We investigated habitat preference of Common Sandpipers as part of a monitoring program in the Őrség National Park, Hungary. Field observations were conducted during the summers between 2008 and 2012 along a 47-km long section of the River Rába. During the observations we recorded the number and location of birds on the river bank. We divided the studied area into 1 km long sections and measured the proportion of the visually distinguishable habitat types (water, low gravel and sand bank, vegetation and degraded area) from a digitalized map. Furthermore, we recorded the number of the low banks and the number of bends of the river within each section, as well as the sections’ distance from the closest hydroelectric power plants and human settlements. In 2012 we also performed a detailed habitat mapping, recording the proportion of the vegetation types along the river bank and the number of fishing spots, embankment strengthenings and gravel banks. We tested the correlations between these habitat variables and number of birds present in the river sections. Our results show that Common Sandpipers were observed more frequently in locations which have (1) larger number and area of low gravel and sand banks, (2) less dense vegetation, and (3) lower proportion of degraded habitats. These findings can be taken into account in the conservation management of River Rába
In the current study, aerial image analysis has been applied to map vegetation communities in a riparian wetland ecosystem, Szigetköz (Hungary). Remote sensing offers an objective and timeeffective method for the detection of detailed vegetation habitats with the use of high resolution aerial photos combined with ancillary botanical and silvicultural data. Three images of the same test site, acquired in three different years have been analysed by sample-based semi-automated image classification technique. Due to the heterogeneous nature of the target vegetation classes, besides using spectral features (e.g. vegetation indices) textural descriptors were also involved in the classification procedure. The most appropriate parameters have been chosen using a statistical feature selection method based on the Jeffries-Matusita distance. The accuracy assessment proved for each scene that the combined use of spectral and textural features gave the best classification results in comparison to the exclusive use of spectral or textural measures. The here-applied feature set can be applied for the analysis of similar riparian sites.