The academic picture of a globalized European countryside, and particularly of rural areas in postsocialist, new member states of the European Union, is one of huge and increasing complexity, diversity, and uncertainties about the future. The aim of this research is to construct alternative scenarios for rural Croatia in 2030, acknowledging its postsocialist transition as an important framework. Future development scenarios were constructed by integrating quantitative and qualitative approaches. The main methods used were: factor and cluster analysis; Monte Carlo simulation; and Delphi method, involving 37 rural experts in two rounds of written questionnaires. Four scenarios were developed: Rural Renaissance, Shift, Road to Nowhere, and Growth without Development. These scenarios provide a set of well-documented and reasonable assumptions to aid in thinking about possible future paths for the Croatian countryside, while at the same time allowing for the discussion of rural development paradigms.

The paper deals with spatial analysis of catchment areas of the public primary schools in the City of Zagreb. Spatial analysis was based on data gathered from city administration bodies and the Croatian Bureau of Statistics. These data...

Supervised classification of remote sensing imagery implies usage of a priori knowledge of analysed area of interest for proper selection of land cover classes’ samples. Spectral and geometric properties of the satellite image were...

Changes in land use and land cover are the result of complex interactions between humans and their environment. This study examines land use and land cover changes in the Lower Neretva Region between 1990 and 2020. Political and economic changes in the early 1990s resulted in changes in the landscape, both directly and indirectly. Multispectral image processing was used to create thematic maps of land use and land cover for 1990, 2005, and 2020. Satellite images from Landsat 5, Landsat 7 and Landsat 8 were the main source of data. Land use and land cover structure was assessed using a hybrid approach, combining unsupervised and manual (visual) classification methods. An assessment of classification accuracy was carried out using a confusion matrix and kappa coefficient. According to the results of the study, the percentage of built-up areas increased by almost 33%. Agricultural land and forests and grasslands also increased, while the proportion of swamps and sparse vegetation areas decreased.