Michal Klaučo

Bohuslava Gregorová

Uglješa Stankov

Vladimir Marković

Polina Lemenkova

Matej

Interpretation of landscape values, typology and quality using methods of spatial metrics for ecological planning

Conclusion

The main result of this work consists in determined ecological significant areas of habitats that are under protection´s system of Natura 2000 Sites. The patches quantification of habitats is the partial result that influences process of determination of ecological significance. The interpretative process examines land cover patches by the set of landscape metrics for the area, size, density and shape (NP, PD, MPS, PSSD and MSI). The output values could express a spatial processes in the landscape, such as perforation, dissection, fragmentation, shrinkage or attrition. The final ecological significance of the study area -Sitno Natura 2000 site -is at degree 3, what means that the area is represented by moderately significant land cover patches -habitats. It indicates the same value as the one at the initial level. According to the value of the ecological significance, the study area has been diversified into three zones, where each one indicates specific level of conservation. The zones and the final degree of the ecological significance of habitats are retroactively compared to historical and cultural human development that started in this area as early as in 1st century BC. Theoretically, such a long period of intense human impacts on the local environment should completely destroy natural environment. Nevertheless, this area demonstrates rather good natural ecosystems conditions and well functioning ecological processes within the habitats. The human impact is now observed only in small range of size not more than 1,50% from total area of Sitno Natura 2000 Site. It can be explained, first, by low population density within the study area comparing to other EU areas, secondly, by accurate usage of the living area by the local population in general, and thirdly, by high resilience of the elements of landscapes towards any human impacts.

Introduction: research problem

This research focuses on landscapes studies with selected research area located in central Slovakia, Sitno area. The research purpose is to perform spatial analysis of the landscapes and patches within the landscapes, in order to assess, which parts of the selected study area have the highest environmental quality and value, for further environmental monitoring and management. This paper presents quantitative ecological assessment of the landscapes within the natural protected area located in Slovakia, central Europe. It contributes to the sustainable development and environmental management of the environmentally protected zones.

Methodology

In this research we performed spatial analysis of Sitno landscapes using methods of landscapes metrics calculation and quantification. This include measurements of landscape elements, assessment of ecological significance, zoning of area into areas of ecological significance with different value levels. Methodologically, the landscape metrics assessment included calculations of Number of Patches (NP), Patch's density (PD), Mean patch size (MPS), Patch's size standard deviation (PSSD) and Mean shape index (MSI).

Techniques

The output of this interpretation is defined by the stepwise levels of ecological values: 1)very significant 2) significant 3) moderately significant 4) almost insignificant 5) insignificant. The final results are presented as calculated numerical values which are assigned for each land cover patch within the landscape. The interpretation of these quantified data allows to define ecological value and quality of the landscapes. The ecological value, or significance, of the landscape elements is represented by the numerical values which indicate quality of the complex ecological processes within the landscape. This enables to assess it as high-level value (very precious) or low-level value (not significant). These degrees are used for zoning of the protected areas. This process quantitatively divides study area into different zones with approach to landscape conservation. The final results consist in assessed ecological values for landscapes in Sitno Natura 2000 Site.

Results

The main result of this work is determined ecological significance of Sitno Natura 2000 Site. The ecological significance of landscape elements represents numerical values (degrees), which interpret quality of the ongoing ecological process in landscape. The zoning of the protected area is done according to the degrees of the ecological significance. This process objectively divides study area into different zones with separately regime for nature conservation and landscape usage. It is characterized as a process based on the objective and ecological approach that respects human sustainable development.

Diversifying study area on zones

Natura 2000 sites are designed according to the ecological and biogeographical criteria, in order to meet specific conservation objectives that are to be achieved by the appropriate conservation measures. They also provide a wide range of provisioning, regulating and sociocultural ecosystem services (Kettunen et al., 2009). Sitno Natura 2000 Site represents type of the protected area, where human impacts are detected, though not in a wide range. The final ecological significance of the study area is in the same range as at the initial level.

According to the mentioned above situation, we may conclude that human impact is not destructing natural processes, naturalness and natural functioning (self-regulatory processes) within the habitats. The spatial organization of the study area has been described according to the final ecological significance and partial ecological interpretation of the landscape metrics (Figure 2). Every part of the study area represents zone with different regime of conservation. Three different types of zones are described and defined as parts of the holistic nature of the protected area, diverse in natural values, originality, ecosystem extent, human actions and human land use types. First, "zone A" represents the "silent" zone, i.e. the most stringent level of protection, "zone B" is an area under limited using, and finally, "zone C" is an area suitable for human sustainable development.

Ecological significance of study area

Ecological significance of landscape elements is based on the described above methodology.

The Table 3 illustrates starting value (SA) of each one habitat. It is a baseline value of the ecological significance, where degrees are assigned according to Hrnčiarová et al. (1997). The study area has ecological significance at degree 3. Modification (SB) of this starting was realized according to: -Value outputs of the landscape metrics for each habitat and their percentage proportion to the metric´s total number. -Scale degree which has stabile or moving values by increasing method. The final degree of the ecological significance is assessed by the mathematical average using partial ecological significance. The study area has final ecological significance (SC) at degree 3, but this value is needed to be diversified to the main class categories of habitats which are represented in the study area. "Artificial surfaces" occupy only 1,49 % of the total area and have final ecological significance at degree 5. "Agricultural areas" occupy 5,69 % and have value of SC at degree 2. Finally, the last class is represented by "Forests and semi-natural areas", which occupy 92,81 % and have degree 2 as well. The mentioned above values represent background materials for the diversification of the study area on zones with special regime of nature and landscape conservation within each zone accordingly.

This is done using information on area, size, density and shapes of the landscapes. Patches, separate parts of the landscapes, were classified using code classification, which allowed to divide landscapes into three ground categories: 1) Artificial surfaces 2) Agricultural areas 3). Forests and seminatural areas. These categories were further sub-divided into 19 sub-clusters, which were defined according to the reference scale and methodological concept of habitat classification.

During the interpretation, the levels of ecological significance (SA) were assigned to every habitat. This process is based on the analysis and assessment of ecological processes in the landscape. The next step modifies the assigned degrees by the percentage proportion (P%) of habitat´s metrics (NP, PD, MPS, PSSD and MSI) to the total number of the metric´s value. Percentage proportion modified starting value of the ecological significance (SA). The result value of the modification factor is partial ecological significance (SB). The final ecological significance for the habitats (value SC) is presented as match average of values SB. 54th International Scientific Conference "Environmental and Climate Technologies", October 14-16, 2013, Riga. Latvia.

Outputs values of MPS (

The

  Patch density (PD) shows distribution and concentration of patches in the landscape, with values 11,22 patches per 1000 ha. It is very small value, which indicates large and compact patches. The study area is not fragmented too much, though, notably, as shown in Table 2: A) Number of categories and subcategories, B) Number of patches, C) Patch density and D) Class area (CA) and TA. The Mean patch size (MPS) was determined from the Number of patches and their area.Riga Technical University Institute of EnergySystems and Environment. Faculty of Power and Electrical Engineering Kronvalda bulv.1 Riga, Latvia.

Table 2 Quantification of habitats by landscape metrics

 2 The study area of Sitno Natura 2000 Sites is located in Slovakia, central Europe. This area (Fig.1) belongs to the pan-European Natura 2000 network of ecologically protected areas. Natura 2000 sites are defined as pan-European reserved nature zones, which meet the requirements of high environmental criteria and belong to natural heritage of Europe. These landscapes are under strict environmental protection due to their high ecological value, unique species, rich biodiversity and other characteristics. Selected under Natura 2000 programme, habitats and endangered species of high ecological value and significance are considered as primary targets of nature conservation. Sitno Natura 2000 Sites include unique landscapes with rare and endangered species. Therefore, they have high environmental and natural value, as important part of the Slovakian and European natural heritage. At the same time, there are detected anthropogenic impacts on Sitno Natura 2000 Sites, caused by the uncontrolled tourism and other factors.

	CORINE Land	Annex
	Cover

Total area (TA) / hectares (ha) 935, 56 100 %

Table 1 Habitats representation and the area of Sitno Nature 2000 Site

 1

Table 3 Determination of ecological significance of study area

 3

		Annex			NP			PD		MPS	PSSD		MSI
	LCC Code	Code	Sa	P(%) Sb P(%)		Sb	P(%) Sb	P(%) Sb	P(%) Sb	Sc
	Artificial	X	5		5			5			5			5			5		5
	surfaces
	1.1.2	X(1.1.2)	5	3,81		5	3,81		5	0,53		5	0,27		5	3,37		5	5
	1.2.2	X(1.2.2)	5	6,67		5	6,67		5	1,96		5	1,70		5	22,21		5	5
	Agricultural area	x 6510	2 2	14,2	2	2	14,2	2	2	2,33	2	2	1,79	2	2	4,27	2	2	2 2
		X(2.3.1)	3	0,95		3	0,95		3	0,39		3	0,00		3	5,40		3	3
		6410	2	4,76		2	4,76		2	0,58		2	0,42		2	3,51		2	2
	2.3.1	6110*	1	2,86		1	2,86		1	0,58		1	1,29		1	3,68		1	1
		6210	2	3,81		2	3,81		2	0,78		2	0,63		2	3,55		2	2
		6240*	1	3,81		1	3,81		1	3,94		1	1,74		1	4,93		1	1
		6230	1	0.95		1	0,95		1	1,67		1	0,00		1	4,11		1	1
	Forests & semi-	X	2		2			2			2			2			2		2
	natural areas
		X(3.1.1)	2	1,90		2	1,91		2	1,38		2	0,86		2	4,44		2	2
		91G0*	1	17,1		1	17,15		1	14,58		1	29,28		2	4,72		1	2
	3.1.1	91H0*	1	4,76		1	4,76		1	2,42		1	2,53		1	3,33		1	1
		91M0	1	3,81		1	3,81		1	6,40		1	3,07		1	4,06		1	1
		9180*	1	6,67		1	6,67		1	15,00		1	20,01		2	4,47		1	2
	3.1.3	9130	2	13,3		2	13,3		2	39,92		3	35,06		4	4,77		2	4
	3.2.4	40A0*	1	3,81		1	3,81		1	1,61		1	0,49		1	3,96		1	1
		X(3.2.4)	3	4,76		3	4,76		3	1,55		3	1,86		3	4,81		3	3
	3.3.2	8150	2	0,95		2	0,95		2	0,33		2	0,00		2	4,03		2	2
		8220	2	0,95		2	0,95		2	4,03		2	0,00		2	6,32		2	2
	Total landscape value	3		3			3			3			3			3		3

7. Acknowledgment

The financial support of this research has been partly provided by the SAIA Scholarship Committee of the Slovakian Academic Information Agency and Slovak Research and Development Agency within bilateral grant schemes. The data used in the current work were distributed by the Matej Bel University, Department of Natural Sciences

  Table2) focus on fact that Artificial surfaces have very small mean size (2,21 ha) within mutual relationship with NP and CA / TA occupy total landscape in partial form. On the other side, the category of Forest and semi-natural areas occupies wide range of the landscape and is diversified into big sum of NP and MPS. The most significant patch value is located in habitat of Tilio-Acerion forest of slopes, screes and ravines (9180*), where MPS is 13,21 ha and CA is 92,49 ha. Comparable patch is Pannonic woods with Quercus petraea & Carpinus betululus (91G0*) but CA represent almost 3 time more value. Each one patch of habitat 9180* indices big size, and it looks like compact biotope, which is not common in Slovakian environment. This habitat is endangered due to its small scale and fragmented appearance. Another remarkable habitat is Pannonic woods & Quercus. petraea & Carpinus betululus (91G0*). Its values of MPS, CA and NP indicate small size for some patches within the habitat. Patch size standard deviation (PSSD) is focused on the significance of the size difference among the patches within the landscape (Table2).The value of PSSD that is closer to zero means the same size of all patches which indicates uniform structure of patches, created by human impact or human planning of landscape. The biggest deviation sizes have the following habitats: Asperulo-Fagetum beech forests (9130), Pannonic woods with Quercus. petraea & Carpinus betululus (91G0*) and Tilio-Acerion forest of slopes, screes and ravines (9180*). According to the above interpretation of habitat 9180*, we can conclude that natural processes are dominating within these habitat patches. This value is needed to be interpreted with other metrics. Visual example is habitat 91G0* with value combination of MPS, CA, NP and PSSD, which illustrates small size of patches and, at the same time, different scale size within them. It should be interpreted as human impact based on forestry which is common in Slovakia. This type of habitat is threatened by the mismanagement, dimensions of intensive using and expansions of acacia. For these reasons, they are highly endangered habitats. No type of human impact in form of tourism is detected within these habitats. The Mean size index (MSI) indicates shape of patches (Table2). The Value index increases with irregularity of the patch shape. Patches with low values have circular shape and imply small range of human impact. This metrics should be interpreted with the Number of patches or area.

According to NP and CA/TA, the most significant value range have artificial surfaces. Very high value has agricultural area in relation to size of patches. It indicates human impact within this area, on one side by agricultural and historical settlement genesis and on the other side, by tourism.

4.1 Landscape elements of the study area

Landscape of the study area is diversified into three base categories: 1. Artificial surfaces, where abstracted human impact; 2. Agricultural area, without any part of arable land; 3. Forests and semi-natural areas. Mentioned categories are divided into 19 sub-categories (Table

1

). The number of categories is defined in accordance with used referenced scale and with the methodological concept of habitat classification. The most representative landscape element within the study area is Asperulo-Fagetum beech forests (9130), as shown in Table

1

. It occupies 52,64 % of the total area (TA). The study area is represented (91,81 % of TA) mostly by Forests and semi-natural areas. The number of patches (NP) in the study area reaches 105.

Code

Description Code Description


Lemenkova Polina
email: polina.lemenkova@mailbox.tu-dresden.de

Elek István

CLUSTERING ALGORITHM IN ILWIS GIS FOR CLASSIFICATION OF LANDSAT TM SCENES (MECSEK HILLS REGION, HUNGARY)

STUDY AREA

The study area is located in Mecsek Hills region, south-western Hungary (45 • 00 N-47 • 00 N; 17 • 00 E-19 • 00 E) .

Environmental characteristics of the region:

-high land heterogeneity -mixed vegetation types -complex landscape structure -intense agricultural land use -high environmental value The region is used for intensive agricultural works. The land cover types in the current area are: The data were acquired from the Global Land Cover Facility (GLCF) web application.

METHODS

The research methodology is based on cluster classification algorithm available in ILWIS GIS. The work is organized in several research steps summarized in the research workflow:

The research area was classified into a set of land cover categories, labeled to following land units:

1) winter wheat 2) spring barley, 3) maize 4) sugar beet 5) maize for ensilage 6) oak and beech forests 7) potato 8) other crops 9) shrubland 10) water 11) not agricult. areas 12) grassland 13) other land cover types Field crops (e.g. maize, winter wheat) were detected on the images. The species with unclear nature of crop or not easily recognized were defined as 'other crops'. A Google Earth aerial imagery was used for visual control inspection. Once all clusters are grouped, the layout was created using representation palette defined in the domain 'Land Cover Types. The research results in 3 maps of land cover types for 1992, 1999 and 2006.

Clustering method can be applied for other agricultural areas, since it enables objective classification in regions with high land heterogeneity and complex landscape structure.

ACKNOWLEDGEMENT

RESULTS

  scenes (GeoTIFF), taken on 14.09.1992, 09.08.1999 and 19.07.2006 .

  We thankfully acknowledge financial support of the current research, provided by the Balassi Institute, HSB (Hungarian State Scholarship Border), Budapest, Hungary. Reference # MÖB/154-2/2011 (Scholarship type B)CLUSTERINGThe research method is based on the cluster classification of Landsat scenes in ILWIS GIS. • USAGE Clustering is algorithm of unsupervised multispectral cluster classification, performed in semiautomated regime in ILWIS GIS. Clustering is an iteration process which groups pixels into clusters. a) Clustering menu, b) Histogram of land cover classes, 1992. • PRINCIPLE Clustering is based on principle of spectral distinguishability of digital cells. The Digital Numbers (DNs) of pixels create unique spectral signatures for various objects. Clustering extracts info on pixels and analyzes similarity of their DNs. Pixels with similar value of DNs are assigned to thematic categories (clusters). The grouping is done according to pixels' similarity, which is larger within a group than among other groups. Patterns of various land cover types. Fragment of Landsat TM color composite, ILWIS GIS menu • ADVANTAGES Clustering is objective technique, useful in situations when fieldwork is not available. It enables to avoid misclassified pixels and ignore external factors (e.g. atmospheric conditions), which significantly facilitates spatial analysis of the images.


Polina Lemenkova

Catrin Promper

Thomas Glade

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

• The region of Ybbs valley (Fig. 2) is located in mountainous Alpine Foreland, Austria. It covers a total area of 131.3 km 2 .

The environmental conditions of the the Ybbs valley are especially prone to landslides:

• Geology: deposits of gravel, sand, marl, conglomerate, calcareous sandstone and clay.

• Most of the region is used as pasture, agricultural land, and large forests.

• Climatic humid to continental. Intensive rainfalls in spring and autumn months.

• The region is attractive for tourists due to its environmental and historical value.

Type of factors Thematic layers

DATA

METHODS

RESULTS

• Source: the data used in this research were generously provided by the Provincial Government of Lower Austria.

• Content: data include vector thematic layers covering the area, aerial imagery, topographic vector layers and maps.

• Organization: data have been organized in a ArcCatalog database and sorted according to their types (Tab.1).

• Compatibility: all data have been stored in the ArcGIS project in Austrian National grid Austria Bundesmeldenetz (BMN) M34.

CONCLUSION

Fig

  Fig. 3. Landslides classified by age. Ybbs valley (Source: Petschko et al., 2011) Fig. 4. Elements at risk: buildings, roads and railway lines. Enlarged: city centre (Data source: Provincial Government of Lower Austria)

  Number of houses at risk in Ybbs valley = 641 (**) Total area of buildings at risk, m 2 = 138 Source: Zischg et al., 2005 (**) Source: Giacometti, 2005. (***) Source: Sterlacchini et al., 2007 Tab. 3. Real estate destruction losses, Ybbs valley, as for average market value (Source: Giacometti, 2005). Tab. 2. Estimation of possible monetary losses for transport.

REFERENCES•

  Bell, R. & Glade, T. 2004. Quantitative risk analysis for landslides -Examples from Bildudalur, NW-Iceland. Natural Hazards and Earth System Sciences 4: 117-131 SRef-ID: 1684-9981/nhess/2004-4-117 • Bellos, P., Frank, G. & Hochbichler, E. 2006. Waldbauliche Bewirtschaftungsrichtlinien für das Einzugsgebiet der Ybbs im Rahmen eines nachhaltigen Naturraummanagements (Endbericht). Forschungsprojekt: GZ BMLFUW LE.3.2/0021-IV/2004 • Cruden, D.M. & Fell, R. (eds) 1997. Landslide Risk Assessment. Proceedings of the international workshop on landslide risk assessment / Hawaii / USA. • Giacometti, P. (ed.) 2005. Economic Evaluation of Risk. ALARM Project (Assessment on Landslide Risk and Mitigation in Mountain Area). Aracne. EU Commission. The Fifth Framework Programme. • Glade, T., Anderson, M. & Crozier, M.J. (eds.) 2005. Landslide Hazard and Risk. John Wiley & Sons, Ltd. • Hartlen, J. & Viberg, L. 1988. General report: Evaluation of Landslide Hazard. Proceedings 5th Int. Symposium on Landslides, Lausanne. Vol.2, 1037-1057. • Heinimann, H.R. 1999. Risikoanalyse bei gravitativen Naturgefahren -Methode. Umwelt-Materialen, 107/1, Bern. • Lee, E.M. & Jones, D.K.C. 2004. Landslide Risk Assessment. Thomas Telford. • Petschko H., Bell R., Brenning A. & Glade T. 2011. Modelling landslide susceptibility in large and heterogeneous regions with generalized additive models: case study Lower Austria. Contributed to the project MoNOE at EGU-2011, Vienna. • Sterlacchini, S., Frigerio S., Giacomelli P. & Brambilla M. 2007. Landslide risk analysis: a multidisciplinary methodological approach. Nat. Hazards 7, 657-675. • Zischg, A., Fuchs, S., Keiler M., & Stötter, J. 2005. Temporal variability of damage potential on roads as a conceptual contribution towards a short-term avalanche risk simulation. Natural Hazards, 5, 235-242.

INTRODUCTION

ACKNOWLEDGEMENTS

The GIS data were provided by the Provincial Government of the Lower Austria. We thank assistance of H.Petschko. The financial support was provided by the Austrian Academic Exchange Service of the Republic of Austria, Austrian Federal Minister for Science and Research (ÖAD).

RESEARCH AREA PERSPECTIVES AND RECOMMENDATIONS

In view of the uncertainties in risk assessment, flexible combination of research approaches is desirable: landslide hazard modeling and application of various methodologies to study area. Buffer zones have to be validated and empirically tested during fieldworks. Data from cadastral and statistical information sources could enable vulnerability assessment.

The geographic location of landslides within the Ybbs valley was defined by H.Petschko. We used these results (Fig. 3) for defining risk zones. 1) Risk zone: 100-meter buffer area surrounding all landslides downwards, as a landslide with volume of 1 km³ may reach 100 meters while moving.

2) Elements at risk: The objects located in the 100 meter distance downwards the landslides are regarded as elements at risk. 3) Estimation of the economic losses: The methodology is proposed by Giacometti (2005) was applied for Ybbs valley using real estate prices for common goods (immobility, roads). Calculations of the economic losses (in €) was done based on the average real estate prices for various aspects of renovation, reconstruction and rebuilding of the destroyed objects.

• Defining elements at risk The elements at risk were selected in 100-meter buffer area near the landslides (Fig. 4). The length and number of the elements at risk within the distance of 100 m near landslides totals to 20.9 km of railway lines, 13.5 km of roads and 641 buildings.

• Calculation the economic losses The calculation of the real estate losses is computed from the calculation of five different types of costs (Tab. 3): 1) demolition, 2) transport and disposal debris, 3) restoration of inner walls, 4) furniture restoration and 5) house construction. The average volume to demolish damaged buildings indicated the dimensions of the losses of ca. 13.65 M €.

The research focuses on the monetary estimation of the possible losses caused by landslides. Estimation of the economic damages is performed using existing simplified methodologies. Calculations were based on real estate and market price of the elements at risk. While assessing potential damage of landslides confusion arises due to these factors. 1. First, the temporal probability of the landslides occurrence is highly difficult to assess: it can only be estimated based on the reliable and obtainable data. This includes historical data continuously reporting the occurrence of the landslides. 2. Secondly, difficulties arise by estimation of the indirect losses and partially damaged objects. The amount of the damages can be assessed based on elements vulnerability, which is very uncertain to estimate exactly. Thus, the vulnerability may differ depending on object location, individual characteristics and external factors. 3. The term "landslide" is not differentiated between debris flows and shallow or rotational landslides. This is an important source for uncertainty, as movement characteristics of these landslides are different. 4. Confusing over different method approaches in the risk assessment may generate various results: difference in magnitude and occurrence of landslides, risk perception and vulnerability assessment. The estimation of landslide risk should be based on complex investigations. The data about landslide probability should be gained from monitoring programmes. The elements at risk are defined based on spatial analysis and infrastructure inventory. The vulnerability estimation should include census data and social questionnaire. The real-life situations may vary depending on the exact price of the individual object.

The estimated price for the debris removal is approx. 3.6 M €, in case buildings are totally affected and covered. The restoration of the inner walls is based on average prices for building restoration and approximate surface of the walls, which totally estimates 2.73 M €. Using approximate prices of the furniture costs (Giacometti, 2005) in a typical house the costs for furniture renovation is € 190,500. Cost for house construction is computed using real estate prices and average house area in m² which sums up to € 349,250. The losses for transport (Tab. 2) include costs to rebuild a road, a railway, and a restoration of the roads. A total of almost 140 M € would have to be invested for the renovation of the Ybbs valley in case of a landslides disaster (Tab.3).


Lemenkova Polina
email: polina.lemenkova@mailbox.tu-dresden.de

Bruce C Forbes

Kumpula Timo

Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia

This paper details changes in land cover types in tundra landscapes (Yamal) since 1988. The research method is supervised classification (Minimal Distance) of the Landsat TM scenes. The new approach of the current work is application of ILWIS GIS and RS tools for Bovanenkovo region.

Figure: 1. Yamal Peninsula

The research area is geographically located on the Bovanenkovo region, the north-western part of Yamal Peninsula, West Siberia, Russia (Fig. 1). The Yamal Peninsula is a flat homogeneous lowland region with low-lying plains of heights <90m. Such geographic settings create specific local environmental conditions in the region. Thus, Yamal is the worlds largest high-latitude wetland system covering in total 900,000 km [START_REF] Leibman | Kriogennyie opolzni Yamala Yugorskovo poluostrova (Cryogenic Landslides of the Yamal and Yugorsky Peninsula)[END_REF] of peatlands, complex system of wetlands, dense lake and river network. Typical for this region are seasonal flooding, active erosion processing, permafrost distribution and intensive local landslides. Dominating vegetation types are typical tundra species (heath, grasses, moss, and lichens), and woody plants (shrubs and willows). 2. Research area selection. The area of interest (AOI) was identified and cropped on the raw images (Fig. 3). This area shows Bovanenkovo region in a large scale. The AOI area best represents typical tundra landscapes. 3. Image classification method is supervised classification (Minimal Distance), which is based on the spatial analysis of spectral signatures of object variables, i.e. vegetation types. The classes sampling was performed using Sample Set tool in ILWIS GIS. The training pixels for each land cover type were selected as representative samples and stored as classification key. They have contrasting colors, visually visible and distinguishable on the image. The defined classes include shrub tundra, willows, tall willows, short shrub tundra, sparse short shrub tundra, dry grass heath, sedge grass tundra, dry short shrub tundra, dry short shrub sedge tundra, wet peatland, peatland (sphagnum). The pixels were associated with land cover classes, using their DNs, similar to the key samples. The assessment of the areas of all land cover classes shows following results. Willows covers 2750,57 ha in 2011, which is more than in 1988, when it covered 1547,52 ha (both 'tall willows' & 'willows' classes). Noticeable is increase in tundra vegetation: 'short shrub tundra', 'sparse short shrub tundra' and 'dry short shrub tundra' have more areas covered in 2011 comparing to 1988: almost 5442,00 ha vs 1823,00 ha. Analysis of the results shows noticeable overall increase of woody vegetation (willows and shrubs) and decrease of peatlands, grass and heath areas. This illustrates environmental process of greening in Arctic, i.e. the unnatural increase of woody plants. The gradual changes in plant species patterns and distribution affect landscape structure in Yamal ecosystems. The triggering factors for these processes could be complex environmental changes in Arctic, as well as local cryogenic processes (e.g. successive change in vegetation recovering after cryogenic landslides).

Research data

Figure: 2 .

 2 Figure: 2. Landsat TM images: 1988 (left) and 2011 (right) The research data are orthorectified Landsat TM scenes covering north-west of Yamal. The images have a time span of 23 years: 1988-08-07 and 2011-07-14, taken in growing season when vegetation coverage is clearly visible.

Figure: 3

 3 Figure: 3. Selecting AOI (study area)

4 .

 4 Thematic mapping: layout of main research results, represented as maps of the land cover classes. The created domain Land classes includes legend with representation colors visualizing each category.

Figure: 4 .

 4 Figure: 4. Land Cover Classes in Bovanenkovo area, Yamal Peninsula: 1988 (left) and 2011 (right)

Table : 1

 : . Statistics on the land cover classes, Bovanenkovo region, Yamal Pennsula. Spatial analysis of land cover types in northern landscapes can help to detect local environmental changes in Arctic regions. Current research details changes in the land cover types in Bovanenkovo region, Yamal Peninsula, during the past 2 decades. These results are received as a result of the spatial analysis of classified images. The GIS mapping is based on the results of the image classification. The research results presented in the current work illustrate spatial distribution of land cover types in the selected area.

	Land Cover Class	1988, # pixels 2011, # pixels 1988, ha 2011, ha
	Shrub tundra	220447	168226	1146.3244 874.7752
	Short shrub tundra	165079	270158	858.4108 1404.8216
	Willows	193645	457004	1006.954 2376.4208
	Tall willows	103954	71952	540.5608 374.1504
	Sparse short shrub tundra	176511	759380	917.8572 3948.776
	Dry grass heath	641420	231719	3335.384 1204.9388
	Sedge grass tundra	27545	57052	143.234 296.6704
	Dry short shrub tundra	8984	16993	46.7168 88.3636
	Wet peatland	761231	531809	3958.4012 2765.4068
	Peatland (sphagnum)	120328	93979	625.7056 488.6908
	Dry short shrub-sedge tundra	173693	92242	903.2036 479.6584
	Increase of wooden vegetation class goes along with shrunk of grass and heath areas:
	'dry grass heath' occupied area of 3335.39 ha in 1988, while currently it covers
	1204.94 ha. Slight decrease can be noticed in the 'peatlands' and 'wet peatlands'
	classes: 3958.40 ha against 2765.41 ha in 2011 by 'wet peatlands', and 625.71 ha in
	1988 versus 488.69 ha by 'peatland (sphagnum)' class.
	Conclusion
	The GIS-based mapping of the northern ecosystems is important tool for the
	landscape monitoring and management. Processing of remote sensing data (e.g.
	Landsat TM scenes) by means of GIS (e.g. ILWIS) improves technical aspects of the
	landscape studies, since it enables assessment of spatio-temporal changes in
	vegetation coverage.

Funding

The financial support of this research has been provided by the Fellowship of the Center for International Mobility (CIMO) of Finland. Contract No. TM-10-7124 (Decision 9.11.2010).

Results

The research output includes following results: 1) two thematic maps of land cover types in Bovanenkovo area, Yamal (Fig. 4) 2) calculation of the areas in ha of land cover types (Tab.1).


Polina Lemenkova

Catrin Promper

Thomas Glade

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Landslide risk assessment aims to provide an objective and justifiable measure of risk using selected criteria. It includes hazard analysis, i.e. analysis of the probability and characteristics of the landslides, identification of the elements at risk, assessment of their vulnerability and final calculation of the respective risk (Glade et al. 2005, Cruden & Fell 1997). According to different risk studies (e.g. [START_REF] Bell | Quantitative risk analysis for landslides -Examples from Bildudalur, NW-Iceland[END_REF], Glade et al. 2005[START_REF] Lee | Model stud ies of erosion of natural barriers by a wave flow occurring as a result of landslides[END_REF]), the complete risk assessment includes risk analysis, risk evaluation and risk management. Herein the first implies estimation of the risk in a given environment, the second mostly concerns social aspects of risk perception and acceptance, and the latter is decision-oriented. Two approaches in landslides risk assessment are traditionally defined: a qualitative landslides risk assessment, which includes ranking method varying in detail and complexity (Hartlen & Vilberg 1998), and a quantitative risk assessment implying probabilistic stability analysis, object-oriented analysis as well as consequence analysis [START_REF] Morgenstern | Toward landslide risk assessment in practice[END_REF], Glade et al. 2005[START_REF] Heinimann | Risikoanalyse bei gravitativen Naturgefahren -Methode[END_REF]). The final model choice is determined by the required accuracy, the data availability, scale of research and the specific character of the research area. This study focuses on the regional quantitative assessment of the monetary losses caused by landslides.

RESEARCH AREA

Geographic location

The research is carried out in the Ybbs valley (Fig. 1) in the Alpine Foreland, Austria. The study region covers a total area of 131.3 km 2 . The land cover at the Ybbs river valley is characterized by the dominated agricultural land, pasture and large forests. The region is of much attractiveness for the tourists due to its environmental and historical value. Therefore, the area is covered by the dense network of transport lines, power supply lines and water supply. University of Vienna, Department of Earth Sciences, Geography and Astronomy, Institute of Geography and Regional Research, Vienna, Austria ABSTRACT: Landslides are natural hazards typical for Alpine regions in Austria. They often lead to negative consequences for the society and environment and cause frequently high economic losses. Estimation of economic risks is a sound basis for landslide risk mitigation and prevention. This research aims to assess risk and estimate economic damage caused by landslides. The study area is located in the Ybbs valley, Lower Austria, a touristic region and a critical transport corridor. Methodology consists of GIS analysis and estimation of the monetary losses. Spatial analysis includes defining elements at risk located in the buffer zone of 100 m near landslides. The results include estimated losses caused by the destruction of immobility and transport, including costs for buildings demolition, restoration, roads rebuilding, debris transport, excavation and removal.

Environmental settings

Situated in mountainous Alpine Foreland, the research area is developed in the conditions of humid to continental climate. This is characterized by moderate temperatures in summer areas, usually reaching +17°C. The most humid months are in summer with the maximal amount of precipitation totaling to 144 mm (July), with an average of 84 mm. In spring and autumn the rainfall is also intensive with maximal values up to 99 mm (May) and 93 mm (September).

In winter months snowfall level reaches a maximum of 84 mm in December (Zentralanstalt für Meteorologie und Geodynamik 2011). Therefore, the meteorological settings create favorable conditions for landslides.

A geological overview of the research area shows that the southern part of Alpine Foreland is composed by the Triassic carbonate rocks. Flysch sandstone can be found in its northern part, and marl of the Helvetic cover and loess layer of the Alpine Foreland [START_REF] Bellos | Waldbauliche Bewirtschaftungsrichtlinien für das Einzugsgebiet der Ybbs im Rahmen eines nachhaltigen Naturraummanagements (Endbericht)[END_REF]. Gravel, sand, marl, conglomerate, calcareous sandstone and clay are deposited in various sedimentation layers. The Ybbstal Alps are located in the Molasse and Neogene basin (Fig. 2). Within the sediments there are conglomerates, gravels and sands as well as sandy marl and clayey marls [START_REF] Thenius | Niederösterreich[END_REF]. The soils in this region are not rich of minerals, with the exception of loess soils; therefore, most of the region is used as pasture. Such loose or weak soil types are especially prone to landslides triggered by intense rainfall [START_REF] Wieczorek | Landslide Triggering Mechanisms[END_REF].

Vegetation is characterized by the presence of sub-alpine species as the area is located in the central European floral region. Rapid snowmelt and intense rainfalls, typical for the climate in Alpine Foreland, intensify erosion processes and largely contribute to the landslide movements.

Socioeconomic settings

Socioeconomic characteristics of the region are dominated by rural and semi-rural areas with population mostly living in one-family houses distributed in the valley. The region is also being widely visited by tourists in winter for skiing. The area of Waidhofen a.d. Ybbs is equipped with a transport network, well-developed for a provincial scale. The transport system in the area consists of railway and roads, widely used by public transport means (buses) and private cars, and encompasses the city Waidhofen a.d. Ybbs and the surrounding area within the Ybbs valley. All farms on the slopes have welldeveloped road access. The accessibility, connectivity and efficiency of the transport network are an essential condition for the development of the urban area situated in the mountainous region.

Therefore, socioeconomic losses from the landslides can be very extensive in this area, distinct by high level of human development, and include much damage to immobility, transportation systems and utilities.

Landslides

Landslides are common geomorphological events occurring often in the mountainous areas with specific geological conditions. The main factors determining landslide occurrence are topographic conditions (e.g. slope angle, curvature, aspect, etc.), lithology, vegetation cover and covering material. Triggers include external meteorological (e.g. precipitation) and/or or tectonic factors (e.g. earthquakes), but also internal factors (e.g. decrease of strength by soil weathering).

The diversity of landslide occurrence is determined by various mechanisms and environmental local conditions, such as topography, lithology, geological structure, hydrogeology, climate and vegetation [START_REF] Lee | Model stud ies of erosion of natural barriers by a wave flow occurring as a result of landslides[END_REF]. [START_REF] Cruden | Landslides Types and Processes[END_REF] proposed the following classification of landslide types: Rotational landslides, translational landslides, block slides, rockfalls, topples, debris flows, debris avalanches, earthflow, creeps and lateral spreads. All of those of various ages and different grade of activity can be found in the study region, however, the main landslide types include rock falls and shallow and deep seated translational and rotational slides. In the study region, various ages of these landslide types can be observed (Fig. 3). 3 METHODOLOGY OVERVIEW There are a wide range of various factors that need to be involved in landslide risk assessment. The assessment of landslides risk is traditionally estimated using the function R = f (H, A, V), where R = risk; H = hazard as landslide probability of a given magnitude, i.e. temporal and spatial likelihood of their occurrence; A = elements at risk; V = vulnerability of the elements at risk towards a given event magnitude. Due to the lack of data, the vulnerability in this research of the identified elements at risk is approximated as "one" in case of landslide occurrence, and "zero" by no hazard. The probability of the landslides is suggested as one, because it is assumed that the landslides occur with certainty. Consequently the risk is based on the assumption that if an element at risk is affected, it is totally destroyed. This assumption had to be taken due to the missing vulnerability curves for the various landslide processes and magnitudes. Thus, this study mostly focuses on the estimation of the exposed elements at risk, their value and potential losses that landslide occurrence might cause.

The analysis of the potential monetary losses is commonly applied within the economic part of the risk assessment. There are two approaches defined in economic losses (Giacometti 2005): direct and indirect damages caused by the hazard. The direct damages include costs of recovery and reconstruction of the elements destroyed during the event. In contrast, indirect damages may involve various consequences that happened because of the interruption of economic activities and flows, e.g. as a result of damaged infrastructure such as energy, communications, tourism, transport network, jobs, etc. (Giacometti 2005). The spatial extent of indirect damages may go far beyond the place of landslide occurrence.

Nevertheless, this study is not addressing indirect monetary losses due to missing data, but approximates the direct costs. It calculates possible losses in real estate and transport routes.

DATA MANAGEMENT

The data for analyzing landslide risk and estimating economic losses have been organized in a digital database. The data include orthophoto imagery covering the area, thematic vector layers and maps provided by the Provincial Government of Lower Austria and modified by H. Petschko from the University of Vienna (i.e. geological maps of Low Austria, landslides location and respective classification of the Waidhofen region) as well as the analysis of spatial pattern of landslides.

_____________________________________________

The data includes different types such as DEMs, vector layers of buildings, roads, railway lines, topographic maps, orthophoto maps, geological and geo- morphological maps, basic geographic information, such as rivers, cadastral objects, polygon layers showing location of nature protected areas, infrastructure layers such as postal codes, provincial regions, areas of construction works, etc.. All data have been stored in the ArcGIS project using common geographic reference system of Transverse Mercator projection on Bessel 1841 ellipsoid, Austrian National grid Austria Bundesmeldenetz (BMN) M34.

METHODS

Elements at risk

In order to estimate the potential effect of future landsliding, a risk buffer zone has been defined. This zone is established as 100-meter buffer area surrounding all landslides downwards, because a landslide with volume of 1 km³ may reach 100 meters in its movement [START_REF] Lee | Model stud ies of erosion of natural barriers by a wave flow occurring as a result of landslides[END_REF]). This buffer zone has been chosen based on the assumption that new landslide will affect the area surrounding the existing landslide body. Therefore, in this work the objects located in the 100 meter distance near the landslides are regarded as exposed elements at risk.

Using Near function of the Proximity Analysis from the Analysis Tools of ArcGIS the 100 m distance from all landslides was calculated. Afterwards, the elements at risk located in the buffer zone were selected by location using the SQL Query Builder tool in Arc Toolbox. Namely, from the respective vector layer containing the featured objects (e.g. "buildings") only those were selected which are located within the buffer zones. The elements at risk included buildings, roads and railway lines. The communication, power and water supply lines, suggested initially, were not included to the list of analyzed objects, due to the lack of precise data.

The objects located in the 100-m zone were then added to the attribute table of the corresponding layer. Elements located far apart from the target (landslides) were given attribute "-1" while all others received positive values. Thus, the selection of the elements at risk from the total amount of the objects of the same class has been made using Selection by Attribute function in ArcMap.

Estimation of the economic losses

Applying the methodology proposed by Giacometti (2005) the estimations of the total losses caused by the landslide hazards in the Ybbs valley were calculated. Based on the real estate prices for the various aspects of houses renovation and rebuilding the economic losses have been assessed. Calculations have been done based on the average price for reconstruction and renovation of destroyed or partially damaged immobility and transport routes.

The potential damage in the affected area has been analyzed using quantitative economic assessment, which includes estimation of the area possibly damaged following a landslide failure (m²) as well as the monetary consequences expressed as damage (€), thus finally expressed as monetary risk in €/m².

The monetary losses were calculated using average real estate prices, costs of renovation, construction and restoration, as well as amount of the elements of risk or total area affected. The estimation of the economic losses caused by real estate and transport network damage in the Ybbs valley is based on Giacometti (2005). In the calculations, the average prices of the buildings construction and market prices for the transport network reconstruction, separately for roads and railways have been used.

Defining elements at risk

According to the accepted area of the landslide risk zone including the downward 100-meter buffer area surrounding all landslides, the elements at risk have been selected using spatial analysis. As a result of the proximity analysis, the length and number of the elements at risk (Fig. 4) located within the distance of 100 meters from the landslides totals to 20.9 km of railway lines, 13.5 km of roads and the 641 buildings located near the landslides.

Calculation the economic losses

The calculation of the real estate losses is computed from the calculation of five different types of costs: demolition, transport and disposal debris, restoration of inner walls, furniture restoration and house construction (Tab. 2). First, the demolition costs for the destroyed houses were estimated (Tab. 2) for the concrete house using demolition costs and costs of mechanical excavation. The average volume to demolish damaged buildings indicated the dimensions of the losses of approx. 13.65 M €. The landslide types often include intensive debris or mud flows. Debris flows in Ybbs area may lead to severe damage to exposed buildings and infrastructure. It may also cause injuries and human deaths. The exact volume of the masses flow may vary depending on the preparatory and triggering factors [START_REF] Crozier | Landslides: Causes, consequences and environment[END_REF]. However, in this study the calculation of the transport and disposal debris is based on the average costs to transport and disposal debris in a dump. Thus, for the Ybbs valley the estimated price for the debris removal is approx. 3.6 M €, in case buildings at risk in the Ybbs valley are totally affected and covered.

The restoration of the inner walls within the buildings has been assessed using prices for two main components of the building restoration: average costs and approximate total surface of the inner walls. Thus, total estimate of the losses for the inner walls restoration is 2.73 M €. Furniture can often be damaged during the buildings destruction. It is rather difficult to estimate the furniture costs exactly, as it varies strongly in different households. However, using approximate prices of the furniture costs as estimated by Giacometti (2005) in a typical house as well as the total area of the house, the costs for furniture renovation is € 190,500.

Finally, a cost for construction of completely new house is computed according to the known real estate prices and an average house area in m 2 (Giacometti 2005), which sums up to € 349,250. Based on the given assumptions it is approximated, that a total of almost 140 M € would have to be invested for the complete renovation of the Ybbs valley in case of a landslide disaster. The economic losses of the transport routes (Tab. 3) are calculated also based on Giacometti (2005), and include costs to rebuild a road, a railway, and a restoration of the roads. The total roads restoration includes debris removal, excavation, transportation and storage of material (Tab. 3). Total length of the roads affected by the landslides is multiplied by the restoration and renovation costs. The final costs for the direct damages of the roads are € 144.88 M € for the whole transport routes (roads and railways), assuming that all roads and railways are totally affected by the landslides.

CONCLUSIONS

The current research focuses on the monetary estimation of the possible losses caused by landslides. Es-timation of the economic damages is performed using existing simplified methodologies. Calculations were based on real estate and market price of the elements at risk. While assessing the potential damage during the hazardous events confusion often arises because of three factors. First, the temporal probability of the landslides occurrence is highly difficult to assess: it can only be estimated based on the reliable and obtainable data. This includes historical data continuously reporting the occurrence of the landslides. Secondly, difficulties arise by estimation of the indirect losses and partially damaged objects. The amount of the damages can be assessed based on elements vulnerability, which is very uncertain to estimate exactly. Thus, the vulnerability may differ depending on object location, individual characteristics and external factors. Third, we do not differentiate in the term "landslide" between debris flows and shallow or rotational landslides. This is an important source for uncertainty, because the movement characteristics of these landslides are different. Moreover, confusing over different methodological approaches in the landslide risk assessment may generate various results based on estimation of landslide hazards. This might differ in magnitude and occurrence of landslides, risk perception and assessing elements vulnerability. Therefore, overall estimation of the landslide risk should be based on complex investigations. The information about landslide probability should be gained from landslide monitoring programmes which are currently not in place. The elements at risk are defined based on spatial analysis and infrastructure inventory. The vulnerability estimation should include census data and social questionnaire. Finally, monetary losses are estimated using market prices and spatial GIS analysis.

PERSPECTIVES AND RECOMMENDATIONS

In view of the difficulties and uncertainties in the determination of landslide risk, the main recommendation is flexible combination of research approaches, landslide hazard modeling, and application of various methodologies to study area. Buffer zones have to be validated by fieldworks. Data from cadastral and statistical information sources could enable carrying out vulnerability assessment. The investigations of supporting guidelines would be of great assistance in research on landslide risk assessment. The obtained results can be empirically tested.

Figure 1 .

 1 Figure 1. A shaded relief of the Ybbs valley in Lower Austria Scale: 1:410,000 (Source: Provincial Government of Lower Austria)

Figure 2 .

 2 Figure 2. Geology of the Waidhofen a.d.Ybbs. Scale 1:6,740,000 (Source: Geologische Bundesanstalt (GBA), 1964, modified).

Figure 3 .

 3 Figure 3. Landslides classified by age. Ybbs valley (Source: Petschko et al., 2011)

Figure 4 .

 4 Figure 4. Elements at risk: Infrastructure of the Waidhofen a.d.Ybbs -Buildings, roads and railway lines. Enlarged: city centre (Data source: Provincial Government of Lower Austria).

  debris, €/m² (*

Table 1 .

 1 Data structure within this project.

	______________________________________________
	Type of factors ______________________________________________ Thematic layers
	Anthropogenic	Roads (railway, highways)
		Areas of demolishing & dismantling
		Areas of construction work
		Settlements
		Natural Parks
	Topographic	Orthophotos
		DEM (1m and 3m resolution), DTM
		Vector relief map
	Environmental	Watershed areas
		Water catchment areas
		Landscape protected zones
		Areas of NATURA-2000
		Geological vector map
		Natural protected zones
		Tectonic lines
		Forest areas

Table 2 .

 2 Real estate destruction losses, Ybbs valley, as for average market value (Source: Giacometti, 2005; 138-140).

Table 3 .

 3 Estimation of possible monetary losses for transport network caused by landslides.

	______________________________________________
	Type of	Construction	Length	Total
	Investment ______________________________________________ cost (€) (m) losses (€)
	Railway tracks, €/m	4,200	20,953 A = 88,005,204
	restoration (*)
	Road rebuilding, €/m (**)	4,131	13,541 B = 55,948,749
	Debris excavation (a)	17	13,541
	Debris removal (b)	25	13,541
	Transportation	(c)	26	13,541
	& storage
	Roads restoration, €/m 2 (***) 68.0	13,541 C = 927,590
	a+b+c
	Total possible direct damages (A+B+C) _____________________________________________ 144,881,544
	(*) Source: Zischg et al., 2005
	(**) Source: Giacometti, 2005
	(***) Source: Sterlacchini et al., 2007

ACKNOWLEDGEMENTS

We thank the Provincial Government of the Lower Austria for provided GIS data. We thank assistance of the researchers from the University of Vienna, R.-Bell and H.Petschko. The financial support was provided by the Austrian Academic Exchange Ser-vice of the Republic of Austria, Austrian Federal Minister for Science and Research (ÖAD) Contract No.Zl.:ICM-2010-02122.


Bogdan Logofatu

Michael Auer

Joint Bachelor Degree in Information Technology

Keywords:

This paper will present the BIT2010 project, a project that will be developed and implemented during the period 2006-2009, under the Socrates-Erasmus Curriculum Development scheme, financed by the European Commission. Based on the influence of the ICT domain on the most important world's economies, the emergency of new higher education programs in ICT is presented. The needs for a new ICT graduates' profile and a new ICT Curriculum were taken into consideration. Clear achievements in the areas of using computers and advanced digital technologies in higher education (virtual campus, e-Learning, virtual and remote laboratory) are reported. Some of these products and services are designed and produced by the staff of the involved universities

1 The rationale of the project

Heading EU i2010 Initiative

On June 2005, the European Commission has adopted the "i2010 initiative-European Information Society 2010" to foster growth and jobs in the information society and media industries in European Union. EU Member States are asked to define National Information Society Priorities in their National Reform Programmes to contribute to the objectives of i2010 [1]. The first pillar of i2010 combines all the regulatory instruments at the Commission's disposal which will allow us to create a modern, market-oriented regulatory framework for the digital economy. The second pillar brings the EU's research and development instruments into the game of digital convergence and sets priorities for our cooperation with the private sector to promote innovation and technological leadership. The third pillar seeks to promote, with the tools available to the Commission, an inclusive European Information Society, supported by efficient and user-friendly ICT enabled public services.

Skills Shortage

The Networking Skills Shortage -an IDC White Paper [START_REF] Kolding | Networking Skills in Europe: Will an Increasing Shortage Hamper Competitiveness in the Global Market?[END_REF] published in September 2005estimates that demand for networking skills in European organizations in 2005 can reach almost 963,000 full time equivalents (FTEs). This number represents the total amount of work that needs to be done in the networking space, and the number of people needed to do this work -assuming they all spend 100% of their available time on networking. However, in terms of actual number of people with networking skills, IDC estimates the demand to be around four million skilled people assuming that people on average spend 25% of their time working with networking technology as part of other responsibilities.

EU Report on Information Society, 2007

The recent EU Report on Information Society [3], provide up-to-date information that are correlated with IDC Report. The economic growth of EU is estimated to be around 2% in 2006. The contribution of ICT to the economy is well established based on empirical evidence from growth accounting models that link the production and use of ICT to productivity. The impact of ICT on productivity in the EU has consistently been only half of the impact in the US over the last ten years. From 1995 to 1999, ICT accounted for 0.9% of the annual productivity growth in the EU compared to 1.7% in the US. From 2000 to 2004 the relative figures were 0.5% and 0.9%. From 1995 to 2000, aggregate productivity in the EU grew by 1.8% per year. At least 55% of that increase was due to ICT. Between 2000-2004, productivity growth fell to 1.1% but the contribution of ICT remained high at around 45%". These figures underline the key role of ICT in realizing the Lisbon objectives of competitiveness and growth and show that the BIT2010 project is addressing a priority domain.

Erasmus BIT2010 Project

New Profile of the ICT graduates

The problem of the skills shortage is not only the number of graduate students; it is also related to their ICT profile targeted to the actual needs of the today companies. The feedback received from the companies includes negative comments on the skills and the mentalities of the students graduated by traditional higher education, both computer engineering and informatics programs. That is why we very much appreciate the point of view defined by the "Career-space" project [4]. This project argues the need for a new ICT curriculum that will produce a new ICT profile. The universities have "to meet the needs of the ICT industry to create and develop new curricula which contain elements from electrical engineering, elements from informatics, and a significant focus on the teaching, training and practicing of behavioural and business skills".

BIT2010 Consortium

The BIT2010 Socrates-Erasmus project was approved in 2006 and it is aiming to contribute to the Information Technology human resource development within the context of the Bologna Process and the "i2010 Initiative". The acronym is BIT2010 and it has the following objectives: (i) to design, develop and implement a new Joint European Bachelor Curriculum in Information Technology, to be offered both traditionally (face-to-face) and through ODL; (ii) to establish the BIT2010 Consortium as a frame for cooperation in the Information Technology area; (iii) to promote and to disseminate the European Higher Education Area good practice and the Bologna Process. The BIT2010 project will design and implement a new Bachelor curriculum in Information Technology and will prepare its graduates for the unique challenges faced by today's technology companies and industries. The project will also train these graduates to become tomorrow's technological leaders. The teaching and learning in the program will be focused on computers and advanced technology in education, collaborative working, creativity, multidisciplinarity, adaptability, intercultural communication and problem/project solving. The BIT2010 consortium is based on six universities belonging to EU Member States: Austria, Germany, Sweden, Poland, Bulgaria and Romania.

Conference ICL2007

September 26 -28, 2007 Villach, Austria 3(6)

New ICT curriculum for the BIT2010 students

A new ICT curriculum was developed by the BIT2010 partners, based on the Career-space recommendations. This basic knowledge is essential for a broad understanding of natural processes and their utilization in technical applications. A broad foundation is also an important prerequisite to enable graduates to communicate effectively with colleagues from other areas using a common "technical language". The teaching of this core subjects should not go into too much depth, but should give students a balanced overview: it should also teach them how they can independently acquire the additional knowledge they need, both during their studies and in later professional life. Currently, the BIT2010 Curriculum includes: Scientific Base (22%); Technology Base (31%); Social-Communicative, Language and Business Skills (13%); Application Base and System Solution Methodology (22%); Thesis (12%). Future engineers need a broad spectrum of knowledge in mathematics, science and technology. The key requirements here are knowledge of system functions in the field in question, and understanding of the technological possibilities (hardware and software) to realize or implement those functions with the help of procedural methods implemented by concrete applications and system solution methodologies. Practical Work Experience -one semester as industry placement. This module was proposed because it is not sufficient just to learn about technical and other issues and pass exams; the techniques need to be used in real situations. This is particularly important to emphasize the connections between different aspects, to encourage a broad systems view and to illustrate the practical, technological and human constraints of solving real-world problems. Not only does this give practical experience of real problem solving, it should also help the student more clearly to identify the kind of work she/he would enjoy after graduation. It may also lead to mutually beneficial contacts and networking opportunities. An other important issue is focusing on Personal & Business Skills. Industry is seriously concerned that universities do not give enough attention to personal and business skills in their current ICT curricula, [START_REF]Commission tables plan to promote business spirit in schools and universities[END_REF]. The BIT2010 curriculum includes innovative practices of university, in particular as regards curriculum reform:

• Defining an IT focused educational offer, structured on the three Bologna cycles.

• A new Joint European Bachelor Curriculum in Information Technology, based on Bologna Process, that is offered both traditionally (face-to-face) and through ODL.

• Designing study plans based on the new IT industry required skills and competencies.

• Study plans includes a compulsory "practice" module • Study plans include at least one semester to study abroad, through virtual or real mobility.

• Use of remote engineering and virtual laboratory.

• Use of virtual campus and eLearning technology.

• A Public-private partnership to access eLearning resources.

• Study plans includes an "industry placement" module (students will practice in a real-life environment) and some course modules focusing on entrepreneurial education in order to increase the employability of the graduates in schools and universities • A new evaluation and assessment system and a grading scale suitable for this approach.

• University as Open Learning Center in the region, provides certain forms of learning with credits to traditional and non-traditional learners.

Recently, on March 2007, our university has participated as partner in preparing two proposals named "EUE-Net"(European University-Enterprise cooperation Network) and "Q-PlaNet"(A model Network for transnational Quality Placements in enterprises), under the EU Lifelong Learning Programme.

Advanced Digital Technology in Education

Learning resources based on e-Learning technology.

One of the main outputs of the BIT2010 project is to provide the students modern and valuable learning resources, most of them in e-Learning technology. Some of these resources will be produced by the partners, within the project. In Fig. 3 is presented a screen shot from a training course included in the ECDL package (seven courses). These courses were designed and produced within the Department for Technologies. The design includes a text based area, located on the right side of the screen.

Virtual campus communications

Our department took into consideration the use of the virtual campus as a tool for communication. A proprietary platform was developed and implemented [8]. The students have access to the following services: writeboard, calendar, chat, forums, internal messages, on-line courses, on-line exams, files (upload/download), administration, Fig. 4. There is also two pilot test implementation of the platforms based on free software: Dokeos and Moodle. They are currently under evaluation.

Remote and virtual laboratories

The department took also into consideration the use of the computers in order to run simulation of different experiments, especially in the domain of Physics education. Nowadays computers a powerful and they are able to run complex piece of software, in real time, in order to simulate the behaviour of the real systems. Simulations might be less expensive and more efficient in training. We are able to report such products developed with graphical programming tools like Interactive Physics, Crocodile-Clips etc. Our experience with the students enrolled within our department shows that 15-30 minutes of initial training provide the students enough skills to produce interesting piece of software targeted to the need of learning Physics. The highly skilled programmers can use more advanced programming languages like: LabVIEW, Macromedia Flash etc. These were presented in ICL 2005/2006. Also, the practical activities included in the CISCO Curriculum can be performed "by distance" due to the NetLAB platform installed and used within our department.

Conclusion

The international nature of this Joint Bachelor Degree will facilitate the recognition of diplomas and employment mobility across Europe. The project fulfils some important EU action lines (Joint study programs at European level, E-learning, i2010 Initiative etc.). The BIT2010 curriculum includes innovative practices of university, in particular as regards curriculum reform Clear achievements in the areas of using computers and advanced digital technologies in higher education are reported: virtual campus, e-Learning, virtual and remote laboratory. Some of these products and services are designed and produced by the staff of the involved universities.

The project partners are aiming to express their gratitude to the European Commission who decided to finance the Socrates-Erasmus CD BIT2010 project.

Fig. 1 .

 1 Fig.1. Need for New ICT Programs Fig.2. ICT Curriculum Model

Fig. 3 .

 3 Fig.3. Screen shot of an e-Learning course

  The left side of the screen is dedicated to pictures, animations, audio items, video items, simulations etc. Demo versions of these courses are available at[6].

	The students have also access to some eLearning resources provided by the public-private
	partnership established with: (i) CISCO Networking Academy Program; (ii) Microsoft IT
	Academy program. Additional information is available at [7].
	Last, but not the least, in March 2007, the University Senate Board decided to start an internal
	project aiming to encourage the academic staff to produce new courses in eLearning
	technology.

Author(s):

Bogdan Logofatu, Associated Professors University of Bucharest, Department for Technologies Bd.M.Kogalniceanu no.36-46, 050107 Bucharest, Romania Email: logofatu@credis.ro Michael Auer, Carinthia Tech Institute, School of Electronics Europastrasse no.4, Villach, Austria Email: M.Auer@cti.ac.at