Share:


Features of the international MSc educational programme in environmental management and modelling

Abstract

“International Msc Educational Programme in Environmental Management and Modelling” (GeoNetC) is a European Commission funded project under ERASMUS+: Higher Education – International Capacity Building programme (Project No 561967-EPP-1-2015-1-SE-EPPKA2-CBHE-JP). It began in October 2015 and ended in October 2018. Initiated by the Lund University and partners from the Middle East countries, the GeoNetC project is an ambitious project aiming to match labour market needs with geospatial education offer both in Europe and Middle East countries.


The aim of this three-year project is to enable European universities to exchange best practices and innovation with each other and with Middle Eastern universities regarding the mismatch between Europe’s geospatial education and training and the geospatial education in Middle East countries. There is a growing need for well-trained students at all levels – vocational, bachelors, masters – in the field of geospatial technologies. Obviously there is a growing number of jobs available in land surveying, mapping data collection, data processing, data delivery and turning data into information in both European and Middle East countries.


Through cooperation, all partners will improve the quality of their respective academic programs. The European partners will make their courses more attractive and well adjusted for students from the Middle East. As well, they will increase the general quality and add state-of-the-art learning components to their offerings, and the partners from the region will significantly increase the academic level and quality in the education they provide. There will be spin-offs into other subjects than environment/Geomatics, since both the pedagogic models developed (e.g. e-Learning) and communication and administrative tools can be used throughout the partner universities. Therefore, this partnership cooperation will be of great value to Partner Countries as well as to Programme Countries.


A number of distance learning courses/modules are developed jointly by partner institutions in Europe and the Middle East. The main aim of the network is to promote the use of spatial information and earth observation for environmental management and modelling through capacity building and institutional development, via a network in which all partners would contribute from their own positions of strength.


All 13 modules are following EU higher education standards regarding e.g. ECTS, and learning outcomes. The outcome of the project, in terms of courses/modules, will be freely used among the partners, with the possibilities of offering individual courses or a whole MSc programme, whether individually or together.


All produced material was evaluated/quality controlled by an external evaluation group of independent experts within environmental management and modelling, higher education, as well as pedagogy.

Keyword : higher education, geospatial education, geoinformation system, well-trained student, distance learning system

How to Cite
Pilesjo, P., Mansourian, A., Runnstrom, M., Groth, R., Goncalves, A., Falcao, A. P., Matias, M. S. P., van Leeuwen, L., Looijen, J., Vrieling, A., Paršeliūnas, E., Sužiedelytė-Visockienė, J., Popovas, D., Obuchovski, R., Šlikas, D., Būga, A., Manafi, M., Toomanian, A., Amin, A. A., Saeed, M. A., Al Bazzaz, R. H., Hassan, H. H., Feizi, H., Karbasi, A., Yasrebi, E., & Mohtashami, T. (2018). Features of the international MSc educational programme in environmental management and modelling. Geodesy and Cartography, 44(4), 134-139. https://doi.org/10.3846/gac.2018.6294
Published in Issue
Dec 31, 2018
Abstract Views
1003
PDF Downloads
533
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Alavipanah, S. K., Hamzeh, S., Jeihouni, M., & Toomanian, A. (2017). Quantitative assessment of Urmia Lake water using spaceborne multisensor data and 3D modeling. Environmental Monitoring and Assessment, 189(11), 572.

Antman, L., Larson, L., Pilesjö, P., & Mårtensson, U. (2006). Experiences from the LUMA-GIS eLearning master’s program: Student perspective and pedagogic models. In [Host publication title missing], Fifth European GIS Education Seminar, Krakow, Poland.

Ardakani, A. S., Valadan Zoej, M. J., Mohammadzadeh, A., & Mansourian, A. (2011). Spatial and temporal analysis of fires detected by MODIS data in northern IRAN from 2001 to 2008. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(1), 216-225. https://doi.org/10.1109/JSTARS.2010.2088111

Asare-Kyei, D. K., Forkour, G., & Venus, V. (2015). Modeling flood hazard zones at the sub-district level with the rational model integrated with GIS and remote sensing approaches. Water, 7(7), 3531-3564. https://doi.org/10.3390/w7073531

Chemura, A., van Duren, I. C., & van Leeuwen, L. M. (2015). Determination of the age of oil palm from crown projection area detected from WorldView-2 multispectral remote sensing data: the case of Ejisu-Juaben district, Ghana. ISPRS Journal of Photogrammetry and Remote Sensing, 100, 118-127. https://doi.org/10.1016/j.isprsjprs.2014.07.013

Dehghani, M., Valadan Zoej, M. J., Entezam, I., Mansourian, A., & Saatchi, S. (2009). InSAR monitoring of progressive land subsidence in Neyshabour, Northeast Iran. Geophysical Journal International, 178, 47-56. https://doi.org/10.1111/j.1365-246X.2009.04135.x

Dijkstra, P., Pirlot, J. Y., Velichkov, D., Paršeliūnas, E., Wijngaarde, M., Donert, K., Frigne, D., Westerbeek, H., Levoleger, K., Popovas, D., Obuchovski, R., Šlikas, D., & Būga, A. (2015, July 6-8). Promoting of the geospatial skills: first results of the Geoskills plus project of the Leonardo Da Vinci programme. In EDULEARN 15 Proceedings, 7th International Conference on Education and New Learning Technologies, Barcelona, Spain (pp. 7270-7280). Valencia: IATED.

Dijkstra, P., Pirlot, J. Y., Velichkov, D., Paršeliūnas, E., Wijngaarde, M., Donert, K., Frigne, D., Westerbeek, H., Levoleger, K., Popovas, D., Obuchovski, R., Šlikas, D., & Būga, A. (2014, July 7-9). Promoting of the geospatial skills: introduction into Geoskills plus project of the Leonardo da Vinci programme. In EDULEARN 14, 6th International Conference on Education and New Learning Technologies, Barcelona, Spain, [CD] (pp. 7587-7596). Valencia: IATED.

Falcão, A. P., Matias, M. P., Pestana, R., Gonçalves, A. B., & Heleno, S. (2016). Methodology to combine topography and bathymetry datasets for hydrodynamic simulations: Case of Tagus River. Journal of Surveying Engineering, 142(4). Article ID 05016005. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000192

Farnaghi, M., & Mansourian, A. (2013a). Automatic composition of WSMO based geospatial semantic web services using artificial intelligence planning. Journal of Spatial Science, 58(2), 235-250. https://doi.org/10.1080/14498596.2013.815148

Farnaghi, M., & Mansourian, A. (2013b). Disaster planning using automated composition of semantic OGC web services: A case study in sheltering. Computers, Environment and Urban Systems, 41, 204-218. https://doi.org/10.1016/j.compenvurbsys.2013.06.003

Girma Gebrekidan, A., de Bie, C. A. J. M., Skidmore, A. K., Venus, V., & Bongers, F. (2016). Hyper-temporal SPOT-NDVI dataset parameterization captures species distributions. International Journal of Geographical Information Science, 30(1), 89-107. https://doi.org/10.1080/13658816.2015.1082565

Hussin, Y. A., Gilani, H., van Leeuwen, L. M., Murthy, M. S. R., Shah, R., Baral, S., & Qamer, F. M. (2014). Evaluation of object – based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal. Applied Geomatics, 6(1), 59-68. https://doi.org/10.1007/s12518-014-0126-z

Jeihouni, M., Alavipanah, S. K., Toomanian, A., & Hamzeh, S. (2015). Assessing the spatio-temporal variations of Tabriz plane aquifer salinization and its relation with Urmia lake water level. Journal of Fundamental and Applied Life Sciences, 5(3), 1228-1236.

Jeihouni, M., Toomanian, A., Alavipanah, S. K., Shahabi, M., & Bazdar, S. (2015). An application of MC-SDSS for water supply management during a drought crisis. Environmental Monitoring and Assessment, 187(7), 396. https://doi.org/10.1007/s10661-015-4643-y

Moura, F., Cambra, P., & Gonçalves, A. B. (2017). Measuring walkability for distinct pedestrian groups with a participatory assessment method: A case study in Lisbon. Landscape and Urban Planning, 157, 282-296. https://doi.org/10.1016/j.landurbplan.2016.07.002

Parseliunas, E., Sponberg, H., & Stankevicius, Z. (2005). European level developments of flexible learning models within geographical information science for vocational training. In Selected Papers of 6th International Conference “Environmental Engineering” (Vol. 2, pp. 957-963). Vilnius: Technika.

Patel, N. R., Parida, B. R., Venus, V., Saha, S. K., & Dadhwal, V. K. (2013). Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra. MODIS satellite data. Environmental Monitoring and Assessment, 184(12), 7153-7163. https://doi.org/10.1007/s10661-011-2487-7

Pilesjö, P., Skidmore, A., Kooiman, A., & Mårtensson, U. (2007, May 8-11). GEM – the first GI Erasmus Mundus masters course. Paper presented at 10th AGILE International Conference on Geographic Information Science, Aalborg, Denmark.

Ragauskas, U., Bručas, D., & Sužiedelytė-Visockienė, J. (2016). Research of remotely piloted vehicles for cargo transportation. Aviation, 20(1), 14-20. https://doi.org/10.3846/16487788.2016.1168006

Rajabi, M. R., Mansourian, A., Pilesjo, P., & Bazmany, A. (2014). Environmental modelling of visceral leishmaniasis by susceptibility mapping using neural networks: a case study in northwestern Iran. Geospatial Health, 9(1), 179-191. https://doi.org/10.4081/gh.2014.15

Rajabi, M. R., Pilesjo, P., Shirzadi, M. R., Fadaei, R., & Mansourian, A. (2016). A spatially explicit agent-based modeling approach for the spread of cutaneous leishmaniasis disease in central Iran, Isfahan. Environmental Modelling & Software, 82, 330-346. https://doi.org/10.1016/j.envsoft.2016.04.006

Sponberg, H., Onstein, E., Johansen, F. J., Ossiannilsson, E., Pilesjö, P., & Mårtensson, U. (2007, November 8). Learning GIS over the Internet. Paper presented at EADTU, International Courses and Services Online Virtual Erasmus and New Generation of Open Educational Resources for a European and Global Outreach.

Sužiedelytė-Visockienė, J., Bagdžiūnaitė, R., Malys, N., & Malienė, V. (2015). Close-range photogrammetry enables documentation of environment-induced deformation of architectural heritage. Environmental Engineering and Management Journal, 14(6), 1371-1381. https://doi.org/10.30638/eemj.2015.149

Sužiedelytė-Visockienė, J., Bručas, D., Bagdžiūnaitė, R., Puzienė, R., Stanionis, A., & Ragauskas, U. (2016). Remotely-piloted aerial system for photogrammetry: Orthoimage generation for mapping applications. Geografie, 121(3), 349-367.

Sužiedelytė-Visockienė, J., Puzienė, R., Stanionis, A., & Tumelienė, E. (2016). Unmanned aerial vehicles for photogrammetry: analysis of orthophoto images over the territory of Lithuania. International Journal of Aerospace Engineering, 2016, 1-9. Article ID 4141037. https://doi.org/10.1155/2016/4141037

Toomanian, A., Harrie, L., Mansourian, A., & Pilesjo, P. (2013). Automatic integration of spatial data in viewing services. Journal of Spatial Information Science, 6. https://doi.org/10.5311/JOSIS.2013.6.87