Latest SCI publications
Research project (§ 26 & § 27)
Duration : 2000-07-01 - 2002-12-31
Estimating regeneration establishment is a hampered by the difficulty in collecting regeneration data and random impacts in the occurrence of regeneration. Artificial neural networks represent a computational methodology widely used to uncover the structure of a large variety of data. In general, one may recommend the application of neural networks in areas characterized by noise, poorly understood intrinsic structure and changing characteristics. Each of those characteristics is present in predicting regeneration establishment within uneven aged mixed species stands. In this project we develop a design and estimation procedure to predict regeneration establishment using data from the experimental forest, University of Agriculture in Vienna, Austria. The result of the study should provide us with tools to estimate the number of juvenile trees per unit area, the relative percentage of individuals by tree species and the mean regeneration height needed to initialize existing juvenile tree growth models.
As a result of global warming increased exceptional floods and extreme heavy precipitation events take place. So the risk of remobilization of deposits increases. Subsequently radioactive heavily contaminated sediments can be mobilized. At LLC-Laboratory Arsenal radioactivity of the danube compartiments: water (dissolved radionuclides), suspended matter and sediment are continuously monitored based on monthly composite samples and event-related samples during floods since 1984. This is a unique Central European radioecological long time series of measurements. The continuation of this sampling and data collection is of great importance to meet future challenges in radiation protection with regard to potential large-scale environmental contamination.
Research project (§ 26 & § 27)
Duration : 2018-09-01 - 2020-06-30
Maps of the growth potential for the non-native coastal Douglas-fir (Pseudotsuga menziesii var. menziesii) are presented for Austria and Germany. For deriving the growth potentials we used an available site-sensitive statistical model which determines productivity as the dominant-height at age 60 (SI60). The model was calibrated using data from 28 Douglas fir stands in Austria and Germany growing on silicate and carbonate bedrock. The model is based on a non-linear relationship between SI60 versus ten climatic as well as soil physical and chemical site parameters at a 1 km x 1 km grid for Austria and Germany. The results revealed that the Northern Alpine foothills in southern Germany are an area with a particularly high growth potential according to the current climatic conditions (mean 1970-2000). We applied two climate change scenarios (RCP 4.5 and RCP 8.5) to assess the future productivity of Douglas fir. The results show that changing growth conditions will have a positive impact on Douglas fir growth at higher altitudes, whereas the productivity may decline in areas where the current growing conditions are dry or highly productive.