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Environmental objective analysis, ranking and clustering of Hungarian cities
91-108Views:32The aim of the study was to rank and classify Hungarian cities and counties according to their
environmental quality and level of environmental awareness. Ranking of the Hungarian cities and
counties are represented on their „Green Cities Index” and „Green Counties Index” values. According
to the methodology shown in Part 1, cities and counties were grouped on different classification
techniques and efficacy of the classification was analysed. However, they did not give acceptable
results either for the cities, or for the counties. According to the parameters of the here mentioned
three algorithms, reasonable structures were not found in any clustering. Clusters received applying
algorithm fanny, though having weak structure, indicate large and definite regions in Hungary, which
can be circumscribed by clear geographical objects. -
Agricultural sector, rural environment and biodiversity in the Central and Eastern European EU member states
46-64Views:26During the second half of the 20th century, agriculture and the rural environment diverged in Western
and Central and Eastern European countries (CEEC). CEE countries itself are heterogeneous in the
respect of land use intensity and history. In the current review we focus on the comparison of the
agricultural sector and threats on biodiversities of EU new-member countries from Central and
Eastern Europe and the old EU(15) member states. The clustering of countries revealed groups
distinguished according to the level of their economic productivity, discriminating mostly among
eastern and western European countries. CEE countries sub-divided according to geographic region,
including also some old members of the EU. Within the western cluster, two large sub-clusters
became evident according to economy affected by altitudinal and climatic differences. Partly because
there are still areas where the intensity of land use remained low, the biological diversity in many
regions of Central and Eastern Europe has remained high. However, loss of extensively used habitats,
the restoration on intensive agriculture, reforestation with exotic species and urbanization are major
threats to nature in CEE countries. The estimated variability among CEE countries is caused by
different historical and cultural backgrounds of those countries. Due to the complexity and
geographical diversity of driving forces, there remains much uncertainty in the possible impacts of
particular factors on land use. This complexity and diversity have to be considered when planning
economic as well as ecological means for developing the agricultural sector and conserving
biodiversity in the future of CEE countries. -
Unsupervised classification of high resolution satellite imagery by self-organizing neural network
37-44Views:35The current paper discusses the importance of the modern high resolution satellite imagery. The acquired high amount of data must be processed by an efficient way, where the used Kohonen-type self-organizing map has been proven as a suitable tool. The paper gives an introduction to this interesting method. The tests have shown that the multispectral image information can be taken after a resampling step as neural network inputs, and then the derived network weights are able to evaluate the whole image with acceptable thematic accuracy.