| Project Detail |
Innovative sensing technologies for forest disturbance management
Innovative technologies are pivotal in research concerning forest disturbances. The EU-funded RESDINET project aims to increase networking activities between the Institute of Forest Ecology of the Slovak Academy of Sciences (IFE SAS), the Finnish Geospatial Research Institute, the University of Eastern Finland and the Swedish University of Agricultural Sciences. The project will raise the reputation, research profile, and attractiveness of IFE SAS by improving IFE SAS staff management capacities, administrative skills, and scientific capabilities concerning innovative remote sensing technologies (RST) in forest disturbance ecology (FDE). RESDINET will perform rigorous analyses of severe insect-induced disturbances by applying novel RST in mountain forests in Slovakia and boreal forests in Finland and Sweden.
The proposed project enhances networking activities between research institution in Widening country (Institute of Forest Ecology, Slovak Academy of Sciences, IFE SAS) and top-class counterparts at the EU level (Finnish Geospatial Research Institute, The University of Eastern Finland and Swedish University of Agricultural Sciences). The project builds on networking for excellence through knowledge transfer and exchange of best practices between involved institutions. The major result will be raising reputation, research profile and attractiveness of IFE SAS. Project implementation will enhance IFE SAS staff management capacities, administrative skills and scientific capabilities in the use of novel remote sensing technologies (RST) in forest disturbance ecology (FDE). The project proposes establishment of initial network and development of a new joint research project in novel RST applications in FDE. Rigorous analyses of severe insect-induced disturbances using novel RST will be carried out in test areas representing different forest and climate types: mountain forests in Slovakia and boreal forests in Finland and Sweden. We will integrate in situ UAV and drone acquired remotely sensed data, existing multitemporal geospatial information and field data, particularly data on bark beetle population density, visible infestation symptoms linked to outbreak phases, and tree physiology parameters measured using electronic dendrometers or sapflow meters. The combined dataset will be used to develop new tools for landscale-level early bark beetle attack identification and for designing bark beetle infestation risk assessment model. We will draw on the latest advances in drone technologies and image analytical tools, including deep Convolutional Neural Networks based machine learning techniques and Artificial Intelligence algorithms. We expect to obtain important scientific results and contribute new knowledge to this scientific field. |
