This PDF file contains the front matter associated with SPIE Proceedings Volume 6749, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

In common with all domains of remote sensing, residues indicative of past human activity can only be detected if they exhibit some form of identifiable contrast with their surroundings. Unlike many other domains these residues do not exhibit consistent spectral signatures. Archaeological spectral responses are commonly expressed as subtle deviations from their surrounding matrix. This is true for crop marks, soil marks and thermal anomalies. The challenge is to collect imagery when the contrast between archaeological residues and the background matrix is maximized and thus to find algorithms that will enhance these sometimes subtle distinctions so that they can be more readily detected. This paper will present work undertaken in the semi-arid environment of Homs, Syria. The project area includes two contrasting environmental zones with a differing repertoire of archaeological remains: a basalt zone (120 km²) and a marl zone (480 km²). Declassified Corona space photography and Ikonos satellite imagery (panchromatic and multispectral) were evaluated to determine their efficacy for detecting a range of different archaeological residues. No single image set was able to provide the best result for the two zones, as each required imagery collected under different environmental conditions.

The paper concerns the results of a research project on the application in archaeological survey of high resolution images of the QuickBird 2 satellite. The research is carried out within the activities of the Italian Archaeological Mission at Hierapolis of Phrygia (Turkey). The use of satellite images with high geometric, radiometric and spectral resolutions has constituted an important tool for archaeological research in the city and in the surrounding area, because vertical aerial photographies and recent and detailed cartographies are non-available. In fact the exceptional spatial resolution of the images makes them comparable to aerial photos on a medium scale; this type of documentation has an enormous potential in the study of urban and territorial ancient contexts. The examination of these images has permitted to detect surface anomalies and traces linked to archaeological buried structures or to paleo-environmental elements; moreover, particulary in the territory, the panchromatic images were georeferenced and used as the base field maps for the survey, in integration with GPS systems. The study of the satellite images and the ground truth verify have made fundamental contributions to the reconstruction of the urban layout of Hierapolis. Also much interesting were the results obtained in the territory of the city, with the integration of remote sensing and archaeological survey; the researches recovered numerous and important data on necropolis, aqueducts, roads, farms, quarries and villages dependent from Hierapolis. All the data collected are integrating into a GIS to produce archaeological maps.

Satellite imagery is an increasingly important tool for cultural and natural heritage management. It has particular relevance in those areas of the world where the heritage resource is poorly understood. In these areas what is known may be significantly biased: i.e. heritage management strategies may have been skewed towards a specific type of remain (normally monumental architecture). This paper will present work undertaken in the landscape around the UNESCO World Heritage site of Sanchi, a major early-historic Buddhist site in Madhya Pradesh, India. Rather than discuss the merits of individual sensors this paper takes a more holistic approach and examines the 'life-cycle' of satellite imagery for an archaeological project. This means that satellite imagery is viewed not just as a source of archaeological information but also as a data source that can be used to contextualise and interpret the archaeological resource. Hence this paper provides a framework which should allow archaeological investigators to select, manipulate and integrate different satellite sensors to provide information which is fit for purpose. This paper discusses the implications of satellite sensors for different activities, including archaeological prospection, landuse mapping and terrain modeling and considers how the synergies of different satellite and archaeological data can be exploited.

The recent integrated aerial photographic assessment of Southern Dobrogea (Romania) is part of the first author's British Academy funded research programme 'Contextualizing change on the Lower Danube: Roman impact on Daco-Getic landscapes'. This seeks to study the effect of the Roman conquest and occupation on the native Daco-Getic settlement pattern on the Lower Danube. The methodology involves integrating a range of remotely sensed imagery including: low altitude oblique aerial photographs, obtained through traditional aerial reconnaissance; medium altitude vertical photographs produced by German, British and American military reconnaissance during the Second World War, selected from The Aerial Reconnaissance Achive at Keele University; and high altitude de-classified military satellite imagery (Corona) from the 1960s, acquired from the USGS. The value of this approach lies not just in that it enables extensive detailed mapping of large archaeological landscapes in Romania for the first time, but also that it allows the recording of archaeological features permanently destroyed by more recent development across wide areas. This paper presents some results and addresses some of the problems raised by each method of data acquisition.

Aerial photography has made the single most important contribution to our improved appreciation of the density, diversity and distribution of archaeological sites in Britain since WWII. This is particularly the case for areas of intensive lowland agriculture where ploughed-out sites are known only from marks in the crops growing above them. However, reconnaissance for such cropmarks is not equally effective throughout the lowlands because of the particular conditions of drier weather, well-drained soils and arable agriculture required before they become visible. In Scotland, for example, there is considerable bias in the discovery and, consequently, known distribution of archaeological sites in favour of the drier eastern side of the country, with its higher percentage of arable agriculture, as opposed to the west with its wetter climate and greater proportion of grazing land. Given that the appearance of cropmarks is linked to moisture stress in growing plants, they are potentially detectable at bandwidths outside the visible and before they become apparent therein. Using a range of imagery (CASI 2, ATM and digital vertical photographic data) from two case study sites in Lowland Scotland to facilitate comparisons, one in the east and one in the west, this paper considers the extent to which hyperspectral imagery can enhance the identification of otherwise invisible archaeological sites.

The launch of several very high spatial resolution satellite (VHSRS) systems (Ikonos-2, Quickbird-2 and others) in the recent past also has provided new possibilities for archaeological research. The emphasis of this paper is to compare and evaluate the contribution of spectral characteristics and pixel resolution of Quickbird-2 and Ikonos-2 for automatic extraction of ancient features from VHSRS imagery. The spectral characteristics of both images have been evaluated by a band-by-band comparison. Apart from a visual comparison, pixel- and object-based classification techniques are applied to assess the effect of different image characteristics. The study is carried out on the antique site of Sagalassos (southwest Turkey). A profound analysis of the VHSRS data reveals that the spectral characteristics of Ikonos-2 capture a more detailed spectral reflectance for the same ground target compared to Quickbird-2. The latter outperforms Ikonos-2 for the visual identification of ancient remains due to its enhanced ground resolution. The application of automatic extraction techniques on archaeological remains in the ancient town of Sagalassos shows opposing results. Compared with the visual interpretation of Quickbird-2, the pixel-based technique gives the best results for Ikonos-2, while an object-based method is best for Quickbird-2.

Concerns over worldwide declines in marine resources have prompted the search for innovative solutions for their conservation and management, particularly for coral reef ecosystems. Rapid advances in sensor resolution, coupled with image analysis techniques tailored to the unique optical problems of marine environments have enabled the derivation of detailed benthic habitat maps of coral reef habitats from multispectral satellite imagery. Such maps delineate coral reefs' main ecological communities, and are essential for management of these resources as baseline assessments. UNESCO's World Heritage Central Pacific Project plans to afford protection through World Heritage recognition to a number of islands and atolls in the central Pacific Ocean, including the Phoenix Archipelago in the Republic of Kiribati. Most of these islands however lack natural resource maps needed for the identification of priority areas for inclusion in a marine reserve system. Our project provides assistance to UNESCO's World Heritage Centre and the Kiribati Government by developing benthic and terrestrial habitat maps of the Phoenix Islands from high-resolution multispectral imagery. The approach involves: (i) the analysis of new Quickbird multispectral imagery; and (ii) the use of MARXAN, a simulated annealing algorithm that uses a GIS interface. Analysis of satellite imagery was performed with ENVI®, and includes removal of atmospheric effects using ATCOR (a MODTRAN4 radiative transfer model); de-glinting and water column correction algorithms; and a number of unsupervised and supervised classifiers. Previously collected ground-truth data was used to train classifications. The resulting habitat maps are then used as input to MARXAN. This algorithm ultimately identifies a proportion of each habitat to be set aside for protection, and prioritizes conservation areas. The outputs of this research are being delivered to the UNESCO World Heritage Centre office and the Kiribati Government as baseline assessments of these resources and to assist in marine reserve planning.

Spatial interpolation methods are widely applied in marine studies to evaluate the spatial distribution of oceanographic parameters and inter-compare time series of maps of selected variables. A variety of such methods are nowadays available and therefore, selection of the most appropriate for a specific case study is not an easy task. Within geography and other spatially oriented disciplines, and most of the times in the framework of a Geographical Information System, several attempts have been carried out to assess the efficiency of various spatial interpolators using diverse methodologies. In this paper, an attempt was carried out to evaluate the accuracy of spatial interpolators for mapping the distribution of organic carbon (weight %), an important indicator of marine sediments in the marine environment. Measurements of organic carbon were carried out in a network of 20 sampling sites in the Gulf of Gera, which is representative of a semi-enclosed and shallow marine ecosystem at the south-eastern part of the island of Lesvos, Greece. For the interpolators under study, the cross-validation error was calculated at each sampling station and calculation of the RMSE (root-mean-square error), the MAE (mean-absolute error) and the MBE (mean-bias error) was carried out to assess the accuracy of their performance. The results revealed the most appropriate interpolator for the given dataset which was then applied to develop the thematic map of the spatial distribution of organic carbon. Discussion on the potential increase of the surface accuracy is also carried out.

Ship traffic monitoring may be performed using satellite SAR data. The advantage with the SAR sensor is the all weather and day/night imaging capability. However, the SAR backscatter contrast between a vessel and the surrounding sea state may be small in high wind conditions and at small incidence angles. The present and future SAR satellites will have the capability of imaging the earth surface with several incidence angles, and with dual-polarimetry (HH/HV, VV/VH or HH/VV). The SAR ship/clutter contrast may threrefore be increased by applying different polarisation combinations, or using higher incidence angles. We have shown that geocoded ENVISAT ASAR images in the coastal region of Norway can be used to gain experience in the combined use of satellite SAR and an automatic identification system (AIS) for ship traffic monitoring. There are plans for placing AIS systems onboard satellites. It will then be possible to fuse the information from satellite SAR with those from satellite (or ground-based coastal) AIS and thereby identify all the detected ships within a SAR image. This data fusion will enable us to develop further knowledge about SAR backscatter properties from vessel types that may not be detected so well using the SAR data only. On the other side, it will be possible to pin-point those ship candidates that do not carry an AIS system, and thereby take appropriate security or rescue actions.

An update of the activities of the Joint Research Centre (JRC), a Directorate General of the European Commission, in the field of long term monitoring sea-based oil pollution are described. JRC has collected all available relevant data concerning sea-based oil pollution from different actors and archives. For the North and Baltic seas, data from aerial surveillance were used and, for this reason, all oil spills are real and confirmed. On the contrary, the data for the Mediterranean and the Black Sea derive from oil spills detected by JRC in archive satellite imagery. We believe that oil spills detected in satellite images are real, even if we will call them "possible oil spills" because they have not been confirmed by an aircraft and or a vessel. For the Mediterranean and the Black Sea, these data represent the only source to assess the problem of sea-based oil pollution in these seas. This paper intends to present a comprehensive view of the long term monitoring of sea-based oil pollution in all the seas around Europe.

The processing and evaluation of digital airborne imagery for detection, monitoring and modeling of mountain pine beetle (MPB) infestations is evaluated. The most efficient and reliable remote sensing strategy for identification and mapping of infestation stages ("current" to "red" to "grey" attack) of MPB in lodgepole pine forests is determined for the most practical and cost effective procedures. This research was planned to specifically enhance knowledge by determining the remote sensing imaging systems and analytical procedures that optimize resource management for this critical forest health problem. Within the context of this study, airborne remote sensing of forest environments for forest health determinations (MPB) is most suitably undertaken using multispectral digitally converted imagery (aerial photography) at scales of 1:8000 for early detection of current MPB attack and 1:16000 for mapping and sequential monitoring of red and grey attack. Digital conversion should be undertaken at 10 to 16 microns for B&W multispectral imagery and 16 to 24 microns for colour and colour infrared imagery. From an "operational" perspective, the use of twin mapping-cameras with colour and B&W or colour infrared film will provide the best approximation of multispectral digital imagery with near comparable performance in a competitive private sector context (open bidding).

This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

Comparing the flat topographical area, it is difficult to extract soil erosion in the complex one. RUSLE is the most comprehensive experimental model for soil erosion, but it is suitable for the flat topographical area. So it is necessary to study suitable algorithm for the complex topographical region. The paper proposes new algorithm for topographical factor and cover-management factor that are affected by complex topography seriously, including topographical factor calculation for steep slope and vegetation fraction extraction based on remote sensing classification. The test indicates the algorithms are more effective than the normal algorithms of RUSLE.

This paper describes a new project of the Institute for Geoinformatics and Remote Sensing, funded by the German Federal Foundation for the Environment (DBU, Deutsche Bundesstiftung Umwelt www.dbu.de). The goal of this project is to develop a mobile zoo information system for Pocket PCs and Smart phones. Visitors of the zoo will be able to use their own mobile devices or use Pocket PCs, which could be borrowed from the zoo to navigate around the zoo's facilities. The system will also provide additional multimedia based information such as audio-based material, animal video clips, and maps of their natural habitat. People could have access to the project at the zoo via wireless local area network or by downloading the necessary files using a home internet connection. Our software environment consists of proprietary and non-proprietary software solutions in order to make it as flexible as possible. Our first prototype was developed with Visual Studio 2003 and Visual Basic.Net.

A contents correlation and Genetic Algorithm based remote sensing images fusion method is presented. Based on the imaging properties of Panchromatic images and multi-spectral images, contents correlation analysis concept is introduced. The fusion procedure is that Contourlet transform decomposition of Panchromatic and multi-spectral images, Analysis of redundancy and supplement relations of images contents, the construction of fusion method to redundancy components and supplement components, fusion algorithms optimization by using Genetic Algorithm. Finally, a fused image can be obtained with inverse Contourlet transform. Preliminary experiment results show that this method is better than ordinary wavelet transform based fusion method, IHS transform based fusion method and PCA transform based fusion method.

We have developed a new and innovative technique for combining a high-spatial-resolution multispectral image with a lower-spatial-resolution hyperspectral image. The approach, called CRISP, compares the spectral information present in the multispectral image to the spectral content in the hyperspectral image and derives a set of equations to approximately transform the multispectral image into a synthetic hyperspectral image. This synthetic hyperspectral image is then recombined with the original low-spatial-resolution hyperspectral image to produce a sharpened product. The result is a product that has the spectral properties of the hyperspectral image at a spatial resolution approaching that of the multispectral image. To test the accuracy of the CRISP method, we applied the method to synthetic data generated from hyperspectral images acquired with an airborne sensor. These high-spatial-resolution images were used to generate both a lower-spatial-resolution hyperspectral data set and a four-band multispectral data set. With this method, it is possible to compare the output of the CRISP process to the 'truth data' (the original scene). In all of these controlled tests, the CRISP product showed both good spectral and visual fidelity, with an RMS error less than one percent when compared to the 'truth' image. We then applied the method to real world imagery collected by the Hyperion sensor on EO-1 as part of the Hurricane Katrina support effort. In addition to multiple Hyperion data sets, both Ikonos and QuickBird data were also acquired over the New Orleans area. Following registration of the data sets, multiple high-spatial-resolution CRISP-generated hyperspectral data sets were created. In this paper, we present the results of this study that shows the utility of the CRISP-sharpened products to form material classification maps at four-meter resolution from space-based hyperspectral data. These products are compared to the equivalent products generated from the source 30m resolution Hyperion data.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a unique source of reach spectral information useful for many applications. It provides observations in 36 spectral bands ranging in wavelengths from 0.4μm to 14.4μm with a spatial resolution from 250m to 1km. The standard MODIS data processing system and products cover the basic operational needs for a number of products and applications. Implemented globally they, however, cannot always make the best use of MODIS 250m and 500m land channels required for terrestrial monitoring and climate change applications. To address the need of regional users in enhanced MODIS data, especially in terms of spatial resolution, an independent technology for processing MODIS imagery has been developed. It uses MODIS level 1B top of the atmosphere swath data as input. The system includes the following steps: 1) fusion (downscaling) of MODIS 500m land channels B3-B7 with 250m bands B1-B2 to obtain consistent 250m imagery for all seven bands B1-B7; 2) re-projection of 250m bands into standard geographic projection; 3) scene identification at 250m spatial resolution to obtain mask of clear-sky, cloud and cloud shadows; 4) compositing clear-sky pixels over 10-day intervals; 5) atmospheric correction; 6) landcover-based BRDF fitting procedure. The fusion technique is designed to work with MODIS/TERRA data due to known problems with band-to-band registration accuracy on MODIS/AQUA. The developed method is applied to generate MODIS clear-sky land products in the Lambert Conformal Conical (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for the North America and the Arctic circumpolar zone. The novel clear-sky compositing approach is proposed that significantly reduces impact of BRDF effect on raw composites by separation of pixels into two ranges of relative azimuth angle within 90°-270° and outside of this interval.

Urban areas are rapidly changing all over the world and therefore provoke the necessity to update urban maps frequently. Remote sensing has been used for many years to monitor these changes. The urban scene is characterized by a very high complexity, containing objects formed from different types of man-made materials as well as natural vegetation. Hyperspectral sensors provide the capability to map the surface materials present in the scene using their spectra and therefore to identify the main object classes in the scene in a relatively easy manner. However ambiguities persist where different types of objects are constructed of the same material. This is for instance the case for roads and roof covers. Although higher-level image processing (e.g. spatial reasoning) might be able to relief some of these constraints, this task is far from straight forward. In the current paper the authors fused information gathered using a hyperspectral sensor with that of high-resolution polarimetric SAR data. SAR data give information about the type of scattering backscatter from an object in the scene, its geometry and its dielectric properties. Therefore, the information obtained using the SAR processing is complementary to that obtained using hyperspectral data. This research was applied on a dataset consisting of hyperspectral data from the HyMAP sensor (126 channels in VIS-SWIR) and E-SAR data which consists of fullpolarimetric L-band and dual-polarisation (HH and VV) X-band data. Two supervised classifications are used; 'Logistic Regression' (LR) which applied to the SAR and the PolSAR data and a 'Matched Filter' which is applied to the hyperspectral data. The results of the classification are fused in order to improve the mapping of the main classes in the scene and were compared to a ground truth map that was constructed by combining a digital topographic map and a vectorized cadastral map of the research area. An adequate change detection of man-made objects in urban scenes was obtained by the fusion of features derived from SAR, PolSAR and hyperspectral data.

Radar satellites are important for geospatial intelligence about urban areas and urban situational awareness, since these satellites can collect data at day and night and independently of weather conditions ensuring that the information can be obtained at regular intervals and in time. For this purpose we have applied change detection techniques developed at TNO to Radarsat I fine beam imagery of various dates to find changes in Baghdad during and after the war in 2003. A drawback of SAR imagery is the poor ability to recognize the detected changes in the scene. In this paper we present a workflow for the characterization and classification of changes detected in SAR imagery. We show that these changes can be characterized using complementary data and context information. For this purpose we have used a digital surface model from Ikonos stereo imagery that contains building heights. We also have used so-called temporal features extracted from a multi-temporal data-set of Radarsat data to select the changes and to detect activity between 2003 and 2007, which has been classified with high-resolution optical data.

Operational SAR satellite systems such as ENVISAT-ASAR and RADARSAT-1 deliver image data of a rather coarse resolution, which allows the recognition or feature extraction only for large man-made objects. State of the art airborne SAR sensors on the other hand provide spatial resolution in the order well below a half meter. In such data many features of urban objects can be identified and used for recognition. Core elements of man-made infrastructure are bridges. In case of bridges over water, the oblique side looking imaging geometry of SAR sensors may lead to special signature in a SAR image depending on the aspect. In this paper, the appearance of bridges over water in SAR data is discussed. Geometric constraints concerning the changing of this signature are investigated using simulation techniques based on an adapted ray tracing. Furthermore, an approach is presented to detect bridges over water and to derive object features from spaceborne and airborne SAR images in the context of disaster management. RADARSAT-1 data with a spatial resolution of about 9 m as well as high-resolution airborne SAR data of geometric sampling distance better than 40 cm are investigated.

Aim of this study is the identification of the hyperspectral scanner operational characteristics allowing for asbestos cement (AC) roofing sheets deterioration status assessment that is related to the asbestos fibers abundance. At this purpose we made laboratory measurements on AC samples with different deterioration status collected in two industrial areas in Italy. The asbestos occurrence in the AC samples was recognized using XRD and FTIR instruments and the abundance of surfacing asbestos fibers was performed by using a high resolution scanner (SEM). The samples optical characteristics and the directional effects that can affect the AC samples were analyzed using a portable field spectrometer (ASD). The results of the ASD measurements (i.e. band-depth ratio of the continuum removed calculated for the asbestos diagnostic band at 2.32μm) were related to the relative percentage of surfacing asbestos fibers (i.e. the AC deterioration status). Since laboratory measurements confirmed that optical measurements are sensitive to variations in asbestos fiber abundance, detection limit analysis was used for defining the requirements (signal-to-noise ratio, band FWHM, and sampling range) of an optimal hyperspectral sensor most suitable for detecting the diagnostic asbestos absorption features.

The purpose of this research is to build digital urban landscape (or urbanscape), based on as-built environmental information. A long-range 3D laser scanner is used to record buildings, plants, and open spaces in static configuration, plus the records of pedestrians, vehicles, objects in dynamic form. Studies have extended to the record of 3D geometries into following information related issues, such as digital contents definition, land registration, urban element integration, characteristic abstraction, and scan "noise" made by pedestrians or vehicles for circulation system evaluation. In order to manage and display large amount of as-built data, the information system is made by 3D database, web pages with measuring function, as well as editing works for different data types and service possibilities.

Historically the atmospheric and meteorological communities are separate worlds with their own data formats and tools for data handling making sharing of data difficult and cumbersome. On the other hand, these information sources are becoming increasingly of interest outside these communities because of the continuously improving spatial and temporal resolution of e.g. model and satellite data and the interest in historical datasets. New user communities that use geographically based datasets in a cross-domain manner are emerging. This development is supported by the progress made in Geographical Information System (GIS) software. The current GIS software is not yet ready for the wealth of atmospheric data, although the faint outlines of new generation software are already visible: support of HDF, NetCDF and an increasing understanding of temporal issues are only a few of the hints.

Until now, interpretation of aerial photographs is a standard tool for monitoring land cover change where fine spatial resolutions are required and this task is expensive and time-consuming. Though, from a spaceborne perspective, the VHR satellite data are, since 1999, capable to meet the mapping and monitoring needs of municipal and regional planning agencies. Indeed, these data from the sensors Ikonos, QuickBird, OrbView-3, and in near future, the Pléiades- HR French sensors, have spatial resolution lower than 5 m in multispectral mode and lower than 1 m in panchromatic mode. These new sources of data combine the advantages of satellite data (synoptic view, digital format suitable for computer processing, quantitative land surface information at large spatial coverage and at frequent temporal intervals ...) with the very high spatial resolution. In spite of these advantages, the use of VHR satellite data involves some problems in traditional per-pixel classification often used in change detection techniques. There are still two occurring classification problems that can strongly deteriorate the result of a per-pixel classification of the VHR satellite data: spectral variability and poor spectral resolution. A solution to overcome these problems is the region-based classification that can be integrated in the common change detection techniques. The segmentation, before classification, produces regions which are more homogeneous in themselves than with nearby regions and represent discrete objects or areas in the image. Each image region then becomes a unit analysis and makes it possible to avoid much of the structural clutter. Image segmentation provides a logical transition from the units of pixels to larger units in maps more relevant to detect the changes in these. In this context, this research project suggests to use region based classification of VHR satellite data in the change detection processe for updates of vector database.

A rapidly increasing demand for accurate and updated geo-spatial information requires advanced techniques for extracting and providing relevant information. The presented work was carried out in a 3654 km² sized area in the region of Stuttgart/Germany, characterized by high dynamic growth and steady economic development. The project Biotope Information and Management System (BIMS) provides and monitors aggregated spatial units relevant for regional planning tasks. We discuss experiences from the first phase of the project, in which we developed an adaptive per-parcel approach for delineating elementary units using SPOT-5 MS data (5 m GSD). The target geometry was pre-defined by digital cadastre data from 2005, but not all existing boundaries were retained: some were dismissed, others introduced. We followed a threefold strategy: (1) a parcel with internal homogeneity remains the same; (2) neighboring parcels with similar spectral behavior are merged; (3) a single, heterogeneous parcel is split and new boundaries are generated. By this, the initial number of units dropped to one fourth. The majority of the units were merged due to trans-boundary homogeneity, one fourth was subdivided. Assets of this approach are its cost-efficiency, the high matching degree of the produced geometry and the transferability to similar cases because of the standardized character of the data sets involved.

The Cerrado is a savanna ecoregion with grassland and woodland subtypes covering ~one-quarter of Brazil and is considered to be a biodiversity hotspot, threatened by land-use conversion. Hyperspectral remote sensing enables spatio-temporal monitoring, while providing the possibility of vegetation-mapping at a high level of specificity. However, because imaging spectrometer data availability/coverage is currently limited, a need exists for effective exploitation of multispectral satellite imagery with broad-area spatial coverage. The objective was to assess the utility of hyperspectral Hyperion and multispectral CBERS-2 satellite imagery in discriminating among Cerrado subtypes and agricultural classes. Temporally-coincident field-transect data for Cerrado physiognomies and agricultural sites were collected, including biophysical metrics. Nonmetric multidimensional scaling and hierarchical cluster analysis were used to identify potential environmental gradients of biophysical groupings. Four Cerrado subclasses were identified: Campo Limpo (Open Cerrado Grassland), Campo Sujo (Shrub Savanna), Cerrado Típico (Wooded Cerrado), and Cerrado Denso (Cerrado Woodland). Subclasses were then merged, forming two Cerrado subclasses. To facilitate sensor intercomparison, image classification involved PCA transformations, followed by unsupervised clustering of the component images. Results indicate that both dimensionality-reduced Hyperion and CBERS datasets were sufficient in distinguishing between the two more general Cerrado subclasses and agriculture, but the Hyperion-derived classification was more accurate.

Hyperspectral thermal IR remote sensing is an effective tool for the detection and identification of gas plumes and solid materials. Virtually all remotely sensed thermal IR pixels are mixtures of different materials and temperatures. As sensors improve and hyperspectral thermal IR remote sensing becomes more quantitative, the concept of homogeneous pixels becomes inadequate. The contributions of the constituents to the pixel spectral ground leaving radiance are weighted by their spectral emissivities and their temperature, or more correctly, temperature distributions, because real pixels are rarely thermally homogeneous. Planck's Law defines a relationship between temperature and radiance that is strongly wavelength dependent, even for blackbodies. Spectral ground leaving radiance (GLR) from mixed pixels is temperature and wavelength dependent and the relationship between observed radiance spectra from mixed pixels and library emissivity spectra of mixtures of 'pure' materials is indirect. A simple model of linear mixing of subpixel radiance as a function of material type, the temperature distribution of each material and the abundance of the material within a pixel is presented. The model indicates that, qualitatively and given normal environmental temperature variability, spectral features remain observable in mixtures as long as the material occupies more than roughly 10% of the pixel. Field measurements of known targets made on the ground and by an airborne sensor are presented here and serve as a reality check on the model. Target spectral GLR from mixtures as a function of temperature distribution and abundance within the pixel at day and night are presented and compare well qualitatively with model output.

The study aims to detect and map alteration halos in Erongo Namibia using Hyperion dataset. Detail surveys and investigations are possible considering hyperspectral sensors capability to render plenty of spectral information from observing surface of earth. In the term of mineral detection there are particular challenges. Main problem is due to very small size of mineral grain comparing to even those data possessing finest ground resolution. One of the methods has been invented for objects smaller than ground resolution of dataset is linear mixture model (LMM). This allows us to estimate abundances of targets in each pixel of scene after determination of endmembers. Regarding to the challenge about mineral detection task, as a matter of fact, finding pure pixels of the data in mineralogical scale is often impossible. Therefore we are able to determine the purest pixels which are a mixture of several minerals themselves and spectral profile of them consists of absorption features of those detectable minerals.

Empirical distribution functions were applied for removing long-term errors from BT data derived from AVHRR sensor on NOAA environmental satellites. This paper investigates BT stability in the NOAA/NESDIS Global Vegetation Index (GVI) data set during 1982-2003. This period includes five NOAA satellites. Degradation of BT over time for each satellite was estimated for geographical location in China. The method of matching empirical distribution function (EDF) improves the time relative stability of BT data for all satellites, especially NOAA-9, -11 and -14.

Multiple regression is a common technique used when performing digital analysis on images to obtain continuous, quantitative, variables (as temperature, biomass, etc). In this scenario pixels are treated as samples from which several independent variables are known; when remote sensing images are available, the different spectral bands offered by a given sensor are often used as independent variables. The dependent variable is also a quantitative variable, such as a forest inventory variable or a climate variable (e.g., temperature). This paper presents an evaluation of the implications of JPEG2000 lossy compression when applied to these regression processes. Annual minimum and annual mean air temperature are modelled using two methods according to the independent variables used: only geographical, and geographical and remote sensing images as independent variables. Raster matrix representing independent variables were compressed using compression ratios from 50% up to 0.01% of the original file size. Results have revealed that, even at high compression ratios, practically the same accuracy, measured with independent reference climatic stations, is obtained, so JPEG2000 seems an interesting technique not heavily affecting these common modelling approaches.

The Geostationary Operational Environmental Satellite (GOES) program is developing a new generation sensor, the Advanced Baseline Imager (ABI), to be carried on the GOES-R satellite to be lunched in approximately in 2014. Compared to the current GOES imager, ABI will have significant advantages for measuring land surface temperature as well as to providing qualitative and quantitative data for a wide range of applications. Specifically, spatial resolution of the ABI sensor is 2 km, and the infrared window noise equivalent temperature is 0.1 K, which are very close to the polarorbiting satellite sensors such as AVHRR. Most importantly, ABI observes the full disk every five minutes, which not only provides more cloud-free measurements but also makes daily temperature variation analysis possible. In this study we developed split window algorithms for the LST measurement from the ABI sensor. We generated the ABI sensor data using MODTRAN radiative transfer model and NOAA88 atmospheric profiles and ran regression analyses for the LST algorithm development. The algorithms are developed by optimizing existing split window LST algorithms and adding a path length correction term to minimize the retrieval errors due to difference atmospheric path absorption from nadir view to the edge-of-scan. The algorithm coefficients are stratified for dry and moist atmospheric conditions, as well as for the daytime and nighttime. The algorithm sensitivity to land surface emissivity uncertainty is analyzed to ensure the algorithm performance.

The ONERA RAMSES system is a flexible SAR system in constant evolution, developed mainly as a test bench for new technologies and to provide specific data for TDRI (Target Detection, Recognition and Identification) algorithm evaluation. It is flown on a Transall C160 platform operated by the CEV (Centre d'Essais en Vol). In this article we compare the SRTM X-SAR DEM and the ONERA's RAMSES SAR sensor DEM over the same area: the St Mandrier peninsula in south of France. Our study is composed of two parts: after a brief description of our sensor we firstly present a global assessment over the peninsula and secondly we focus on the large buildings to evaluate the detection and measurement capabilities of the SRTM products.

Accurate simulation tools for the design of space borne synthetic aperture radar systems (SAR) are compulsory for the analysis of the system's capabilities, because ground based experimental tests are in most cases impossible and very costly. Through a simulation process it is possible to analyze the image quality parameters for a given system configuration or evaluating the effects in SAR images when this configuration is changed. A new fast SAR image simulator (SARIS) is currently under development on the basis of an existing toolset called SAR end-to-end simulator (SETES). This image simulator produces SAR images by using the point spread function (PSF) of a focused point target response in contrast to SETES's very expensive raw data generation module. In SARIS the SAR image is produced through a convolution of the PSF with the so-called reflectivity map of the scene. In this paper first simulation results with a prototype of SARIS are given to show effects like motion errors and low peak-to-side-lobe ratios.

The ONERA RAMSES system is a flexible SAR system in constant evolution, developed mainly as a test bench for new technologies and to provide specific data for TDRI (Target Detection, Recognition and Identification) algorithm evaluation. It is flown on a Transall C160 platform operated by the CEV (Centre d'Essais en Vol). This paper gives an overview of the system, lists briefly the latest upgrading (electronic and SAR processor) to acquire high resolution data without deramp-on-receive mode and then presents the recent campaign which took place in Sweden, with simultaneous acquisitions at P and L bands.

The HRSC-AX (High Resolution Stereo Camera - Airborne eXtended) systems are multiple line "pushbroom" instruments developed by the German Aerospace Center (DLR). The dpp-profile is a simple correction method to remove additive path radiance based on view-angle dependent histogram statistics; it neither requires an absolute calibration nor a co-registration of the image data. It was developed at the DLR in Berlin-Adlershof and has been applied to several thousand km² of HRSC-AX data for more than 2 years. The results are very encouraging: the dpp-method was applied to more than 90% of the examined targets (cities and rural areas), in less than 10% of the targets the method was not used due to the ambiguity of the statistics and the risk of tampering the data. In all selected targets the blue and the green channel were corrected, whereas the red channel was only corrected in about 50% of the selected targets where the statistics agreed with the expected pattern. A degradation of the image quality after the correction was never observed. Depending on the amount of atmospheric effects the visual differences between corrected and uncorrected image data can be described from almost not visible, same quality to strongly improved. Also the statistics of the improved image mosaics are more conclusive and show less systematic fluctuations. The dark pixels, which are used in the statistics of the dpp-method, resulted to be in almost all examined targets dark shaded vegetation. In areas clear of vegetation other wide spread dark objects (like water bodies) have to be identified as suitable dark objects, which is not always possible. Further investigation concerning the influence of the dpp-correction on classification results and an extension of the dpp method to derive atmospheric parameters like optical depth are currently in progress.

We describe the concept for a logic-tree based geographic information system (GIS) that can infer subsurface geology and material properties using geoinformatics concepts. A proof-of-concept system was devised and tested integrating the capabilities of traditional terrain- and image-analysis procedures with a GIS to manipulate geospatial data. Structured logic trees were developed to guide an analyst through an interactive, geologic analysis based on querying and mentoring heuristic logic. The hypotheses were that a GIS can be programmed to 1) follow the fundamental logic sequence developed for traditional terrain- and image analysis procedures; 2) augment that sequence with correlative geospatial data from a variety of sources; and 3) integrate the inferences and data to develop "best-guess" estimates. We also developed a method to estimate depth to bedrock, and expanded an existing method to determine water table depth. Blind evaluations indicate that an analyst can infer the correct geologic conditions 70-80% of the time using this method. This geologic analysis technique can be applied wherever an estimate of subsurface geology is needed. We apply the results of our geological analysis to the prediction of local site specific seismic propagation. Comparisons are made with synthetic seismograms computed from a limited set of geological vignettes.

The Institute of geological and Mineral Exploration of Greece (I.G.M.E.), in the frame of CSF 2000-2006 (Community Support Framework 2000-2006), has been implementing the pilot project titled "Collection, Codification and Documentation of geothematic information for urban and suburban areas in Greece - pilot applications". Geological, geochemical, geophysical, geotechnical, hydrogeological and other data concerning the urban and surrounding areas of Drama (North Greece), Nafplio & Sparti (Peloponnesus) and Thrakomakedones (Attica) is collected. Drillings, geological and geotectonic mapping (scale 1:5.000) and other "in situ" measurements and field works are taking place. The contours of the 1:5.000 topographic maps are digitized and a high detail DEM is created. The DEM and ground control points collected with GPS are used for the Orthorectification of very high-resolution satellite data. Then, the orthorectified satellite data is used for the land use classification and the urban area mapping. All initial and derived analogical and digital data is compiled and processed in specially designed geo-databases in GIS Environment. The final results will be presented in geothematic maps of different scales (1:5.000, 1:10.000 etc). Thematic maps (geological, geotechnical, geochemical, geophysical etc) and other digital data such as geodatabases, DEMs will be available to all, public or private sector, concerning geological environment in urban and suburban areas. All these data will constitute the essential base for land use planning and environmental protection in specific urban areas. Through this pilot project, new scientific approaches, methodologies and standards will be developed and improved in order to be applied to other future projects concerning capital centers of the country.

The island of Rhodes represents an uplifted easternmost segment of the Hellenic forearc extending between Greece and Turkey, which is associated with the subduction of the African plate below the Aegean. Middle Miocene-Pleistocene sedimentary basins, are separated by a stack of Alpine nappes of the Hellenide orogen exposed in uplifted fault blocks such as Plattenkalk series (Attaviros Group), Gavrovo-Tripolitza series (Lindos Group), Pindos-Olonos series (Profitis Ilias Group) and several outcrops of Pelagonian series with ophiolitic mélanges. These pre-Neogene formations are dominated by low-grade metamorphic sediments, which were folded and faulted during several phases of the alpine orogeny. The Rhodes region was uplifted in the Middle Miocene after which subsidence and deposition of sediments took place in the Upper Pliocene and Lower Pleistocene. In this paper we present the combined use of remote sensing and GIS techniques for the geological mapping of Rhodes Island at a 1/50.000 scale. The geological formations, geotectonic units and the tectonic structure were recognized in situ and mapped. Interpretation of medium resolution satellite images (Landsat 7 ETM and Terra ASTER) has been carried out in order to detect the linear or not structures of the study area. The in situ mapping was enhanced with data from the digital processing of the satellite data. All the analogical and digital data were imported in a geodata base specially designed for geological data. After the necessary topological control and corrections the data were unified and processed in order to create the final layout at 1/50.000 scale.

This paper describes a mechanical monolithic sensor for geophysical applications developed at the University of Salerno. The instrument is basically a monolithic tunable folded pendulum, shaped with precision machining and electric-discharge-machining, that can be used both as seismometer and, in a force-feedback configuration, as accelerometer. The monolithic mechanical design and the introduction of laser interferometric techniques for the readout implementation make it a very compact instrument, very sensitive in the low-frequency seismic noise band, with a very good immunity to environmental noises. Many changes have been produced since last version (2006), mainly aimed to the improvement of the mechanics and of the optical readout of the instrument. In fact, we have developed and tested a prototype with elliptical hinges and mechanical tuning of the resonance frequency together with a new laser optical lever and laser interferometer readout system. The theoretical sensitivity curve for both laser optical lever and laser interferometric readouts, calculated on the basis of suitable theoretical models, shows a very good agreement with the experimental measurements. Very interesting scientific result is that the measured natural resonance frequency of the instrument is ≈ 70mHz with a Q ≈ 140 in air without thermal stabilization, demonstrating the feasibility of a monolithic FP sensor with a natural resonance frequency of the order of 5 mHz with a more refined mechanical tuning.

The Shuttle Radar Topography Mission (SRTM) was launched on 11 February 2000 and 3 arc second data were publicly released in July 2004. Easy availability of SRTM 3 arc second data, covering almost 80% of the land surface on earth, has resulted in great advances in morphometric studies and numerical description of landscape features. In this study we introduce a new procedure using Neural Network - Self Organizing Map - to characterize morphometric features of landscapes.. We also investigate the effect of two resolutions for morphometric feature identification. Specifically we investigate how the SRTM 3arc second latitude / longitude data projected to UTM coordinates with 90 meter respectively 28.5 m grid, corresponding to Landsat TM data resolution, affect the morphometric characterization. Morphometric parameters such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived by fitting a bivariate quadratic surface with a window size of 5×5 for the 90 m data (450 m on the ground) and 9×9 for the 28.5 m data (about 250 m) . Kohonen Self Organizing Map as an unsupervised neural network algorithm is employed for the classification of these morphometric parameters into 10 exclusive and exhaustive classes. These classes were analyzed and interpreted as morphometric features such as ridge, channel, crest line, planar and valley bottom for both data sets based on morphometric signatures, feature space and 3D inspection of the area. The difference change detection technique was used between two DEMs (DEM-90 and DEM-28.5 m) to analyze differences in morphometric features identification. The results showed that the introduced method is very useful for identification of morphometric features. Increasing spatial resolution from 90 meter to 28.5 meter, can produce digital elevation models (DEMs) allowing more precise identification of morphometric features and landforms. Increasing spatial resolution overcomes the main constrains for morphometric analysis with SRTM 90 m data, such as artifacts, unrealistic feature presentations and isolated single elements in the output map. Increased spatial resolution together with the smaller window size emphasized local conditions but main morphometric features were preserved. An overall change of 66.36 % is observed for morphometric features in the 28.5 meter DEM. The most and least frequent changes occurred for class no.6 (moderate slopes, channel) with 82.74% and class no.7 (Gentle slope to flat, valley bottom, planar) with 43.31% respectively. Increasing spatial resolution can be applied also to watersheds studies like drainage modeling.

The Project called Sistema Rischio Vulcanico (SRV) is funded by the Italian Space Agency (ASI) in the frame of the National Space Plan 2003-2005 under the Earth Observations section for natural risks management. The SRV Project is coordinated by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) which is responsible at national level for the volcanic monitoring. The objective of the project is to develop a pre-operative system based on EO data and ground measurements integration to support the volcanic risk monitoring of the Italian Civil Protection Department which requirements and need are well integrated in the GMES Emergency Core Services program. The project philosophy is to implement, by incremental versions, specific modules which allow to process, store and visualize through Web GIS tools EO derived parameters considering three activity phases: 1) knowledge and prevention; 2) crisis; 3) post crisis. In order to combine effectively the EO data and the ground networks measurements the system will implement a multi-parametric analysis tool, which represents and unique tool to analyze contemporaneously a large data set of data in "near real time". The SRV project will be tested his operational capabilities on three Italian Volcanoes: Etna,Vesuvio and Campi Flegrei.

The volcanic ash detection procedures are based on Brightness Temperature Difference (BTD) algorithm using the thermal infrared channels centred around 11 and 12 microns of a multispectral satellite sensor. The Mie code has been is included in the retrieval procedure to compute the ash optical properties from the ash microphysical characteristics. The simulations has been carried out using MODTRAN radiative transfer model. The Nasa-Modis and the Noaa-Avhrr measurements of Mt. Etna eruptive plume occurred in November 2006 have been analyzed to retrieve the plume optical thickness, the particle effective radius and the size distribution.

Remote sensing is a key application in global-change scienceand urban climatology. Urbanization, the conversion of other types of land to uses associated with growth of populations and economy has a great impact on both micro-climate as well as macro-climate.Urban areas tend to experience a relatively higher temperature compared with the surrounding rural areas. This thermal difference, in conjunction with waste heat released from urban houses, transportation and industry, contribute to the development of urban heat island (UHI). The aim of this study is to examine the changes in land use/cover pattern in a rapidly changing area of Bucharest metropolitan area in relation to urbanization since the 1984s till 2005 and then to investigate the impact of such changes on the intensity and spatial pattern of the UHI effect in the region.Investigation of radiation properties, energy balance and heat fluxes is based on satellite data from various sensors Landsat TM, ETM+, ASTER and IKONOS.So called effect of "urban heat island" must be considered mostly for summer periods conditions and large European scale heat waves.

This study aims to establish a practical image analysis method for the use of middle-scale resolution images acquired by the multi-spectral sensors such as Landsat-7/ETM+, Terra/ASTER and ALOS/AVNIR-2 as the complementary data sources of higher resolution images such as Quickbird for the purpose of environmental monitoring of wide-range areas. For this purpose, an image analysis based on mixture is investigated as one of the effective approaches. As the information target, we selected vegetation cover rate (VCR) in urban area because it is one of the important environmental factors to affect urban environment issue such as heat island phenomena. In order to realize easy and efficient computation for estimating the mixture rate of vegetation categories, the linear mixture model using two main categories including vegetation and non-vegetation, is applied in combination with the least square estimation of multi-regressive coefficients for vegetation cover rate (VCR) and non-vegetation cover rate (non-VCR) with several bands data by multi-spectral sensors. In addition, two sub-categories for both of vegetation and non-vegetation categories are considered to specify representative pixel values as correct as possible, that is, trees and grasses for vegetation, and buildings and bare-soils for non-vegetation respectively, and their optical mixture rates are estimated as well as the mixture rate of vegetation and non-vegetation categories. For this purpose, an iterative procedure is adopted, in which each mixture rate of two sub-categories for vegetation and non-vegetation is varied by ten percent steps and the least square estimation is applied with all combinations of mixture rates of sub-categories for vegetation and non-vegetation. The experiments for VCR extraction were conducted in the test site of Hiroshima-city and by using multi-spectral data acquired by Landsat-7/ETM+, Terra/ASTER, and ALOS/AVNIR-2. The accuracy for VCR extraction was evaluated based on the comparison with the VCRs obtained by means of pixel-wise vegetation/non-vegetation classification of a Quickbird multi-spectral image. The result shows that the number of bands is one of the important parameters in general. However, it was verified that the combination of wavelength regions is more important than the number of bands. The result of this study suggests that the combination of wavelength regions is essential in middle-resolution multi-spectral images for vegetation cover rate estimation based on mixture analyses.

The urban population density declines from Central Business District (CBD) to city edge. Some studies have explored several mathematical forms to describe the relationship between population density and location. But these models assume that the city is single center and the urban population decline with concentric circle format. It is difficult to satisfy this requirement in actual cities. A distance variable, city edge distance, has been developed to solve the problem in this paper. Using city edge distance can express multi-centered city situation. The irregular city is easily transformed into a virtual concentric circle city. Then the Clark model and power exponential model are transformed into new format based on the city edge distance. A case study indicates that the transformed formats could successfully simulate the urban population density. But more applications are needed in different city types to validate and improve this distance variable.

The selection of suitable scales is one of the key issues in the monitoring of the land use, or more generally, in the study areas of ecology and geography. The scale change trend of land use in the mainstream area of the Tarim River in recent 50 years are lucubrated in this paper by interpreting the land use data in the 1950s, 1970s, 1990s and 2000 with the available maps and RS images. Taking the area of land use as the parameter in selecting the scales, the histograms of the patches in area as are charted. The normalized scale variances under 9 scales are calculated. By reinforcing the calculated results with the landscape indexes including the Shannon-Weaver diversity index, Simpson diversity index and fractal dimensions, the characteristics and scale change trends of the land use in the Tarim River Basin can be summarized as following: (1) The variance of the areas as patches in the region is in great disparity. The patch of sandlands is the largest and its proportion in the year of 2000 was 43.77%; (2) In the course of the study period, the frequency distribution of the areas of patches, sandlands, saline or alkaline lands, forest land and shrub lands in this region was in normal distribution. However, the positions of the peak values and the distribution patterns were different; (3) Normalized scale variance table reveals that the most suitable scale of land use in the region is at 1:50000 or in the range of 1: 50000~1: 100000 in general. The general patterns of the normalized scale variances in the Tarim River Basin in the 4 study periods were similar; (4) Comparing with the normalized scale variances, there were no significant distribution trends of the three landscape indices.

Based on Landsat MSS/TM images and CBERS-02 CCD data of Beitun Oasis in Ertix River Watershed in the years of 1972, 1989, 1999 and 2005, the landscape patterns for the past 30 years were analyzed. Using the GIS data collective platform, we calculated the landscape pattern conversion probability matrix, landscape pattern index, and contribution rates of major landscape components to characterize the impacts and responses of landscape pattern changes and landscape ecological processes. The results indicate that in this region the areas of farmlands, urban & rural residential lands and waters are increasing, the area of woodlands is decreasing, and that of grasslands is decreasing and then increasing. In the desert landscape patterns, the areas of sandlands and Gobi deserts & bare lands are decreasing after increasing, and those of saline or alkaline lands & marchlands are increasing obviously in the latter period. The features of landscape ecological process of urban & rural residential lands are concentrated in spatial pattern, but for grasslands and woodlands, those are fragmentized in spatial patterns. The landscape components convert very frequently, and the landscape pattern is not stable. Woodlands ecosystem function reducing and soil salinization and alkalization result in a negative influence on the local ecological system. It is essential to adjust the landscape patterns to rehabilitate and construct the fragile ecological system of modern oasis landscape ecosystem in arid area and use water resources reasonably, so that ecological environment and social economy is healthy and stable with sustainable development.

An Airport Qualification may be required for a pilot to receive qualification for the execution of an approach or departure from a terrain, weather, or procedure challenging airport. The FAA identified these challenging airports and calls them "Special Pilot Qualification Airports". This Qualification may be accomplished through a familiarization using airport images or through a familiarization flight with an authorized person. Currently, Jeppesen offers Airport Familiarization Charts. These charts depict approach procedure photos to a runway from the pilot's perspective and aerial views of the airport. Before the approach, Pilots make use of these photos to get familiarized with the airport, the runway layout, the approach and terrain. Jeppesen qualification charts cover all FAA identified airports and other challenging airports. A first prototype for generating "Synthetic Airport Familiarization" pictures and videos has been researched, developed, implemented and validated. Flight Information Data as well as Remote Sensing Data and their derived data was processed and visualized through Geo Information System (GIS). This paper describes a new possibility to generate airport familiarization images using Remote Sensing Data, terrain data, airport vector data, obstacles and approach procedure data through GIS. The objective is to replace analogues photos with synthetic pictures and also to generate new Airport Familiarization Videos. Finally, an overview of the potential feature extensibility of the Synthetic Airport Familiarization System is presented.

Here, we discuss an interesting concept that brings an added flexibility in chemo/bio sensing. We present system that can be switched photonically between two states, only one of which exhibits ion-binding behaviour. The system is based on molecular photoswitch spiropyran, which is probably the most studied compound exhibiting above characteristics. Upon irradiation with UV light the passive spiropyran (SP) molecule undergoes a heterocyclic ring cleavage that results with the formation of the merocyanine (MC) which is zwitterionic form capable of ion binding. In contrast to the uncharged and colourless spiropyran form, the merocyanine form is highly charged and can be utilized as ligand for other charged species. Moreover, it is strongly coloured, and the colour tells us which form is present. In addition it provides interesting information about the immediate environment of the merocyanine binding site (e.g. polarity, presence of certain ions etc.). In this work, we present a SP-based system in which SP is immobilized and protected within a polymeric matrix. Such system may be used for detection of metal ions in highly polar solvents, e.g. water. The response characteristics and kinetics of MC-Cr³⁺ complex formation and SP-MC switching within the polymer matrix have been determined. Simple light emitting diodes (LEDs) have been employed for photoswitching and colorimetric measurement of SP-MC switching and MC-Cr³⁺ complex formation as light sources and detectors.