From Trunk to Crown: Multiview Drone Remote Sensing for Comprehensive Tree Health Assessment скачать в хорошем качестве

From Trunk to Crown: Multiview Drone Remote Sensing for Comprehensive Tree Health Assessment

Instagram:   / resdinet   Facebook:   / resdinet   X: https://x.com/DE_Research_ Website: https://www.resdinet.eu ************************************************************ The fourth RESDiNET Workshop in the use of remote sensing data in forest disturbance ecology, Oct 27-31, 2025, Tatranska Javorina, Slovakia. ************************************************************ Prof. Eija Honkavaara from the National Land Survey Finland presented her study entitled "From Trunk to Crown: Multiview Drone Remote Sensing for Comprehensive Tree Health Assessment" at the RESDiNET 4th Workshop, October 29, 2025. The study is co-authored also by Alva Anttonen, Emma Turkulainen, Eetu Pakkanen, Väinö Karjalainen, Yibo Zhang, Teemu Hakala, Niko Koivumäki,Päivi Lyytikäinen-Saarenmaa, Roope Näsi, and Raquel Alves de Oliveira. ************************************************************ RESDiNET is an international research project funded by the European Research Executive Agency, Horizon Europe. The project enhances networking activities between research institutions in Widening country (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 project establishes an initial network and the development of a new joint research project in novel RST applications in FDE. Rigorous analyses of severe insect-induced disturbances using novel RST is carried out in test areas representing different forest and climate types: mountain forests in Slovakia and boreal forests in Finland and Sweden. The project integrates 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 is used to develop new tools for landscape-level early bark beetle attack identification and for designing bark beetle infestation risk assessment models. We 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.