According to the "contrast reduction" principle the aerosol optical thickness (AOT) can be retrieved and mapped over
heterogeneous (such as urban) areas using a set of two satellite images of high spatial resolution: (i) one "reference
image" with minimum aerosol content involving negligible AOT, and (ii) one "polluted image" with AOT to be
assessed. AOT values retrieved in this way are thus relative to the reference image, and could be miscalculated when
other than atmospheric changes have taken place in the time between the acquisitions of the two images. Previously
developed DTA and SMA image processing codes are subject to this potential source of AOT miscalculation because
the contrast reduction is applied to single spectral bands. The new CHRISTINE code takes into consideration contrast
reduction in more than one spectral band and uses the Angstrom's power law to isolate atmospheric effects attributable
to aerosols. Preliminary testing of the new code over the Athens urban area against results obtained using the previous
codes showed a considerable improvement in terms of the area over which AOT can be retrieved with high confidence.
CHRISTINE has also a complementary feature of providing information on the aerosol size distribution emerging from
Angstrom coefficient approximation.
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.
Our recent work has demonstrated the feasibility of using satellite-derived data to draw quantitative maps of particulate loading within the planetary boundary layer. Our method, when used in conjunction with atmospheric dispersion models and ground data, can provide a comprehensive estimate of tropospheric pollution from particulate matter. Information filtering techniques are used to reduce the error of the information fusion algorithm and, consequently, produce the best possible estimate of tropospheric aerosol. Two data filtering methods have been used and their effectiveness with regard to overall error reduction is determined in this work. The first one is based on a weight scheme to take into account an empirical estimate of local error and/or uncertainty in input data. The second uses a modified Kalman filter for error reduction. The effectiveness of each of the filtering techniques depends on factors such as relative error variance across the computational domain, and precision of model input, i.e. on the accuracy of the ground emissions inventory and the reliability of measured ambient aerosol concentrations. The ICAROS NET fusion method was applied in the greater area of Athens, Greece over several days of observation in order to assess conclusively the adequacy of the information fusion filters employed.
The air quality in Munich is monitored by the measurement network of the Bavarian Agency for Environmental Protection. Additional information can be provided from retrievals of optical thickness and corresponding particle concentrations from satellite images in an area of approximately 100 km x 100 km (depending on the satellite sensor used). The satellite measures the optical thickness of the entire atmosphere, which has to be attributed mainly to the mixing layer. The mixing layer height is determined either by remote sensing, by radiosondes, or by numerical models of the boundary layer. The corrected optical thickness of the satellite images can be interpreted as the particle concentration in the mixing layer. Data from the ground-based monitoring network and from satellite retrievals are fused in the ICAROS NET platform.
This platform is applied to supply additional information on the air quality in the Munich region and it is tested as well as evaluated during field campaigns in summer and winter. The adaptation to the Munich region covers the development of routines for the collection of data, for example from the measuring network, and the disposal of information, which were defined by the Bavarian agency for environmental protection. During measurement campaigns in and around Munich PM 10, PM 2.5 and PM 1.0 concentrations and mixing layer heights by remote sensing (SODAR, ceilometer, WTR) were determined. Temporal variations of the concentration, the spatial distribution (3 measurement locations) and concentration conditions for selected particle sizes are presented.
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.
Recent studies worldwide have revealed the relation between urban air pollution, particularly fine aerosols, and human health. The current state of the art in air quality assessment, monitoring and management comprises analytical measurements and atmospheric transport modeling. Earth observation from satellites provides an additional information layer through the calculation of synoptic air pollution indicators, such as atmospheric turbidity. Fusion of these data sources with ancillary data, including classification of population vulnerability to the adverse health effects of fine particulate and, especially, PM10 pollution, in the ambient air, integrates them into an optimally managed environmental information processing tool. Several algorithms pertaining to urban air pollution assessment using HSR satellite imagery have been developed and applied to urban sites in Europe such as Athens, Greece, the Po valley in Northern Italy, and Munich, Germany. Implementing these computational procedures on moderate spatial resolution (MSR) satellite data and coupling the result with the output of HSR data processing provides comprehensive and dynamic information on the spatial distribution of PM10 concentration. The result of EO data processing is corrected to account for the relative importance of the signal due to anthropogenic fine particles, concentrated in the lower troposphere. Fusing the corrected maps of PM10 concentration with data on vulnerable population distribution and implementation of epidemiology-derived exposure-response relationships results in the calculation of indices of the public health risk from PM10 concentration in the ambient air. Results from the pilot application of this technique for integrated environmental and health assessment in the urban environment are given.
Integrated air quality management requires considering many information classes simultaneously, including environmental quality data, health impact pathway models, economic analyses, the respective regulatory framework, and the priorities of the concerned stakeholders. Integrated air pollution assessment in particular includes accurate representation of the pollution distribution in time and space, identification of the main emission sources, and evaluation of the possible alternatives for coping with the observed environmental burden. The current state of the art in air quality assessment, monitoring and management comprises analytical measurements and atmospheric transport modeling. Earth observation from satellites may provide an additional information layer through the calculation of synoptic air pollution indicators, such as the tropospheric aerosol optical thickness. This paper outlines a paradigm for efficient data and model fusion for the integrated assessment of the health impact due to airborne chemicals. The information management techniques employed and the problems due to the multidisciplinary nature of the phenomena analyzed are highlighted. Selected examples from using this methodology for the assessment of air quality in the European Union are given.
High spatial resolution (HSR) satellite observations, while not frequent enough to follow air pollution's dynamic fluctuations, can provide spatially resolved information related to urban air quality. More specifically, HSR satellites can provide independent "spatial measurements" on the columnar aerosol optical thickness in the visible (AOTV). When normalized to ground level, AOTV can be correlated to fine aerosol concentrations, and when monitored over long or representative periods it could be used as an air-quality indicator to bridge the gap between "point measurements" by ground-based sampling, and "spatial estimations" by atmospheric modelling. We briefly review in this paper the methods we developed to map AOTV over urban areas from HSR satellites; we then describe qualitative AOTV validation procedures for the case of Athens. We finally present preliminary quantitative results from a pilot application where we compared data on air quality acquired using the three tools (i.e., satellite observations, atmospheric modelling and ground measurements) over two polluted European sites. This comparison showed good agreement between satellite-derived AOTV, on the one hand, and ground-level aerosol precursor concentrations and modelling-derived pollutant flow patterns on the other. These preliminary results encouraged an in-depth investigation of the benefits from the complementary use of these three techniques for integrated air-quality monitoring. During four pilot campaigns foreseen in the framework of the ICAROS NET project, we plan to collect detailed atmospheric data and run numerical models in conjunction with the satellite passages.
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.
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