Artificial barriers significantly disturb the landscape unit. Roads split the contiguous landscape units, thus basically modi fying their ecological characters. The more artificial barriers are constructed in the landscape, the more fragmented it is. Theref ore, the contiguous landscape unit is divided into two or more patches, weakening resilience and stability of ecological systems. During decrease in patch size, the stability reduces until the patch size is at its minimum viable or effective population size. In current study analysing the degree of fragmentation caused by artificial barriers in meso-scale landscape units (meso-regions) we can get an overall picture about changes in their stability and sensitivity. The major aims of this study is to investigate the fragmentation of landscape units caused by three types of artificial barriers (roads, railways and settlements) in micro-regions, and to measure the degree of fragmentation and its spatial-temporal (1990, 2011 and future scenario to 2027) changes using mathemat ical/ statistical analysis and landscape metrics (Number of Patches, Division, Landscape Splitting Index and Effective Mesh Size). By calculating landscape fragmentation metrics, the micro-regions are identified, which must be protected with high priority in the future. In the planning processes, type and position of artificial barriers could be more properly determined by calculation of these landscape metrics.
According to the forecasts of numerous regional models (eg. REMO, ALADIN, PREGIS), the number of predicted rainfall events decreases, but they are not accompanied by considerably less precipitation. It represents an increase in rainfall intensity. It is logical to ask (if the limitations of the models make it possible) to what extent rainfall intensity is likely to change and where these changes are likely to occur in the long run. Rain intensity is considered to be one of the key causes of soil erosion. If we know which areas are affected by more intense rain erosion, we can identify the areas that are likely to be affected by stronger soil erosion, and we can also choose effective measures to reduce erosion. This information is necessary to achieve the neutral erosion effect as targeted by the EU. We collected the precipitation data of four stations every 30 minute between 2000 and 2013, and we calculated the estimated level of intensity characterizing the Carpathian Basin. Based on these data, we calculated the correlation of the measured data of intensity with the values of the MFI index (the correlation was 0.75). According to a combination of regional climate models, precipitation data could be estimated until 2100, and by calculating the statistical relationship between the previous correlation and this data sequence, we could estimate the spatial and temporal changes of rainfall intensity.
An integrated approach was applied in this article to provide a medium-scale map of land use intensity for Hungary. The main goal was to estimate its value by a small set of parameters, which are freely available and have a high resolution. The basis of the evaluation was the CORINE 2012 dataset, and a matrix method was applied to integrate the ratio of natural/semi-natural vegetation, woody vegetation and the Natural Capacity Index in the assessment to describe the complex approach of land use intensity. The medium level land use intensity map provides information for decision makers/landscape planners on the current status and spatial pattern of anthropogenic impact and indicates those hot-spots where land use intensity is high and should be focused research and management to intervene in order to encourage sustainable land use. 46% of the arable lands in Hungary show the most intensive land use. Comparing the map with the previously published hemeroby map of Hungary, more intensive impact on landscape transformation through human action was found. In agricultural areas both researches agree that the intensity and human activity is really high, and the lowest intensity class is rare in Hungary except for mountain regions and protected areas.