A Hajnóczy-barlang mikroklímájának hatása egészséges populáció légzésfunkcióira

Saját, ill. barlangász társaink korábbi méréseire támaszkodva elvégeztük a Hajnóczy-barlang mikroklímájának elemzését, és vizsgáltuk a tábor és a barlangászat hatására kialakuló légzésfunkciós változásokat. Eredményeink azt mutatják, hogy a kúraszerű barlangi tartózkodás jelentősen és tartósan javítani képes egészséges emberek légzésfunkciós állapotát. A javulás több, egymástól elválaszthatatlan tényező komplex hatásának következménye. A spirometriás paraméterek közül az obstruktív elváltozások egyik mutatója, a PEF követte a legérzékenyebben a légzőrendszerben bekövetkező változásokat, amely megerősíti az egészséges emberek latens pozitív bronchomotor-tónusának létezésére vonatkozó feltételezésünket. | Based on former observations (made by themselves and speleologists) authors analysed the microclimate of the cave Hajnóczy ; and registered changes in the respiratory function due to the cave stay and speleologic activity. Authors experienced that a treatment-like stay in the cave could significantly improve the respiratory function of healthy individuals. This improvement is the result of several correlated factors. Among spirometric parameters, PEF (revelant for obstructive disorders) followed most precisely these changes. This later observation argues in favour of the hypothetical existence of positive bronchomotor tone in healthy individuals

Creating excess water inundation maps by sub-pixel classification of medium resolution satellite images

Excess water frequency factor, which indicates the number of inunda-tions in the area under study within a certain period of time, is the most dynamic variable among the parameters applied in the complex meth-odology of excess water hazard mapping. Creating excess water inun-dation maps, representing the situation in the most realistic way, was hitherto a critical moment in excess water hazard mapping. Instead of field measurements, since the database of Landsat satellite images became accessible in 2009, it is possible to process satellite images taken from the year 1985, with using new, non-traditional methods different from the pixel-based classification. These methods are mainly sub-pixel based classifications and they are applied principally on images taken in periods of extended excess water inundation, under clear weather conditions. In our research project, medium-scale map-ping was supported principally by hand-held or mounted multispectral (the bands of visible and infrared light) digital aerial photography. The photo-taking process, depending on the actual meteorological condi-tions, can be flexibly accomplished in the most extended inundation period, thus it is possible to create excess water maps at the scale of 1:10000

Investigation of Daily Natural and Rapid Human Effects on the Air Temperature of The Hajnóczy Cave in Bükk Mountains

The aim of this study the authors measured and analyzed the effect of the exterior daily temperature change on the interior temperature in a dripstone cave visited by cavers exclusively. The measurement was carried out in the Hajnóczy Cave located in the southern part of Bükk Mountains in Hungary. Although only one entrance is known, there are more evidences for the strong effect of exterior conditions on the interior processes like temperature fluctuation and dripstone development. Using high resolution wireless digital thermometer sensor network the air temperature and air humidity were measured in 32 points in every 10 minutes for long time but now the data of a 8-days period were analyzed. Based on these data different zones of the cave could be separated and during summer conditions the climatic variability of the entrance transitional and deep cave zone was described. Based on statistical analysis of spatial information significant correlation was found between the exterior temperature fluctuation and that of such a cave chamber, which is relatively far from the cave entrance. This fact proves that existence of a fissure system which is permeable for air but not passable for cavers. During the measurement the human effect was also analyzed and 0.3-0.6 °C temperature rising was recognized for a short time. Because of the surface vicinity the effects of the environmental change can have sensible impact on the cave and its natural phenomena. Among others temperature rising, air humidity decreasing were detected in present study

Investigation of daily natural and rapid human effects on the air temperature of the Hajnóczy cave in Bükk mountains

The aim of this study the authors measured and analyzed the effect of the exterior daily temperature change on the interior temperature in a dripstone cave visited by cavers exclusively. The measurement was carried out in the Hajnóczy Cave located in the southern part of Bükk Mountains in Hungary. Although only one entrance is known, there are more evidences for the strong effect of exterior conditions on the interior processes like temperature fluctuation and dripstone development. Using high resolution wireless digital thermometer sensor network the air temperature and air humidity were measured in 32 points in every 10 minutes for long time but now the data of a 8-days period were analyzed. Based on these data different zones of the cave could be separated and during summer conditions the climatic variability of the entrance transitional and deep cave zone was described. Based on statistical analysis of spatial information significant correlation was found between the exterior temperature fluctuation and that of such a cave chamber, which is relatively far from the cave entrance. This fact proves that existence of a fissure system which is permeable for air but not passable for cavers. During the measurement the human effect was also analyzed and 0.3-0.6 °C temperature rising was recognized for a short time. Because of the surface vicinity the effects of the environmental change can have sensible impact on the cave and its natural phenomena. Among others temperature rising, air humidity decreasing were detected in present study

Identification and Spectral Evaluation of Agricultural Crops on Hyperspectral Airborne Data

Hyperspectral remote sensing combined with advanced image processing techniques is an efficient tool for the identification of agricultural crops. In our study we pursued spectral analysis on a relatively small sample area using low number of training points to examine the potential of high resolution imagery. Spectral separability measurements were applied to reveal spectral overlapping between 4 crop species and for the discrimination we also used statistical comparisons such as plotting the PC values and calculating standard deviation of single band reflectance values on our classes. These statistical results were proven to be good indicators of spectral similarity and potential confusion of data samples. The classification of Spectral Angle Mapper (SAM) had an overall accuracy of 72% for the four species where the poorest results were obtained from the test points of garlic and sugar beet. Comparing the statistical analyses we concluded that spectral homogeneity does not necessarily have influence on the accuracy of mapping, whereas separability scores strongly correlate with classification results, implying also that preliminary statistical assessments can improve the efficiency of training site selection and provide useful information to specify some technical requirements of airborne hyperspectral surveys

Comparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping

Data fusion has shown potential to improve the accuracy of land cover mapping, and selection of the optimal fusion technique remains a challenge. This study investigated the performance of fusing Sentinel-1 (S-1) and Sentinel-2 (S-2) data, using layer-stacking method at the pixel level and Dempster-Shafer (D-S) theory-based approach at the decision level, for mapping six land cover classes in Thu Dau Mot City, Vietnam. At the pixel level, S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets (i.e. fused datasets). The datasets were categorized into two groups. One group included the datasets containing only spectral and backscattering bands, and the other group included the datasets consisting of these bands and their extracted features. The random forest (RF) classifier was then applied to the datasets within each group. At the decision level, the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory. Finally, the accuracy of the mapping results at both levels within each group was compared. The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group. The highest overall accuracy (OA) and Kappa coefficient of the map using D-S theory were 92.67% and 0.91, respectively. The decision-level fusion helped increase the OA of the map by 0.75% to 2.07% compared to that of corresponding S-2 products in the groups. Meanwhile, the data fusion at the pixel level delivered the mapping results, which yielded an OA of 4.88% to 6.58% lower than that of corresponding S-2 products in the groups