RESDINET
Financiado
Network for novel remote sensing technologies in forest disturbance ecology
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...
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.
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Duración del proyecto: 40 meses
Fecha Inicio: 2022-08-30
Fecha Fin: 2025-12-31
Fecha Fin: 2025-12-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-08-30
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
HORIZON-WIDERA-2021-ACCESS-03-01:
Twinning
Cerrada
hace 54 años
Presupuesto
El presupuesto total del proyecto asciende a
2M€
Líder del proyecto
USTAV EKOLOGIE LESA SAV V. V. I.
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
330.30K€ |
Participante
383.33K€ |
Participante