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Observation can offer a simultaneous and continuous view of the urban thermal environment with minimum cost and form the basis for the estimation of several environmental parameters relevant to UHIs (Keramitsoglou ... Observation can offer a simultaneous and continuous view of the urban thermal environment with minimum cost and form the basis for the estimation of several environmental parameters relevant to UHIs (Keramitsoglou ...doi:10.3390/su9061040 fatcat:g34oj74tjzezzdllhnxlr45xpm
A fully automated system for the identification of possible oil spills present on Synthetic Aperture Radar (SAR) satellite images based on artificial intelligence fuzzy logic has been developed. Oil spills are recognized by experts as dark patterns of characteristic shape, in particular context. The system analyzes the satellite images and assigns the probability of a dark image shape to be an oil spill. The output consists of several images and tables providing the user with all relevantdoi:10.1016/j.envsoft.2004.11.010 fatcat:vz3ockuk2fctxoza2uakxvihle
more »... ation for decision-making. The case study area was the Aegean Sea in Greece. The system responded very satisfactorily for all 35 images processed. The complete algorithmic procedure was coded in MS Visual CCC 6.0 in a stand-alone dynamic link library (dll) to be linked with any sort of application under any variant of MS Windows operating system.
The Urban Heat Island (UHI) is an adverse environmental effect of urbanization that increases the energy demand of cities and impacts human health. The study of this effect for monitoring and mitigation purposes is crucial, but it is hampered by the lack of high spatiotemporal temperature data. This article presents the work undertaken for the implementation of an operational real-time module for monitoring 2 m air temperature (TA) at a spatial resolution of 1 km based on the Meteosat Seconddoi:10.3390/rs8040306 fatcat:qjjtijw2ljdwbmkbs252abmljq
more »... eration-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This new module has been developed in the context of an operational system for monitoring the urban thermal environment. The initial evaluation of TA products against meteorological in situ data from 15 cities in Europe and North Africa yields that its accuracy in terms of Root Mean Square Error (RMSE) is 2.3˝C and Pearson's correlation coefficient (Rho) is 0.95. The temperature information made available at and around cities can facilitate the assessment of the UHIs in real time but also the timely generation of relevant higher value products and services for energy demand and human health studies. The service is available at
Intensification of extreme temperatures combined with other socioeconomic factors may exacerbate human thermal risk. The disastrous impacts of extreme weather during the last two decades demonstrated the increased vulnerability of populations even in developed countries from Europe, which are expected to efficiently manage adverse weather. The study aims to assess trends in the exposure of European populations to extreme weather using updated historical climatic data in large European cities ofdoi:10.3390/atmos10080436 fatcat:bd2cdwdgwzb6zgdad4qoexn6zm
more »... different local climates and a set of climatic and bioclimatic indices. Colder cities experience higher warming rates in winter (exceeding 1 °C/decade since the mid-1970s) and warmer cities in summer. Hot extremes have almost tripled in most cities during the last two or three decades with simultaneous advancing of hot weather, while northernmost cities have experienced an unprecedented increase in the heat waves frequency only during the last decade. Bioclimatic indices suggested a robust tendency towards less cold-related stress (mainly in cold cities) and more heat-related stress in all cities. A doubling or tripling in the frequency of heat-related 'great discomfort' was found in southern cities, while in the cities of northern Europe, heat-related 'discomfort' conditions are becoming increasingly more frequent and have nearly quadrupled during the last decade.
In response to the urging need of fire managers for timely information on fire location and extent, the SITHON system was developed. SITHON is a fully digital thermal imaging system, integrating INS/GPS and a digital camera, designed to provide timely positioned and projected thermal images and video data streams rapidly integrated in the GIS operated by Crisis Control Centres. This article presents in detail the hardware and software components of SITHON, and demonstrates the first encouragingdoi:10.3390/s90201204 pmid:22399963 pmcid:PMC3280855 fatcat:ltnkxdzfw5htleoncjw4wzi6iy
more »... results of test flights over the Sithonia Peninsula in Northern Greece. It is envisaged that the SITHON system will be soon operated onboard various airborne platforms including fire brigade airplanes and helicopters as well as on UAV platforms owned and operated by the Greek Air Forces.
Greece is a high risk Mediterranean country with respect to wildfires. This risk has been increasing under the impact of climate change, and in summer 2007 approximately 200,000 ha of vegetated land were burnt. The SEVIRI sensor, on board the Meteosat Second Generation (MSG) geostationary satellite, is the only spaceborne sensor providing five and 15-minute observations of Europe in 12 spectral channels, including a short-wave infrared band sensitive to fire radiative temperature. In Augustdoi:10.3390/rs3030524 fatcat:nvzeu4sldjfuplhparocpg6i2e
more »... , when the bulk of the destructive wildfires started in Greece, the receiving station, operated by the Institute for Space Applications and Remote Sensing, provided us with a time series of MSG-SEVIRI images. These images were processed in order to test the reliability of a real-time detection and tracking system and its complementarity to conventional means provided by the Fire Brigade. EUMETSAT's Active Fire Monitoring (FIR) image processing algorithm for fire detection and monitoring was applied to SEVIRI data, then fine-tuned according to Greek conditions, and evaluated. Alarm announcements from the Fire Brigade's archives were used as ground truthing data in order to assess detection reliability and system performance. During the examined period, MSG-SEVIRI data successfully detected 82% of the fire events in Greek territory with less than 1% false alarms.
Journal of Climate
The Long-Term Coupling between Column Ozone and Tropopause Properties COSTAS VAROTSOS, COSTAS CARTALIS, ANDREW VLAMAKIS, CHRIS TZANIS, AND IPHIGENIA KERAMITSOGLOU Department of Applied Physics, University ...doi:10.1175/1520-0442(2004)017<3843:tlcbco>2.0.co;2 fatcat:4clxj7zhrfczda3z7s6pkin4ja
Iphigenia Keramitsoglou and Chris T. Kiranoudis monitored the experimental process and produced the SEVIRI LST data. ... predictor set comprising VIs, albedo, emissivity, land cover, slope, aspect, and the sky view factor data to downscale LST images retrieved from SEVIRI (Spinning Enhanced Visible and Infrared Imager), while Keramitsoglou ...doi:10.3390/rs9010023 fatcat:25ujvpt6ojc5rolrkgiyugkkni
Keramitsoglou et al. / Remote Sensing of Environment 115 (2011) 3080-3090 ...doi:10.1016/j.rse.2011.06.014 fatcat:tghqehwo5jbknmjy64mogdw22m
This paper presents the results of an operational nationwide burnt area mapping service realized over Greece for the years 2007-2011, through the implementation of the so-called BSM_NOA dedicated method developed at the National Observatory of Athens for post-fire recovery management. The method exploits multispectral satellite imagery, such as Landsat-TM, SPOT, FORMOSAT-2, WorldView and IKONOS. The analysis of fire size distribution reveals that a high number of fire events evolve to large anddoi:10.3390/s130811146 pmid:23966201 pmcid:PMC3812647 fatcat:344opttzgvar5cuqda5gpbzcue
more »... extremely large wildfires under favorable wildfire conditions, confirming the reported trend of an increasing fire-severity in recent years. Furthermore, under such conditions wildfires affect to a higher degree areas at high altitudes, threatening the existence of ecologically significant ecosystems. Finally, recent socioeconomic changes and land abandonment has resulted in the encroachment of former agricultural areas of limited productivity by shrubs and trees, resulting both in increased fuel availability and continuity, and subsequently increased burnability.
<p><strong>Abstract.</strong> This study focuses on the assessment of surface solar radiation (SSR) based on operational neural network (NN) and multi-regression function (MRF) modelling techniques that produce instantaneous (in less than 1<span class="thinspace"></span>min) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite and the Copernicus Atmospheredoi:10.5194/amt-11-907-2018 fatcat:dm6zfryqsndkjphzor6bvyki2y
more »... oring Service (CAMS), respectively, these models are capable of calculating SSR in high resolution (1<span class="thinspace"></span>nm, 0.05°, 15<span class="thinspace"></span>min) that can be used for spectrally integrated irradiance maps, databases and various applications related to energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15<span class="thinspace"></span>min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range −15 to 15<span class="thinspace"></span>% for the NN that produces spectral irradiances (NNS), 5–6<span class="thinspace"></span>% underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from −100 to 40 and −20 to 20<span class="thinspace"></span>W<span class="thinspace"></span>m<sup>−2</sup>, for the 15<span class="thinspace"></span>min and monthly mean global horizontal irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10<span class="thinspace"></span>% as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications which are related to solar energy production planning and use.</p>
Heat waves are projected to become more frequent, longer-lasting, and intense. At the same time, urban areas are confronted with the urban heat island (UHI) phenomenon, which adds to the thermal stress experienced during hot spells. Focusing on the Paris area during the hot summer of 2003, we investigated the influence of heat waves on UHI intensity, i.e., the urban-rural temperature contrast. In a first step, this was done based on observed temperatures from an urban and a rural site, showingdoi:10.3390/urbansci1010003 fatcat:2omgvzqd7jgszkbqacyly4mhg4
more »... hat per • C increase in the daytime temperature, the nighttime UHI intensity increased by 0.086 • C. Recognizing the limited spatial representativeness of the urban experimental site, located in a park, we then performed simulations with an urban climate model, covering the wider Paris area for the summer of 2003. First, a validation was done using the aforementioned temperature measurements to do so. Subsequently, we estimated the sensitivity of the nighttime UHI intensity with respect to the daytime temperature, this time using simulated temperatures of the densely built-up areas in the center of Paris, yielding an increase of UHI intensity of 0.19 • C per • C increase in the daytime temperature. While these results only apply to the domain and period studied, they do confirm recent reports that the UHI intensity increases during heat waves. The results also show that for the cooler parts of the urban fabric (e.g., parks), the UHI intensification during heat waves is around half of that of the dense urban fabric, thus providing some insights into possible mitigation strategies for the future. of 11 otherwise, frail people die prematurely but not by much, as they would have died soon afterwards anyway. However, it has been shown that, whereas minor heat wave episodes do induce a fair share of harvesting, this effect decreases as a function of the heat wave strength  . In particular, for the major European heat wave of 2003, it was found that the harvesting effect was modest  . Indeed, while of the 15,000 excess deaths occurring in France some 4000 would have died before the end of 2004 in any event, in the absence of the disaster the remaining 11,000 would have lived statistically eight to 11 years longer, thus amounting to an estimated 100,000 lost life-years in France alone  . Urban areas, which are now home to the majority of humans, exhibit additional heat stress because of the urban heat island (UHI) effect. Indeed, cities experience air temperatures in excess of rural values, their average nighttime temperatures being higher by a few • C, but increasing to 7-8 • C and more under favorable conditions. Because of this UHI increment, cities are particularly vulnerable to heat waves. In a recent study on Berlin, it was found found that during heat waves, mortality rates were higher in the city, especially in the most densely built-up districts  . In a study on Paris , it was concluded that, during the heat wave of the summer of 2003, areas exhibiting the highest remotely sensed nighttime infrared surface temperature suffered the highest excess mortality. Also for the 2003 European heat wave, it was found that heat-related excess mortality was especially high in cities, Paris being featured on top with an excess mortality of nearly 140% during the period of 1-19 August 2003  . Even though this enhanced excess mortality can at least partly be attributed to the vulnerability of the urban population (e.g., a larger share of isolated elderly people), increased mortality has been associated with the urban temperature increment itself  . Other studies also have established this combined effect on mortality by ambient conditions (heat exposure) and social vulnerability  . More insights regarding the spatial variability of heat mortality are available in        . In addition, recent studies on cities in the US present evidence that the urban temperature increment itself increases during heat waves. The authors of  investigated the impact of the heat wave occurring in the second half of July 1999 in the Midwestern US, with temperatures rising well above 32 • C, which resulted in several hundred excess deaths. During this event, the cities of Chicago and St. Louis were found to be disproportionally hotter than their rural surroundings, i.e., during the heat wave the UHI intensity of these cities was higher than average. The authors of  considered a heat wave episode in Baltimore, and they equally found that during the heat wave, the urban-rural temperature contrast itself increased. The authors of [23, 24] , analyzing measurements in Madison, also noticed an increase in the UHI intensity on hot days. Here, we tackle a similar research question, i.e., we investigate whether the urban-rural temperature increment gets enhanced during heat waves or not, and to what extent. This is done for the area of Paris (France), using both in situ measurements and model simulation results, for the extended summer (May-September) of 2003. This particular period is characterized by a wide range in temperature conditions, with daytime maximum temperatures down to around 12 • C on some days in May, and reaching just short of 40 • C on the hottest days during the first half of August of that year. The remainder of this paper is organized as follows. Section 2 describes the in situ observational data, which are subsequently used to evaluate the UHI intensity as a function of ambient (background) temperature. Section 3 then introduces a simulation conducted with an urban climate model on the Paris area. After validation, the modeling results are used to extend the observation-based results to the entire urban agglomeration of Paris. Finally, Section 4 presents the conclusions of this study. Observations The observation-based analysis presented here relies on synoptic meteorological data contained in the archives of the National Climatic Data Center (US), from which we extracted data for one urban and one rural location in the Paris region (see station positions in Figure 1 ). The Paris-Montsouris station (WMO code 071560) is taken as representative for urban climate conditions. It is located in the Montsouris Park near the center of Paris, at a position of approximately 48.81 • N and 2.33 • W. This park Urban Sci. 2017, 1, 3 3 of 11 has a diameter of approximately 400 m, and the station is positioned within the park at more than 100 m from the nearest edge. Therefore, the representativity of this location for urban conditions is to be considered with caution (more on this below). The Melun-Villaroche station (WMO code 071530) is located at 48.61 • N and 2.68 • W, which is near a small airfield 8 km north of the center of Melun, which itself is 35 km from the city center and 20 km from the outskirts of Paris. It is located in the middle of agricultural fields and grassland, at a distance of several kilometers from the nearest human settlement, thus constituting a representative rural station.
The downscaling of frequently-acquired geostationary Land Surface Temperature (LST) data can compensate the lack of high spatiotemporal LST data for urban climate studies. In order to be usable, the generated datasets must accurately reproduce the spatiotemporal features of the coarse-scale LST time series with greater spatial detail. This work concerns this issue and exploits the high temporal resolution of the data to address it. Specifically, it assesses the accuracy, correct patterndoi:10.3390/rs8040274 fatcat:owxp2oy34nghppg7yepmn4zpp4
more »... n and the spatiotemporal inter-relationships of an urban three-month-long downscaled geostationary LST time series. The results suggest that the downscaling process operated in a consistent manner and preserved the radiometry of the original data. The exploitation of the data inter-relationships for evaluation purposes revealed that the downscaled time series reproduced the smooth diurnal cycle, but the autocorrelation of the downscaled data was higher than the original coarse-scale data. Overall, the evaluation process showed that the generation of high spatiotemporal LST data for urban areas is very challenging, and to deem it successful, it is mandatory to assess the temporal evolution of the urban thermal patterns. The results suggest that the proposed tests can facilitate the evaluation process. Surface UHI (SUHI)     . In contrast to TA data, which are point measurements confined to local conditions  , thermal infrared (TIR) remote sensing is capable of providing a simultaneous and synoptic view of the urban thermal environment  . This enables the more detailed assessment of the urban hotspots and the relationship between the urban core and the surrounding natural lands    . Nevertheless, the use of satellite TIR data is not straightforward, and a number of limitations and problems, such as the atmospheric influence, the unknown emissivity and the effective anisotropy, have to be addressed prior to their exploitation  . To that end, one of the most important problems concerns the spatial and temporal resolution of the LST data. In particular, the available satellite sensors cannot provide datasets that capture the high spatial and temporal variability of SUHIs, and thus, their exploitation in urban climate studies is limited     . For instance, the Sun-synchronous Landsat series satellites, which offer the appropriate high spatial resolution (~100 m), acquire LST data every 16 days; whereas the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) geostationary satellites provides~4 km TIR data with a more appropriate temporal resolution of 5-15 min. To overcome this problem, the statistical downscaling of geostationary LST data has been proposed. This process can lead to the generation of Downscaled LST (DLST) time series that combine high spatial and temporal resolution and preserve the radiometry of the original thermal data      . In detail, the LST statistical downscaling is a scaling process that aims to enhance the spatial resolution of coarse-scale LST imagery using fine-scale auxiliary datasets. These auxiliary datasets are usually referred to as LST predictors and are statistically correlated to the LST . An LST downscaling scheme comprises two major parts. The first part is the set of LST predictors used for explaining the spatial variation of LST, while the second part is the regression tool used for associating the LST predictors with the LST data. These two parts are synergistically exploited in a three-stage procedure: firstly, the LST predictors are upscaled and co-registered to the coarse-scale LST data; then, a relationship between the coarse-scale LST data and the LST predictors is established using the regression tool; and finally, this relationship is applied to the fine-scale LST predictors, so as to generate the DLST data. Agam et al.  and Kustas et al.  include also a fourth process stage, which is an adjustment of the generated DLST data based on the differences of the observed and regressed coarse-scale LSTs. This post-downscaling processing aims to compensate the loss of LST variability due to the use of inflexible regression tools, such as least-square or linear fits  . In recent years, a large number of relevant works have been published  testing different LST predictors (separately or combined), such as: Vegetation Indices (VIs), emissivity data, land cover maps and topography data; and also regression tools, such as: linear regressors, least-square fits, Support Vector Regression Machines (SVMs) and Neural Networks (NNs)         . From the available list of LST predictors, the most widely used is the Normalized Vegetation Difference Index (NDVI)    , which is strongly negatively correlated with summer daytime LST imagery  . Most of the aforementioned works focus on the downscaling of~4 km data down to 1 km. The downscaling to even higher spatial resolutions (<500 m) is still a very challenging task, mainly because the assumption that the LST data-predictor relationship is valid in both spatial scales weakens or ceases to apply  . The work of Bechtel et al.  is one of the few studies that discusses the downscaling of geostationary urban LST data down to 100 m with a Root-Mean-Square-Error (RMSE) of 2.2˝C (recent LST fusion studies [32, 33] also report similar downscaling factors). In this work, a large set of LST predictors was utilized that also included for the first time LST annual climatology data in the form of annual cycle parameters (ACPs)    . The use of the ACPs for downscaling LST data below the 1-km cap provided very promising results  . However, this type of LST predictor has not been used as extensively as the rest, and thus, the available relevant literature is still very limited. Besides the identification of more robust predictors, another issue that requires attention is the evaluation of the generated high spatiotemporal DLST time series, which is hampered by the lack of appropriate ground truth data  . Presently, most downscaling studies utilize independent LST image data (confined to certain time spots) with which they compare the generated data and
Lecture Notes in Computer Science
Data distribution and access are major issues in space sciences as they influence the degree of data exploitation. The project "Space-Data Routers" (SDR) has the aim of allowing space agencies, academic institutes and research centres to share space data generated by single or multiple missions, in an efficient, secure and automated manner. The approach of SDR relies on space internetworking -and in particular on Delay-Tolerant Networking (DTN), which marks the new era in space communications,doi:10.1007/978-3-642-30630-3_34 fatcat:w2rnb7kmsveylmjeybteyqgsim
more »... nifies space and earth communication infrastructures and delivers a set of tools and protocols for space-data exploitation. The project includes the definition of limitations imposed by typical space mission scenarios in which the National Observatory of Athens (NOA) is currently involved, including space exploration, planetary exploration and Earth observation missions. In this paper, we present the mission scenarios and the associated major SDR expected impact from the proposed space-data router enhancements.
This study focuses on the assessment of surface solar radiation (SSR) based on operational Neural Network (NN) and Multi-Regression Function (MRF) modelling techniques that produce instantaneous (in less than one minute) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring Service (CAMS), respectively, these models aredoi:10.5194/amt-2017-351 fatcat:ob7x3va5jjdalku745g3vaukji
more »... able of calculating SSR in high resolution (1&thinsp;nm, 0.05 degrees, 15&thinsp;min) that can be used for spectrally-integrated irradiance maps, databases and various applications related with energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15-min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range &minus;15 to 15&thinsp;% for the NN that produces spectral irradiances (NNS), 5&ndash;6&thinsp;% underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from &minus;100 to 40&thinsp;W/m<sup>2</sup> and &minus;20 to 20&thinsp;W/m<sup>2</sup>, for the 15-min and monthly mean Global Horizontal Irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10% as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications, which are related with the solar energy production planning and use.
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