marsh are all in excellent condition at one or more plots. They have unique habitats with some specialized plant species. Fawn Pond is in good condition at most plots and scores very high in comparison to other wetlands within the riverine mainstem hydrogeomorphic class. Metric scores across mitigation wetlands were low. Two of the three wetlands (Brookside and Rockside) are not meeting the benchmarks originally established by the United States Army Corps of Engineers and Ohio Environmental Protection Agency. Krejci is still a young mitigation site and success will be determined over time. Park-supported invasive species control efforts will be crucial for long-term success of these sites and future mitigation/restoration projects. The wetlands monitored because of proposed ecological restoration projects (Pleasant Valley, Stanford, and Fawn Pond) have extensive invasive plant communities. These restoration sites should be re-evaluated for their feasibility and potential success and given an order of prioritization relative to the newer list of restoration sites. Cuyahoga Valley NP has added many new areas to their list of potential wetland restoration sites after these areas were selected, and there may be better opportunities available based on restoration objectives. Restoration goals should be based on the park's desired future conditions, and mitigation goals of outside partners may not always be in line with those. The multiple VIBI plots dispersed throughout the large wetlands at Cuyahoga Valley NP detected and illuminated spatial patterns in condition. Many individual wetlands had a wide range of VIBI scores within their boundaries, sometimes reflecting localized disturbances, past modifications, and management actions. Most often, these large fluctuations in condition were linked to local invasive plant infestations. These infestations appear to be the most obvious and widespread threat to wetland ecosystems within the park, but also the most controllable threat. Some sensitive species are still present in some of the lowest scoring plots, which indicates that invasive plant species control efforts may pay off immediately with a resurgence of native communities. Invasive plant control at rare habitat sites would have large payoffs over time by protecting some of the park's most unique wetlands. Reference wetlands would also be good demonstration sites for park managers to try to maintain exemplary conditions through active management. Through this work, park managers can evaluate the feasibility, effectiveness, and scalability of management practices required to maintain wetland condition.
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Saltus, Christina, Molly Reif i Richard Johansen. waterquality for ArcGIS Pro Toolbox. Engineer Research and Development Center (U.S.), październik 2021. http://dx.doi.org/10.21079/11681/42240.
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Monitoring water quality of small inland lakes and reservoirs is a critical component of USACE water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs that cover vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an ESRI ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) that was founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available 1) to automate the download of Sentinel-2 Level-2A imagery, 2) to create stacked image with options for cloud and non-water features masks, 3) to apply water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and 4) to create linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user's guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool's inputs, outputs, and troubleshooting guidance.
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Saltus, Christina, Molly Reif i Richard Johansen. waterquality for ArcGIS Pro Toolbox : user's guide. Engineer Research and Development Center (U.S.), wrzesień 2022. http://dx.doi.org/10.21079/11681/45362.
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Monitoring water quality of small inland lakes and reservoirs is a critical component of the US Army Corps of Engineers (USACE) water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs covering vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an Environmental Systems Research Institute (ESRI) ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available: (1) automating the download of Sentinel-2 Level-2A imagery, (2) creating stacked image with options for cloud and non-water features masks, (3) applying water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and (4) creating linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user’s guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool’s inputs, outputs, and troubleshooting guidance.
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Bingham, Sonia, Craig Young i Tanni Hubbard. Sentinel wetlands in Cuyahoga Valley National Park: II. Condition trends for wetlands of management concern, 2008?2018. National Park Service, 2023. http://dx.doi.org/10.36967/2301705.
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Twenty important management areas (wetlands of management concern) and reference wetlands compose the sentinel wetlands at Cuyahoga Valley National Park. These wetlands are monitored more intensively than other wetlands in the program. This is the second report in a two-part series, designed to summarize the results from intensive vegetation surveys completed at sentinel wetlands from 2008 to 2018. The first report (Bingham and Young 2023) characterized the conditions in each wetland and provided baseline reference information for other reports and site-specific projects. In this report, we examine results from five selected metrics more closely within and across three natural wetlands of management concern groups (restoration wetlands, mitigation wetlands, and rare habitat wetlands) using the reference wetlands as overall benchmarks. We used the Ohio Rapid Assessment Method (ORAM) to evaluate habitat in the sentinel wetlands. In addition, a total of 37 long-term sample plots were established within these wetlands to monitor biological conditions over time using vegetation as an indicator. Multiple plots were located in larger wetland complexes to capture spatial differences in condition. Vegetation was intensively surveyed within the plots using the Vegetation Index of Biotic Integrity (VIBI), where all plant species are identified to the lowest taxonomic level possible (genus or species). The sample plots were surveyed twice, and the five evaluation metrics included the VIBI score, Floristic Quality Assessment Index (FQAI), percent sensitive plant species, percent invasive graminoids, and species richness. For the analysis, VIBI plot locations were rank ordered based on their 2018 scores, the range and average for each metric was examined across the wetlands of management concern groups and plotted against reference wetlands for comparison, and the two survey years (pre-2015 and 2018) were plotted against each other for substantial changes from the established baseline. Across the sample plot locations, VIBI scores ranged from a low of 7 (Stanford Run SF1) to a high of 91 (Columbia Run 554). The top scoring plots were at four reference wetlands (Stumpy Basin 526, Virginia Kendall Lake 241K, Columbia Run 554, and Boston Mills 683) and one rare habitat wetland (Beaver Marsh BM3). All of these plots fell within an excellent condition range in one or both survey years. They each have unique habitats with some specialized plant species. The majority (24) of the sentinel wetlands plots ranked within the poor or fair ranges. These include the three mitigation wetlands: Brookside 968, Rockside RS2, and Krejci, as well as all plots within the Pleasant Valley and Stanford Run wetlands. Most of the large wetlands had dramatic condition differences within their boundaries? effected by pollution sources, land-use modifications, and/or invasive species in some areas more than others. We documented these wide condition ranges at Fawn Pond, Virginia Kendall Lake, Beaver Marsh and Stumpy Basin, but the most pronounced within-wetland differences were at Virginia Kendall Lake, which had a 58-point difference between the highest and lowest scoring plot. Fawn Pond is in good condition at most plots and scored very high in comparison to other wetlands within the riverine mainstem hydrogeomorphic class. The average and range of most metric scores were notably different across the four different wetlands groups. Average values at rare habitat wetlands plots were similar to reference plots for VIBI and FQAI scores, percent invasive graminoids, and percent sensitive metrics. Krejci KR1 and Fawn Pond FP3 had unusually high percent cover of sensitive species (31.0% and 27.9%, respectively) for the mitigation and restoration groupings. However, average overall metric scores across the restoration and mitigation wetlands were generally very low, with Stanford Run being the lowest scoring restoration wetland and Brookside being the lowest scoring mitigation wetland. With restoration efforts completed, the expectation is that mitigation wetlands should be performing much higher. Two of the three mitigation wetlands sites are not meeting the mitigation benchmarks that were created for them by the US Army Corp of Engineers and the Ohio Environmental Protection Agency. Contractor reports state that the wetlands met the criteria within the first five years of establishment. However, upon release from monitoring and maintenance, invasive species have gradually re-established, which has led to condition deterioration over time, and lower metric scores. VIBI scores stayed the same or improved (only slightly in many cases) in the majority of plots (67.6%) between survey years. The Krecji mitigation wetlands had the largest improvement in VIBI scoring. Scores at six plots decreased by at least 10 points from the baseline survey. Two of the park?s most beloved wetlands, Beaver Marsh (at one location) and the Stumpy Basin reference plot, had the two most notable declines in VIBI scores. In 2018, 11 plots (29.7%) had greater than 25% invasive graminoid cover (e.g. cattail, common reed grass, reed canary grass) and 18 plots (48.7%) experienced an increase in invasive graminoid cover between survey years. A marked increase (>10% cover) in invasive graminoids was documented at eight locations (Rockside 1079RS2, Beaver Marsh BM5, Fawn Pond FP3 and FP4, Brookside 968, Stumpy Basin SB1, and two other Pleasant Valley plots: 1049 and 969). These trends are likely to continue, and biological conditions are expected to deteriorate at these wetlands in response. Regardless of invasive species increases, many of the wetlands showed remarkable resilience over the last decade with fairly stable VIBI categories.
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Schweiger, E., Joanna Lemly, Dana Witwicki, Kirk Sherrill, Ruth Whittington, Lewis Messner, Erin Cubley, Katherine Haynes i Sonya Daw. Florissant Fossil Beds National Monument wetland ecological integrity: 2009?2019 synthesis report. National Park Service, 2023. http://dx.doi.org/10.36967/2300778.
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Wetlands at Florissant Fossil Beds National Monument (FLFO) are important because they are biodiversity hotspots and support iconic wildlife. They also provide valuable ?ecosystem services? such as attenuating floods, storing water, recharging aquifers, stabilizing and sequestering sediment, storing carbon, enhancing water quality, and cycling nutrients. This report summarizes 11 years (2009?2019) of wetland monitoring in three sentinel wetland complexes in FLFO. Monitoring included annual samples of 10 wet meadow and fen wetland sites in these complexes. We partition the data into a baseline period encompassing the first three years of our work for which we estimate conditions using a variety of indicators, or vital signs. We then estimate trend across the whole period of record. Results are compared to assessment points that allow us to interpret results in a management relevant context. We collected data on vascular plant species composition, woody species stand structure and damage and mortality of woody stems, soil chemistry, groundwater chemistry, and depth to water at shallow groundwater wells in each complex. We also documented human disturbance in and around each wetland complex. Projections suggest that climate change will likely reduce the number and extent of wetlands in the park and cause declines in the condition of associated flora and fauna and critical ecological functions, such as carbon and water storage. Human use can cause disturbance to wetlands across multiple scales and has well documented relationships with wetland condition. Wetlands at FLFO were impacted by over a century of cattle ranching and other human uses that increased soil erosion, altered hydrology, and made the area more susceptible to invasion by exotic plant species. Disturbance indicators at both a landscape and smaller scale generally showed an intermediate level of disturbance in and around our sentinel sites. Across all sites and indicators, we generally saw lower groundwater levels in response to drought conditions in 2010?2013 and in 2017?2018. However, groundwater at the Hornbek complex responded differently, potentially benefiting from a restoration project that filled incised channels in late summer 2012. Vegetation indicators showed relatively stable and healthy conditions in the park, except for native species cover, which was in intermediate condition.