Climate change will affect arctic and subarctic ecosystems more than other ecosystems worldwide, with temperature increases expected up to 4-6°C. While this is threatening the integrity and biodiversity of the ecosystems in itself, the ecosystem feedbacks triggered are even more worrisome. During millions of years, atmospheric carbon (C) has been stored in (sub-) arctic soils. With warming, C is emitted from soils in form of carbon dioxide or methane, and contributes to further climate warming - feeding a climate warming feedback loop. Despite decades of research, scientists still struggle to accurately determine the scale of future carbon release. Overarching and basic questions remain unanswered, partially due to limited access of these remote areas and technological limitations: How much carbon will escape from the Arctic under a future climate? How do the multitude of ecosystem processes, driven by plant growth, microbial activities and soil characteristics, interact to determine soil carbon storage capacity?
The EU H2020 MSCA funded ITN 'FutureArctic' aims to pave the way for generalized permanently connected data acquisition systems for key environmental variables and processes. The consortium will initiate a new machine-learning approach to analyse large high-throughput environmental data-streams, through installing a pioneer "ecosystem-of-things" (EoT) at the ForHot research site in Iceland. FutureArctic will thus channel an important evolution to machine-assisted environmental fundamental research. This is achieved through the dedicated training of researchers with profiles at the intersectoral edge of computer science, artificial intelligence, environmental science (both experimental and modelling), social sciences and sensor engineering and communication.
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Three companies in FutureArtic focus on the development of technology, using rapid prototyping techniques, to assist in the continuous assessment of crucial ecosystem aspects. The unique expertise of VSI in FutureArctic is smart-imagery of root systems and mechatronic process automation. Thus, VSI focuses on (multispectral) image analysis of root development and turnover in the project (ESR12). VSI will further host secondments of early stage researchers (ESR3, ESR9), in close collaboration with the academic partner Dr. Ivika Ostonen, University of Tartu, for in situ validation of the developed root imaging technology.
Objectives of ESR12: To develop an automated minirhizotron (MR) imaging system to facilitate root phenological studies in situ. This will be applied in an autonomous, permanently installed and remote-controllable MR system, tested and validated at the field site in Iceland. The ESR will also identify multi-spectral wavelength (ranges) allowing for enhanced segmentation and species-specific differentiation of three exemplary artic target root systems.
Expected Results of ESR12: Advanced image capturing able to optimize separation between roots and soil. A HD MR imaging system is designed, prototyped and field-tested, able to record root and hyphae images autonomously in remote areas and extreme arctic conditions. Strategies how to integrate multi spectral imaging approaches in future generations of MR imaging systems will be developed, to facilitate future utilisation of hyper- or multispectral imaging approaches to study roots in situ.
Planned Secondments of ESR12:
Baykalov, P., Bussmann, B., Nair, R. et al. (2023). Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models. Plant Methods 19, 122.
Further publications are in preparation.
The Innovative Training Networks (ITN) of the EU aim to train a new generation of creative, entrepreneurial and innovative early-stage researchers, able to face current and future challenges and to convert knowledge and ideas into products and services for economic and social benefit. ITN will thus raise excellence and structure research and doctoral training in Europe, extending the traditional academic research training setting, incorporating elements of Open Science and equipping researchers with the right combination of research-related and transferable competences. It will provide enhanced career perspectives in both the academic and non-academic sectors through international, interdisciplinary and intersectoral mobility combined with an innovation-oriented mind-set. The aims of ITN are thus completely in line with our aims at Vienna Scientific Instruments, to enhance cross-sectoral collaboration to support the Knowledge Society.
Both beneficiaries and partners of FutureArctic are active in the network-wide training program. Partner NWTE ensures that all consortium members can train the whole ESR community in their speciality, ranging from ecosystem science (UAntwerpen, UIBK, UNIVIE, UTARTU, UCPH, CREAF, LBHI), to the development of sensor applications and business management at the interface of natural sciences and engineering (VSI, Svarmi, DMR, PRENART, MIRICO), over novel machine-learning techniques to analyse data-streams and implement results into new environmental models (IMEC, Microsoft), to societal and agricultural applications of environmental research and the potential impact of big data on the future of science (ILVO, UNIVIE).
Call: H2020-MSCA-ITN-2018 (Marie Skłodowska-Curie Innovative Training Networks)
Funded by: European Commission (Horizon 2020), Brussels
Funding duration: 48 month (ESRs: 36 month each)
Start: 1. June 2019
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813114