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Comité International de Coordination du 17/12/1999 Point d'information VERSION PROVISOIRE

International audience; Current research presents study of the hydrological characteristics in the surroundings of the Laguna de Gallocanta (Spain) using remote sensing methods and combination of two GIS: ArcGIS and Idrisi GIS. The methodology includes following workflow: 1) selection of the multi-temporal Landsat images and creating color composites; 2) Masking and overlaying study area out of the whole image; 3) Calculation of the direction and length of stream flow in the lake basin; 4) assessment of possible risks of floods and stability of coastal slopes. The research has aim of hydrological assessment testing GIS tools for environmental monitoring. The images are presented by Landsat ETM+, MSS and TM scenes acquired on years 1976, 1987, 1992 and 2000, respectively, with a cloud cover of 0%. The images were georeferenced in ArcGIS and rectified to the Grid Coordinate System using available vector layers. The georeferenced rectified image was then re-projected to the UTM projection WGS84 datum. The combinations of various bands (channels 2-1-4, 5-4-7, and 2-4-7) were tested and visualized. Second step included processing DEM (Digital Elevation Model) Data acquired by the SRTM providing high quality elevation data. The pre-processing included mosaicing images and cutting out study area. The hydrologically conditioning the DEM was performed: the elevations are modified in order to model flow directions. Modelling flow direction and flow accumulation was performed on the conditioned DEM using ArcGIS. The next step included extracting hydrologic features such as stream directions, length and basin extents from a DEM. The pit removal tool was used to remove error pits from a DEM. Final step included calculations of the flow direction and flow length that demonstrate the direction of flow out of each cell. Finally, using Basin function all cells belonging to the watersheds of one or more target cells were calculated. The results shown that area has sensitive hydrological settings which explains its prone to the environmental change. The research demonstrated successful use of GIS for the purposes of hydrological modeling in the environmental studies.

Данная работа представляет результат геопространственного анализа выбранной территории (западная часть Турции, регион Измир) с комбинированным использованием космических снимков Google Earth, Landsat TM и ГИС ПО Erdas Imagine. В работе рассмотрены преимущества совместного использования космических снимков разной категории и детальности (мультиспектральные снимки Landsat TM и ETM+, а также Google Earth веб-сервис) вкупе с ГИС обеспечением для задач геопространственного анализа, часто решаемые в Высшей школе на курсах географии и наук о Земле. Данная работа приводит пример успешного изучения ландшафтов. Изучение распространения различных типов земной поверхности, моделированное с помощью Landsat TM и Google Earth позволяет анализировать динамику изменений ландшафтов, а также подтверждает возможность эффективного использования веб-сервиса Google Earth для тематического картографирования. Для классификации снимков были использованы технологии обработки снимков методами доступных модулей ПО Erdas Imagine. Веб-сервис Google Earth был успешно использован для верификации и валидации результатов картографирования с помощью модуля «Linking with Google Earth».

The study area is Šumava National Park (ŠNP), the largest of the four national parks (68,064 hectares) located in the south-west of Czech Republic, on the border with Germany. Since 1990 it has been the protected Biospherical Reserve of UNESCO (Bilek et al. 1990). Being a unique mosaic of natural and secondary habitats of exceptional natural value of European-wide significance, the components of the ŠNP represent is the largest terrestrial significant part of the Natura 2000 network in Czech Republic and Germany. For instance, the fauna species of ŠNP include protected important examples, such as e.g. lynx, otter and peregrine (Bláha et al. 2013). The ŠNP is established as a special regime of environmental protection with unique biological communities that are the most precious objects of protection. The area is represented by the vast wooded areas, mountain spruce and mixed forests of various ages, peat bogs, meadows biotops, moors and lakes. Altogether, they create a unique mosaic of biotopes, which encompass a variety of rare, endemic and endangered species, e.g. lynx, pearl mussel, owls, songbirds. The research aim was to analyse how the ecosystem landscapes located within the study area changed since 1991 until 2009 (18-year time span) using remote sensing data and GIS. The data include GIS layers in two forms: raster layers as Landsat TM images with 18-years interval (1991 and 2009), and vector thematic layers in ArcGIS shape-file format. The data were stored in a GIS project. Technically, the GIS project were generated in Quantum GIS (QGIS) software. Methodologically, the applied workflow used in this research included following steps: 1) Data capture, unpacking and storage. 2) Organizing GIS project. 3) Geo- referencing and re-projection. 4) Activating GDAL and GRASS remote sensing plugins. 5) Preliminary data processing. 6) Generating contour layers from DEM. 7) Colour composition from 3 Landsat TM bands. 8) Defining Region of Interest: raster mosaicing and clipping. 9) False colour composites (bands 4-3-2). 10) Setting up parameters for classification. 11) Image classification using K-Means algorithm. 12) Pattern recognition. 13) Spatial analysis. The detailed illustrations of these steps are shown in the proposed presentation. The GIS analysis is used to test the importance of the natural and human-induced land used changes for survival of the important floristic locations in several case studies. The outcomes are illustrated by two maps showing geographic distribution of land cover types within the study area in given time periods of 18-year time span. The results demonstrate visualization of the ecosystems in 1991 and 2009 showing dynamics of land cover types in the given time. The work demonstrated effective application of QGIS software combined with multi-source data (remote sensing and geoinformatics) for the purpose of environmental protection of precious areas of the Šumava National Park.

International audience; The study area is located in Central Europe and focusing on Mecsek Hills, a low mountain range in the south western part of Hungary. The region of central Europe includes complex mis of elements from mediterranean and continental climate, since the area is located in transitional zone of sub-atlantic and sub-mediterranean climate types. Mecsek Hills is a unique region of the Hungarian environment. Its central European location specifies distribution of diverse landscape types, formed under conditions of transitional climatic settings, mixed from sub-atlantic to sub-mediterranean. The distribution of ecosystems in the region illustrate adaptation of soil properties to actual climate conditions. As determined by the present-day climatic and geomorphological settings, soils in the Mecsek region are rich in nutrients, and landscapes are characterized by various mixed soil types. Brown forest soils predominate on hilltops of Mecsek, while alluvial soils on floodplains. According to the underlying soil types, the landscapes are characterized by mixed vegetation types. The most typical is Pannonian mixed forests of temperate broadleaf and mixed forests, common in Euro-Siberian region. There are also forests of Turkey and hornbeam oak in the catchment area of Mecsek region. The dominating vegetation coverage types on the slopes of Mecsek Hills include beech forests, ravine forests and oaks. Some regions of the Mecsek Hills include unique biogeographic areas with endemic species, not founded elsewhere in Carpathian Basin. The overall climate change, especially the global temperature increase, controls balance of heat and water budget on the Hungarian landscapes, and has considerable impact on the agricultural landscapes. In the past decades there were changes in climate, detected in the Carpathian Basin region, which illustrate general fluctuations in the climatic settings in Hungary and change of overall average air temperatures. Combination of the satellite images with GIS techniques is a key method for land patterns identification and classification of ecosystems. Research methodology is based on the GIS spatial analysis tools and classification of Landsat TM image, which was used for visualizing landscapes. An ILWIS GIS was applied to perform spatial analysis and mapping. The research algorithm is clustering, which classifies pixels with similar value of Digital Numbers (DNs) to thematic classes. As a result, spatial analysis on distribution of land cover patterns was performed. Data processing include image pre-processing, enhancement, classification, spatial analysis and interpretation. A land cover map was created by classifying study area into land categories. The core method used in the current work for the interpretation of imagery is clustering algorithm. This method is based on the remote sensing general principle that each unique pixel on a multichannel image has spectral signature defined by the reflectance of its DN in each spectral band. The DNs of pixels create unique signatures for various objects, distinguishable from other objects. Multispectral cluster classification was applied for the Landsat TM image, by extracting information about values of the pixels DNs, analyzing their spectral signatures.Current work demonstrated analysis of climate factors affecting ecological settings of Mecsek Hulls, and local landscapes visualized by means of ILWIS GIS and remote sensing data (Landsat TM). The results consist in recognized distribution of land use patterns. ILWIS GIS is a convenient open source GIS, useful for spatial analysis and land use monitoring. Clustering method is useful for ecological mapping, since it enables objective identification of the land types in regions characterized by high land heterogeneity and complex structure, such as agricultural fields mixed with natural land cover types. The experience of Landsat TM imagery processing by means of ILWIS GIS, described in the current work, is a contribution towards agricultural mapping.

isbn: 978- 985-481-315-8; International audience; The emphasis of this research is to demonstrate application of Landsat satellite imagery as a major resource for student and educational research. Landsat images are highly useful and strongly recommended for educational purposes as they are provided free of charge and timely regular geospatial data with 30-m resolution covering any places of the Earth. The case study describes mapping land cover types in ecosystems. It details how exactly satellite images can be used for geospatial research step by step. The research methodology is based spatial analysis tools of the open source GIS software: Quantum GIS and ILWIS GIS. The pre-processing of the imagery included data import and settings of colors and contrast. The contrast of the Landsat TM imagery has been enhanced using «Stretch To MinMax» function in QGIS viewer. The series of Landsat TM images, which cover Bovanenkovo region in Yamal peninsula with 30-m resolution, were initially stored as raster file in Erdas Imagine (.img) format. The images were imported to ILWIS from .img into ILWIS .mpr raster map format (ASCII) using GDAL. After converting, each image contained collection of 7 Landsat raster bands, as well as theirs metadata stored in Map List (.mpl) file, information about georeference (.grf) and coordinate system in .csy file. The coordinate systems of the Landsat imagery is based on the WGS 1984 datum. To improve the quality of image and visibility of coastal lines, the image enhancement of the Landsat bands was performed using Edge Enhancement linear filter from the Image Processing menu. Stretching filter was applied to the bands, in order to improve color contrast: since not all pixel values have been used in the image, the actual diapason range was stretched linearly to 0-255 values. To visualize spectral information from the Landsat image, a color composite map has been created using combination of three raster images of the individual bands. Supervised classification of the raster imagery includes image analysis aimed at recognizing class membership for each pixel. The main research method used in current research is supervised classification, which enabled to assign land cover classes by adjusting classification parameters and thresholds in DN values of spectral signature of pixels. The principle of Minimum Distance method, used for pixels classification is based on the calculating of shortest straight-line distance in Euclidian coordinate system from each pixel’s DN to the pattern pixels of land cover classes. The researched described successful application of Landsat remote sensing data and GIS for environmental studies.

International audience; The emphasis of this research is application of spatial analysis using clustering algorithm, and remote sensing data (Landsat TM imagery) for agricultural mapping of land cover types. The study area covers Mecsek Hills region, located in south-western Hungary. This region is characterized by high land heterogeneity and complex landscape structure, caused by intense agricultural land use in the region with mixed vegetation type and high environmental value. The research data consists in Landsat TM scenes taken for years for years 1992, 1999 and 2006. The methodology is based on cluster classification algorithm available in ILWIS GIS. Based on clustering technique, the agricultural land cover classes were identified by association of pixels on the Landsat TM scenes to thematic clusters. Different land use types were classified, which include natural vegetation coverage, anthropogenic areas and agricultural fields, sub-divided to various crop types. Once classification was complete, agricultural thematic maps have been created. The final research output consists in three independent agricultural thematic maps of land cover types for years 1992, 1999 and 2006.

Longtemps considérés comme des acteurs majeurs de l’aménagement du territoire au niveau régional, les Universités et les établissements d’enseignement supérieur et de recherche, connaissent aujourd’hui des logiques de regroupement à l’échelle nationale (PRES, puis COMUES), alors que le contexte concurrentiel s’accroit à l’échelle internationale. L’évolution des effectifs inscrits et diplômés, ou le « classement académique de Shanghai », sont fréquemment considérés comme les seuls indicateurs d’attractivité et de réussite des universités. Cependant, devant les nombreux défis de société qui se posent aux institutions universitaires, il semble aujourd’hui nécessaire de mieux comprendre le fonctionnement global de ces établissements. Comment définir les périmètres d’action et d’influence des établissements d’enseignements supérieurs et de recherche ? Quels sont les phénomènes géographiques expliquant leurs développements et découlant de ceux-ci ? Penchons-nous sur l’Université de Poitiers, avec l’exploitation cartographique de données internes afin de mieux appréhender ses relations avec les territoires et les acteurs locaux : La géographie de la commande publique, la localisation des lieux de stages étudiants, les territoires d’apprentissages (Attractivité de la filière d’apprentissage et contribution des acteurs locaux).

International audience; Current work presents results of the terrain data processing for hydrological analysis using two GIS software: ArcGIS and Idrisi GIS. The aim of the research is to analyse the situation of the river basin in the selected study area (Gallocanta, Spain), to assess possible risks for floods and landslides. The study aims to understand the processes and spatial and temporal patterns of surface-climate interaction in a watershed to assess the impacts of the local climate change on the hydrological conditions of the lake's surroundings and to develop geomorphic analysis using SRTM input data, for effective environmental management.

International audience; This research is focused on the effective management of spatial data: organizing geographical and environmental data of the Finnish ecosystems in a structured and systematical way for further environmental assessment. The study area is situated within central Finland and some data in the North Finland. The work is aimed on the structuring and management of spatial data for environmental assessment. The data were organized in an ArcGIS project. The data were collected during the last 20 years. The data build up core environmental information cluster for the biogeochemical soil analysis of the ecosystem components. To represent data measurement placements within the study area and for additional spatial or thematic information: satellite imagery, shaded maps, pictures captures during the expeditions and any text describing the places. The mapping project FINLAND.mxd has been started using ArcGIS 9.1 software. The research has been carried out on the Biogeochemistry Research Unit, Faculty of Natural and Environmental Sciences, Department of the Environmental Sciences, University of Kuopio (Eastern Finland).

Работа оценивает изменения в типах растительного покрова российского Севера в течение последних двух десятилетий. Результаты проведенной классификации спутниковых снимкой показывают экологические аспекты изменения лесотундровых экосистем. Технически, обработка данных была выполнена в ПО ГИС ILWIS, с использованием методов дешифрирования и контролируемой классификации. Результаты позволяют выявить изменения в типах и распределении растительного покрова. This paper focuses on the assessment of the changes in land cover types in Yamal peninsula over the past two decades, which shows local environmental aspects of the climate change in North. Technically, the data processing was performed in ILWIS GIS, using semi-automated methods of image interpretation and supervised classification. The results of the classification analysis enable to identify changes in land cover types and their distribution in Yamal.

International audience; The paper studies changes in land cover types in tundra landscapes during the past two decades. The study area is located in the Yamal Peninsula, north-central Russia. The main objective of this research is to analyse changes in vegetation distribution and land cover types over the area of Yamal Peninsula. Methodology of the work aims at technical application of the remote sensing and GIS tools for studies and includes georeferencing, creation of color composites, supervised classification. The research data includes Landsat scenes. The research method consists in Landsat image processing (Fig.1), georeferencing via the Google Earth and spatial analysis performed in ILIWIS GIS. The choice of Landsat scenes for land cover mapping is explained by their well-known advantages of application in geosciences and cartography. The final outcomes show changes in the vegetation coverage and land cover classes in Bovanenkovo region of the Yamal Peninsula, which happened during the past two decades. The results are received by comparing and analyzing of two classified maps covering the same geographic region with time span of 23 years: in 1988 and 2011. The changes mostly concern types of land covers and overall increase of shrubland and willows. It can be explained by the complex environmental changes in Arctic regions, which leads to ―greenness‖ processes, or unnatural increase of willows.

The study area is focused on the coastal city of Taipei. The environmental changes of the city were detected by geospatial analysis of the satellite images. The areas occupied by different landscape types were calculated and analysed. It was detected that various districts were developing with different rate and intensity due to the complex factors of both human and ecological origin. The results showed intensive urban development, decline of green areas, increase of urban spaces since 1990. Область исследования сосредоточена на прибрежном городе Тайбэй (Тайвань, Китай). Экологические изменения города были обнаружены с помощью геопространственного анализа спутниковых снимков. Площади, занимаемые различными типами ландшафта, были проанализированы. Было обнаружено, что разные части города развиваются с разной скоростью и интенсивностью из-за сложного сочетания антропогенных и природных факторов. Результаты показали процессы интенсивной урбанизации, деградацию зеленых ареалов, увеличение площадей застроенных территорий с 1990 года.

International audience; Environmental mapping is a necessary tool for the geoscience research in the university classes of geography, GIS cartography and mapping. However, GIS methods of processing remote sensing data are often being discussed, and the optimal approaches are disputed. This work reports innovative approach of the processing Landsat TM satellite image in ILWIS GIS software using unsupervised and supervised classification methods. The methods of ILWIS GIS are compared and the results described in this paper. The methodology is based on the use of supervised classification of the satellite image, an innovative method in geographic research. Supervised classification of the raster imagery aimes at the recognizing of the class membership for each pixel during image analysis. The supervised classification of the multi-spectral imagery has been performed using 'Classify' operator in ILWIS applied to Landsat TM 1988. The classification process included following steps. First, the models of the classes were defined by creating a "sample sat" in ILWIS GIS. Namely, the training pixels with similar spectral values were defined and selected as representations for various classes. These pixels have contrasting colors, visually visible and distinguishable on the image, which serve as training areas for diverse classes. The sample pixels were defined in the Sample Set Editor in ILWIS, which was initially created in the main menu. A created Sample Set has a reference to the set of Landsat bands (1-7), which are needed to create sample statistics. After assigning pixel sets, a raster polygon map was automatically created with .mpr file extension. It contained sample pixels, location and legend, i.e. the names of the classes allocated to pixels. Easy interpretation of the image strongly depends on the optimal color composite map. The classification has been completed in interactive way, using several attempts of creating training samples, selecting various sample sets and respective pixels selected in Sample Set Editor using domain for classification ―Landclasses. The classification was repeated the until the final results were achieved. Shadows of green colors represent grass, shrub vegetation coverage and forest canopy. The results demonstrate successful application of the innovative approach of technical satellite image processing for the studies of the environment. The presented methods can be used by students of geography.

International audience; The study region is located in western Turkey, Izmir surroundings. The research examines spatiotemporal changes in landscapes of Izmir region, during the decade 1995-2005. Methods entail raster processing, spatial analysis and mapping by means of geospatial techniques and Erdas Imagine software. The Landsat TM images were geo-processed for detection of changes in the land cover types. It demonstrated effective use of the remote sensing data and spatial analysis for vegetation studies: a combination of digital satellite images, GIS cartographic tools and methods of spatial analysis of vegetation coverage are highly suitable and efficient for the monitoring of highly heterogeneous landscapes located in the area of intensive anthropogenic activities (western Turkey). The study contributes towards technical development of cartographic methods of the environmental monitoring.

International audience; The goal of the study is to perform comparative analysis of image processing methods, enabled by means of IDRISI GIS software. The purpose has two aims. First, a spatial analysis of land cover types in the coastal landscapes of western Estonia, Pärnu area, at two various temporal dates. Second, an overview of the technical methods of IDRISI GIS enabling to evaluate image processing. The main research method consists in classification of satellite images for resulting geoecological mapping of landscapes. The ISOCLUST classification enabled to create multi-spatiotemporal thematic maps of Pärnu area. The research method is based on the spatiotemporal analysis of the geospatial data, performed by means of GIS tools and remote sensing data. The images were downloaded from the Earth Science Data Interface, Global Land Cover Facility. The Landsat TM imagery include scenes of June 2006 and June 1992. Both images cover summer months, thus enabling vegetation coverage to be easily recognized.; 74 кулонометрия) и другие методы. Большое количество часов лабораторных ра-бот (70% от общего количества часов отведенных на изучение дисциплины), позволяют студентам на практике самостоятельно изучить основные современ-ные аналитические методы. Значительное внимание уделяется радиохимиче-скому анализу природных объектов, что связано с загрязнением окружающей среды естественными и искусственными радионуклидами. Поэтому в данном курсе предусмотрены работы по изучению методов дозиметрического контро-ля. Лабораторный практикум курса «Аналитические методы в геоэкологии» представлен следующими расчетно-аналитическими занятиями:-взвешивание на весах различных типов;-методы фильтрования растворов;-титриметрический метод определения общей щелочности и карбонатной жесткости воды;-спектрофотометрический метод определения концентрации вещества;-фотометрический метод определения нитритов с использованием реак-тива Грисса;-измерение рН раствора потенциометрическим методом;-оценка радиационной обстановки в помещениях и на открытом воздухе дозиметрическими методами. В рамках освоения каждого расчетно-аналитического занятия студенты свободно ориентируются в современных методах физико-химического анализа, могут выбирать аппаратуру в соответствии с типом образца, областью примене-ния, требуемой чувствительностью и точностью, использовать методы количе-ственного определения концентрации различных веществ и применять полу-ченные знания на практике для решения различных прикладных геоэкологиче-ских проблем. The goal of the study is to perform comparative analysis of image processing methods, enabled by means of IDRISI GIS software. The purpose has two aims. First, a spatial analysis of land cover types in the coastal landscapes of western Estonia, Pärnu area, at two various temporal dates. Second, an overview of the technical methods of IDRISI GIS enabling to evaluate image processing. The main research method consists in classification of satellite images for resulting geoecological mapping of landscapes. The ISOCLUST classification enabled to create multi-spatiotemporal thematic maps of Pärnu area (fig. 1). The research method is based on the spatiotem-poral analysis of the geospatial data, performed by means of GIS tools and remote

Introduction. The study area encompasses selected regions of the Brussels municipality, Belgium. In the past years the city of Brussels is experiencing intensification of the density of building structures. Unlike in some other European cities, where the most evident problem is urbanization and expansion of the city margins to the suburbia, the urban structure Brussels is the intensification of the buildings density in the city centre and the existing dwelling districts. Thus, the city structure tends to become more intense and dense, due to the process of filling the empty spaces in the urban patterns and high level housing. Another example of urban processes in Brussels is reorganisation of the industrial areas. At the same time, monitoring vegetation areas is essential for environmental sustainability of the capital city. The lack of the green spaces may cause ecological instability and increase atmospheric pollution. For studies of the specific problems of the Brussels city the remote sensing data (raster image) was used together with NDVI function, in order to detect areas covered by city parks. Methods and ResultsStep I. The panchromatic image was loaded into project and processed.Step II. The image was segmented using the principle of Multiresolution Segmentation. This operation consists in the splitting of an image into segments in order to simplify complexity of the whole initial image. It is performed by the machine embedded logic based on mathematic algorithms and simplifying models. The general rule divides the area into regions according to the principle “neighbour pixels have similar parameters” (spectral reflectance value, texture, form, shape).Step III. All vegetation coverage has been detected and separated from other objects (impervious structures) using natural characteristics of the vegetation spectral reflectance. For this, first the arithmetic expression for Normalized Difference Vegetation Index (NDVI) was created in eCognition operators using formula for spectral reflectance in visible (VIS) and near infrared (NIR) bands. This enables to detect pixels of vegetation: NDVI=(NIR-VIS)/(NIR+VIS). After this operation was created, it was added to the conditions of objects processing.Step IV. After this, the operation of extraction of vegetation was performed. The logical condition for vegetation detection is that we assign all objects which have values of NDVI more than 0.3 to vegetation. This is based on the properties of vegetation: dense tree canopy usually have positive values of NDVI (0.3 to 0.8). On the contrary, other objects, which do not belong to vegetation, have low NDVI values. For example, water bodies have low reflectance in both spectral bands (band 3 and band 4). Therefore, they have very low positive and sometimes slightly negative NDVI values (depending on local hydro-chemical conditions, depth, etc). Bare soils usually also have small positive NDVI values (0.1 to 0.2), since their spectral reflectance in near-infrared bands is larger than in red ones. So, using this knowledge, the NDVI formula was applied, and green areas within the city of Brussels were distinguished. The objects with NDVI values more than 0.3 were assigned to the “vegetation” class.Conclusions. The urban landscapes have complex environmental and socio-economic function and serve as habitat and agricultural surface in the surroundings. Land cover studies supported by satellite image contribute to the development of urban management system. Using object-oriented approach together with GIS techniques applied to remote sensing data enables to perform geospatial analysis with focus on urban landscapes.

Le document suivant présente la mise en œuvre d'un modèle de données spatio-temporel pour conserver et interroger les objets géographiques qui matérialisent les limites communales depuis 1794.

International audience; La problématique de l'eau a envahi les sphères médiatiques et sociologiques, qu'il s'agisse du manque d'eau, des différentes formes de vulnérabilité liées aux inondations, à l'épuisement ou à l'altération qualitative de la ressource en eau, ou encore de la protection et de la mise en valeur des hydrosystèmes. Cet ouvrage invite le lecteur à parcourir les lieux qui unissent la société à l'eau et à découvrir les géographies de l'eau à travers les avancées les plus récentes.

International audience; Cet article traite de la réalisation d’un Système d’Information Géographique et d’Aide à la Décision pour optimiser la gestion des ressources forestières. A travers cette réalisation, on s’interroge sur la place de l’information géographique dans le processus de concertation et dans l’approche du développement durable. Les données cadastrales, les images satellites, les données cartographiques et photographiques de terrain sont présentées. Les traitements effectués et l’intégration de l’ensemble sont exposés. Les résultats obtenus fournissent une connaissance inatteignable sans recours à ces outils. Ils mettent en lumière l’importance des images satellites et de la composante spatiale de l’information géographique. Dans le contexte actuel attentif au développement durable, elle apparaît nécessaire à la prise de décision.

Cet article traite de la réalisation d’un Système d’Information Géographique et d’Aide à la Décision pour optimiser la gestion des ressources forestières. A travers cette réalisation, on s’interroge sur la place de l’information géographique dans le processus de concertation et dans l’approche du développement durable. Les données cadastrales, les images satellites, les données cartographiques et photographiques de terrain sont présentées. Les traitements effectués et l’intégration de l’ensemble sont exposés. Les résultats obtenus fournissent une connaissance inatteignable sans recours à ces outils. Ils mettent en lumière l’importance des images satellites et de la composante spatiale de l’information géographique. Dans le contexte actuel attentif au développement durable, elle apparaît nécessaire à la prise de décision.

International audience; Methods of GIS mapping of environmental of the Barents and Pechora Seas under the influence of both natural and anthropogenic factors have been devised. For integrated GIS project of the Russian Arctic Seas several thematic layers have been created. These layers characterize the natural conditions of the Seas, the information on income of pollutants from different sources, possible ways of their transfer and deposition in bottom sediments. The analysis of the maps of geoecological situation of the Barents and Pechora Seas allows picking out the next geoecological regions: relatively safe, potentially dangerous and of high ecological stress. Methods of GIS mapping of sea territories for the means of estimating their ecological condition formed under the influence of both natural and anthropogenic factors are being developed. Scientists believe that this situation is due to several reasons: lack of firm evaluation criteria of sea pollution, imperfection of modern technologies and methods of ecological monitoring of the sea water areas. The analysis of the information on sea ecology that has been gathered up to now shows dissimilar data coverage of littoral and open water areas. Most of the information is provided for shelf areas of the water objects. Also there is a great diversity in the observation time and space. Retrieving methods of ecological information provide a diverse character of observations. As a result there is a sure lack of primary data and reliable information that is necessary for forming data banks and bases. For the means of producing a series of analytical and complex maps of Barents and Pechora seas with the use of GIS technologies we have analysed and worked with different data concerning natural and anthropogenic factors that determine ecological condition of the seas. Also we have developed a method of forming data base that includes maps of different scales, topics and numerous statistics. The GIS ArcInfo has been used to analyse topological thematic layers. Such layers as hydrography, sea shore, populated areas, administrative and political borders, a coastal line, morphological features of a sea bottom, etc. were created. ArcView GIS was used to create an initial database about pollution of the sea environment of the investigated seas, which should be extended and updated the later stages of the project. Information on different sources of water pollution is structured as DBF tables, containing data on the concentration of different pollutants (radioactive substances, petroleum hydrocarbons, pesticides, heavy metals) for different points of the Barents and Pechora seas areas. Further tasks (visualization, editing, combining and analysing different layers of information, modelling, creation and editing of legends and attribute tables, charts, layouts) were performed using ArcView GIS. As a result, we have developed several thematic GIS layers that characterize natural conditions of sea water areas and that provide information on income of pollutant from different sources, possible ways of their transfer and precipitation in bottom sediments. First of all is the topography of the sea bottom. The shelf of the Barents Sea is deeper than the ones of other Arctic seas. The biggest depths and the most dissected bottom relief are registered in the western part of the Barents Sea. Overdeepened, dissected with hollows and depression relief of the bottom of Barents and Pechora Seas define the possible paths of transfer and accumulation places of the pollutants. Coastal shallow waters with the depths above 50 meters occupy the most territories only in the south-east and north-west parts of the sea (Medvejinsko-Nadejdinskaya elevation). The layer containing information about geomorphologic specifics of the coast, rugged coastline and geomorphologic characteristics of linear morphostructures and morphologically similar regions, is no less important. The next thematic layer contains data concerning lithological composition of the ground of the Barents and Pechora Seas that actually differ in absorption characteristics. In specialists opinion the regions that are formed with thin dispersion material, such as the middle and border parts of the shelf, are favourable for the absorption of different pollutants. The coastal area that is usually covered with sand and other coarse ñ grained material is less influenced by the pollutants. During the research we produced and analysed a series of analytic maps.

National audience; La volonté de la Direction Archéologie & Muséum de la ville d’Aix-en-Provence (Damva) de restituer les paysages anciens de la commune afin de développer, enrichir et croiser les réflexions archéologiques et paléo-environnementales a notamment abouti à la création d’un nouvel outil de modélisation en trois dimensions du sous-sol du centre-ville. Ce projet, pensé dans une logique de partage des données, d’échange des savoirs et de mutualisation des moyens au sein de la collectivité, vise à répondre à des problématiques dépassant l’approche archéologique ou paléogéographique.