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Research project (§ 26 & § 27)
Duration : 2020-08-28 - 2021-05-27

For the assessment of the game influence on forest regeneration, it is important to characterize both the regeneration potential, the silvicultural conditions (e.g. forest stand structures, canopy cover, ground vegetation, etc) and game influence parameters of the forest ecosystems. This should enable the establishment and development of the factors, which influence the regeneration process to be differentiated from those factors of the game influence. Possibilities and added value through the combination of terrestrial surveys and remote sensing techniques are developed. Furthermore, the combination of inquiries about forest structure, forest regeneration and ground vegetation provide an important basis for decision-making with regard to the assessment of habitat quality and habitat capacity. Recommendations for forest and game management, based on the scientific and research-relevant elaborations, will be derived.
Research project (§ 26 & § 27)
Duration : 2020-03-01 - 2022-02-28

TEACHER-CE is integrating and harmonizing results of previously funded projects recognizing their links to topic V-Climate change (CC) adaptation and risk prevention. The main territorial challenge to be addressed concern development of effective CC adaptation processes and prevention of water-related risks in CE, where the effects can be already clearly observed and, in future years, could have strong impact at territorial level. The main objective is to develop an integrated TEACHER-CE Toolbox focusing on management of water resources, including CC, floods/heavy rain/drought risk prevention, small water retention measures and protection of water resources through sustainable land-use management, based on the integration of tools of selected projects: RAINMAN, FRAMWAT, PROLINE-CE, SUSTREE, LUMAT (all CE); H2020 FAiRWAY; LifeLocalAdapt; DRIDANUBE and DAREFFORT (DTP), C3SDisaster Risk Reduction Sectoral Information System and C3S Soil Erosion Demo Case. The project focuses on downstreaming of project outputs/tools to municipalities/regions; TEACHER-CE project will build on tools for integrated water management including CC adaptation and risk prevention of previously funded projects. Experiences gained on local level within TEACHER-CE will be applied for maximizing Toolbox use to effectively and robustly upstream CC adaptation in sectoral plans. The innovation of TEACHER is the controlled and documented integration process of outputs and multiple tools of previously funded projects from different funding programs in a single toolbox with testing and verification in 9 pilot actions in 8 countries. The project will demonstrate how a transnational, level-spanning and cross-sectoral designed partnership creates innovation by focusing on one central topic by fostering a critical exchange of knowledge and experiences of persons and institutions involved aiming on rapid and thorough CC adaptation process of different sectors, having land-use management in specific focus.
Research project (§ 26 & § 27)
Duration : 2020-01-01 - 2022-06-30

In recent years, the Alps and other mountain regions in Europe have been increasingly affected by forest fires. Forest fires pose a major threat to the protective function of mountain forests and the provision of ecosystem services for the economy, tourism and society. The current forecasting tools for estimating the danger of forest fires often have a too coarse resolution, are based exclusively on weather information and do not take into account the actual surface moisture and vegetation conditions of forests. The CONFIRM project aims to integrate high-resolution Copernicus Sentinel 1 and 2 observational data of surface moisture and vegetation conditions with LiDAR data, predictions of current weather conditions, socio-economic information, topographic data and a forest fire database to develop a novel, high-resolution and satellite-based integrated forest fire hazard system (IFDS) for Austria and neighbouring regions. The aim is to use novel remote sensing methods and state-of-the-art machine learning methods to develop daily analyses and forecasts of the ignitability and propagation risk of forest fires according to the requirements of meteorologists, fire brigades, forest specialists and infrastructure providers.

Supervised Theses and Dissertations