The Urban Heat Island (UHI) is an adverse environmental effect of urbanization that increases the energy demand of cities, impacts the human health, and intensifies and prolongs heatwave events. To facilitate the study of UHIs the Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (IAASARS/NOA) has developed an operational real-time system that exploits remote sensing image data from Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) and generates high spatiotemporal land surface temperature (LST) and 2 m air temperature (TA) time series. These datasets form the basis for the generation of higher value products and services related to energy demand and heat-related health issues. These products are the heatwave hazard (HZ); the HUMIDEX (i.e. an index that describes the temperature felt by an individual exposed to heat and humidity); and the cooling degrees (CD; i.e. a measure that reflects the energy needed to cool a building). The spatiotemporal characteristics of HZ, HUMIDEX and CD are unique (1 km/5 min) and enable the appraisal of the spatially distributed heat related health risk and energy demand of cities. In this paper, the real time generation of the high spatiotemporal HZ, HUMIDEX and CD products is discussed. In addition, a case study corresponding to Athens’ September 2015 heatwave is presented so as to demonstrate their capabilities. The overall aim of the system is to provide high quality data to several different end users, such as health responders, and energy suppliers. The urban thermal monitoring web service is available at http://snf-652558.vm.okeanos.grnet.gr/treasure/portal/info.html.
Knowledge of the air and land surface temperature and their temporal and spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions and to monitoring environmental changes due to urbanization. We present a number of air and land surface temperature products that have been produced, archived, evaluated, and analyzed for 10 European cities within the framework of the European Space Agency-funded "Urban Heat Islands and Urban Thermography" project. We evaluate in what way these products are suited to explore the urban thermal dynamics and how products with different temporal and spatial resolution can provide a complementary view, both for thermal patterns as well as heat waves. Level of confidence was evaluated through quantitative, qualitative, and user-based analyses.
The bioclimatic index most commonly used in urban climate studies to describe the level of thermal sensation that a person experiences due to the modified climatic conditions of an urban area, is the discomfort index (DI) of Thom. DI reflects the proportionate contribution of air temperature (Ta) and relative humidity (RH) on the human thermal comfort. In this study, the discomfort index is estimated using thermal infrared data as acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the National Oceanic and Atmospheric Administration (NOAA) satellite. For this purpose, a dataset of AVHRR-14 daytime images collected during the warm season from June to August 2000 covering the Greater Athens Area, in Greece, was used. Air temperature was related to a split-window estimate of land surface temperature (Ts), whereas relative humidity was assessed in terms of dew point temperature (Td) and of a split-window estimate of atmospheric precipitable water (PW). AVHRR-estimated DI values were compared with coincident DI values obtained from air temperature and relative humidity observations recorded at standard meteorological stations. Statistical analysis showed a good agreement (r2 = 0.79) between the AVHRR-estimated and the station-observed DI values, with a root mean square error (RMSE) of 1.2oC and a bias of 0.9oC. Results demonstrate the potential of using AVHRR data for defining the spatial variation of the DI index at a higher resolution (1.1 km) than is feasible from meteorological stations.
The success of any decision support system for managing wildfires lies on its ability to simulate fire evolution. Therefore, accurate information on the natural fuel material in any area of interest is necessary. The present study aims to provide methodological tools to explore in depth the potential of new Earth Observation data for horizontal mapping of vegetated areas. Two approaches are mainly described. The first one deals with the classification of ASTER visible, near- and short-wave infrared images in a detailed nomenclature including both different species and tree densities. This is important for wildfire studies since the same vegetation classes may represent completely different risk ignition levels depending on their morphological characteristics (i.e., trees height and density). The improvement of class separability using hyperspectral images acquired by Hyperion is also investigated. The second approach refers to a pattern recognition software tool for single tree counting using a very high spatial resolution image acquired by IKONOS-2 satellite. According to this approach, the regions dense in plants are identified by applying a suitable thresholding on the image. The resulted regions are further processed in order to estimate the number and location of single trees based on a pre-specified crown size per stratified zone. The outcome of the latter approach may provide direct evidence of tree density relating to ground biomass. Finally, the two approaches are used in a complementary manner to explore the possibilities offered by new sensor technology to override past limitations in species and fuel classification due to inadequate spectral/spatial resolution. The pilot application area is Mount. Pendeli and the east side of Mount. Parnitha, in the prefecture of Attiki, Greece.
Kernel-based reclassification algorithm derives information on specific thematic classes on the basis of the frequency and spatial arrangement of land cover classes within a square kernel. This algorithm has been originally developed and validated for the urban environment. The present work investigates the potential of projecting this technique to the classification of very high spatial resolution satellite imagery of natural ecosystems. For that purpose a software tool has been developed. The output, apart from the reclassified image, includes a post-classification probability map which shows the areas where the kernel reclassification algorithm has given valid results. The software was tested on an IKONOS image of Lake Kerkini (Greece), a wetland of great ecological value, included in the NATURA 2000 list of ecosystems. The results show that the algorithm has responded successfully in most cases overcoming problems previously encountered by pixel-based classifiers, such as pixel noise.
This study investigates the potential of classifying complex ecosystems by applying the radial basis function (RBF) neural network architecture, with an innovative training method, on multispectral very high spatial resolution satellite images. The performance of the classifier has been tested with different input parameters, window sizes and neural network complexities. The maximum accuracy achieved by the proposed classifier was 78%, outperforming maximum likelihood classification by 17%. Analysis showed that the selection of input parameters is vital for the success of the classifiers. On the other hand, the incorporation of textural analysis and/or modification of the window size do not affect the performance substantially. The new technique was applied to the area of Lake Kerkini (Greece), a wetland of great ecological value, included in the NATURA 2000 list of ecosystems.
Digital Elevation Models (DEMs) and land cover products are primary inputs for hydrologic models of surface runoff that affects infiltration, erosion, and evapotranspiration. DEM and land cover play important role in determining the runoff characteristics of specific catchment areas. Recently, at local level, a number of data sources have been used to derive land cover products for high resolution studies. These studies have been carried out for a number of different applications, including estimation of biomass and vegetation mapping. A hydrologic land cover classification includes information not only about vegetation species, but also about the land surface and what classes are important hydrologically. This kind of classification must therefore incorporate information on elevation, slope, aspect, surface roughness, as well as vegetation species derived from satellite added-value products. The main problems when generating hydrologic land cover maps is the lack of accurate DEMs and the confusion of spectral responses from different features. In this study, a Terra/ASTER image acquired over the region of Heraklion, Crete, Greece was used. ASTER stereo imagery is used for DEM production because it gives a strong advantage in terms of radiometric variations versus the multi-date stereo-data acquisition with across-track stereo, which can then compensate for the weaker stereo geometry. GCPs (Ground Control Points) derived from differential GPS measurements were also used for absolute DEM production. A hydrologic land cover classification scheme was developed by combining ASTER multispectral imagery, ASTER DEM products and the spectral signatures derived from field observations at predefined training sites.
Our paper describes an operational application of NOAA-AVHRR satellite imagery in combination with satellite-based land cover data for comprehensive observation and follow-up of 10 000 fire outbreak and of their consequences in Greece during summer 2000. At a first stage, we acquired and processed satellite images on a daily basis and we interpreted them in view to smoke-plume tracking and fire-core detection at national level. Information was acquired eight times per day and derived from all AVHRR spectral channels. At a second stage, we assessed the consequences of the fires by producing burnt-area maps on the basis of multi-annual normalised vegetation indexes using again AVHRR data but this time in combination with the European CORINE Land Cover database (CLC). The derived burnt-area maps compared successfully to the in-situ inventories available for that year. Our results showed burnt area estimates with an accuracy ranging from 88% to 95%, depending on the predominant land-cover type. These results, along with the very low cost and hi-acquisition frequency of AVHRR satellite imagery, suggest that the combination of moderate resolution satellite data with harmonised CLC data can be applied operationally for forest fire and post-fire assessments at national and at pan-European levels.
Synthetic Aperture Radar (SAR) images are extensively used for the determination of oil slicks in the marine environment, as they are independent of local weather conditions and cloudiness. Oil spills are recognized by the expert's eye as dark patterns of characteristic shape in particular context. However, the major difficulty to be dealt with is to differentiate between oil spills and look-alikes of natural origin. A fully automated system for the identification of possible oil spills that imitates the expert's choice and decisions has been developed. The system's architecture comprises several distinct modules of supplementary operation (georeferencing, land masking, thresholding, segmentation) and uses their contribution to the analysis and assignment of the probability of a dark image shape to be an oil spill by means of a fuzzy classifier. The output consists of several images and table providing the user with all relevant information as well as supporting decision making. The case study area was the Aegean Sea in Greece. The system responded very satisfactorily for all 35 images processed. The complete procedure described is a fully automated stand-alone application running under Windows operating system.
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