Network for novel remote sensing technologies in forest disturbance ecology

Widening ParticipationHORIZON-CSAID: 101078970
EC Contribution
€14,959
Consortium Size
4 orgs
Start Year
2023
Summary

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.

Consortium (4)

Project Results (16)

Source: CORDIS, the EU research results database.

Publications (11)
A Multiview UAV Imagery-Based Method for Assessing Spruce Tree Health at the Individual Tree Level
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 2025DOI
Alva Anttonen, Axel Päivänsalo, Emma Turkulainen, Raquel Alves Oliveira, Kirsi Karila, Niko Koivumäki, Roope Näsi, Eija Honkavaara
Carotenoids increase as an indicator of early stress of trees: estimations using Green Shoulder Indices from hyperspectral drone data and radiative transfer model PROSAIL
IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium· 2025DOI
Langning Huo, Niko Koivumäki, Roope Näsi, Eija Honkavaara
Domain Generalization in Deep Learning for Forest Health Monitoring Using Multispectral UAS Data
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 2025DOI
Emma Turkulainen, Raquel Alves Oliveira, Niko Koivumäki, Päivi Lyytikäinen-Saarenmaa, Roope Näsi, Mikko Pelto-Arvo, Johanna Tuviala, Eija Honkavaara
Hyperspectral drone images indicate that green shoulder indices are robust in pre-emergence detection of spruce bark beetle infestation across spatial and temporal scales
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 2025DOI
Luiz Henrique Elias Cosimo, Eva Lindberg, Henrik Persson, Jonas Bohlin, Langning Huo
International Journal for Remote Sensing
Sensitivity analysis of the Green Shoulder indices in pre-emergence detection of single trees attacked by European spruce bark beetle· 2025DOI
Huo, Langning
Multispectral drone images for the early detection of bark beetle infestations: assessment over large forest areas in the Italian South-Eastern Alps
Frontiers in Forests and Global Change· 2025DOI
Bozzini, Aurora; Huo, Langning; Brugnaro, Stefano; Morgante, Giuseppe; Persson, Henrik Jan; Finozzi, Valerio; Battisti, Andrea; Faccoli, Massimo
Sensitivity analysis of the Green Shoulder indices in pre-emergence detection of single trees attacked by European spruce bark beetle
International Journal of Remote Sensing· 2025DOI
Langning Huo, Niko Koivumäki, Roope Näsi, Eija Honkavaara
Spectral signatures discrimination of Norway spruce trees under experimentally induced drought and acute thermal stress using hyperspectral imaging
Forest Ecology and Management· 2025DOI
Matúš Pivovar, Roope Näsi, Eija Honkavaara, Miroslav Blaženec, Jaroslav Škvarenina, Roman Modlinger, Jaroslav Rožnovský, Rastislav Jakuš
Towards scalable wide area UAS monitoring of forest disturbance using hydrogen powered airships
International Journal of Remote Sensing· 2025DOI
Emma Turkulainen, Janne Hietala, Jiri Jormakka, Johanna Tuviala, Raquel Alves de Oliveira, Niko Koivumäki, Kirsi Karila, Roope Näsi, Juha Suomalainen, Mikko Pelto-Arvo, Päivi Lyytikäinen-Saarenmaa, Eija Honkavaara
Bark beetle pre-emergence detection using multi-temporal hyperspectral drone images: Green shoulder indices can indicate subtle tree vitality decline
ISPRS Journal of Photogrammetry and Remote Sensing· 2024DOI
Huo, Langning
Large-Area UAS-Based Forest Health Monitoring Utilizing a Hydrogen-Powered Airship and Multispectral Imaging
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 2024DOI
E. Turkulainen; J. Hietala; J. Jormakka; J. Tuviala; R. A. de Oliveira; N. Koivumäki; K. Karila; R. Näsi; J. Suomalainen; M. Pelto-Arvo; P. Lyytikäinen-Saarenmaa; E. Honkavaara
Deliverables (5)