Descrizione del progetto
Tecnologie di rilevamento innovative per la gestione dei disturbi forestali
L’innovazione tecnologica è fondamentale nel campo della ricerca relativa ai disturbi forestali. Il progetto RESDINET, finanziato dall’UE, intende incrementare le attività di collegamento in rete tra l’Istituto di ecologia forestale dell’Accademia slovacca delle scienze (IFE SAS), l’Istituto finlandese di ricerca geospaziale, l’Università della Finlandia orientale e l’Università svedese di scienze agrarie. Il progetto accrescerà la reputazione, il profilo di ricerca e l’attrattiva dell’IFE SAS migliorando le capacità di gestione, le competenze amministrative e le capacità scientifiche del suo personale in merito alle tecnologie innovative di telerilevamento nell’ecologia dei disturbi forestali. RESDINET effettuerà analisi rigorose di gravi disturbi indotti da insetti applicando nuove tecnologie di telerilevamento nelle foreste montane slovacche e nelle foreste boreali in Finlandia e Svezia.
Obiettivo
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.
Campo scientifico
- natural sciencesbiological sciencesecology
- engineering and technologyenvironmental engineeringremote sensing
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- agricultural sciencesagriculture, forestry, and fisheriesforestry
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
HORIZON-CSA - HORIZON Coordination and Support ActionsCoordinatore
960 01 Zvolen
Slovacchia