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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.

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).

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.

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; Anjouan (Comoros), humid tropical island of Comoros archipelago located at the northern entrance of the Mozambique Channel between Africa and Madagascar, is experiencing a disturbance of the water regime of rivers. The origins of the phenomenon are far from being identified in a systemic way to cope with the consequences already effective in the reduction of water resources in coastal areas and environmental risks associated with excess water. Thus, a methodological approach multi-source, multi-date and multi-scale linking field data (surveys and investigations) remote sensing satellite (SPOT image) and air, to Geographic Information Systems (MNT, 3D,…) and climate and demographic statistics have been adopted to reply this problem. The combination of these data allowed us to characterize the watershed, to specialize and to diagnose the dynamics of environmental change on the hydrological regime of degradation and propose a numerical model of development that integrates the socio-economic and ecological dimension of watershed; Anjouan (Comores), île tropicale humide de l’archipel des Comores situé à l’entrée Nord du Canal de Mozambique entre l’Afrique et Madagascar, connaît une perturbation du régime hydrologique de ses rivières. Les origines du phénomène sont loin d’être identifiées d’une façon systémique pour pouvoir faire face aux conséquences déjà effectives de la diminution des ressources en eau sur les zones côtières et aux risques environnementaux liés aux excès hydriques. Une approche méthodologique multi-source, multi-date et multi-échelle a été adoptée pour répondre à cette problématique, associant des données de terrain (prospections et enquêtes) à la télédétection satellitaire (image Spot) et aérienne, aux Systèmes d’Information Géographique (MNT, 3D,…) et aux statistiques climatiques ainsi que démographiques. Le croisement de ces données a permis de caractériser, spatialiser et diagnostiquer les dynamiques d’évolution environnementale d’un bassin-versant représentatif d’Anjouan (Bassin versant d’Ouzini-Ajaho), notamment la dégradation du régime hydrologique, et de proposer un modèle numérique d’aménagement qui intègre les dimensions socio-économiques et écologiques du bassin-versant

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.