Project Details
Description
The overall objective of ARTISDIG is to pioneer the science behind a digital twin of the Earth’s forests, capturing their diversity, growth and productivity. New scientific results would enable to integrate forest biodiversity in the Digital Twin Earth, which is being implemented via the Destination Earth (DestinE) initiative as a part of the European Green Deal. ARTISDIG will develop novel methods to quantify and monitor boreal forests’ structural and spectral variation by applying AI-based algorithms to interpret satellite data. The ground-breaking idea is to combine physical and AI models, which has been identified as a significant scientific challenge in the forthcoming years. Our interdisciplinary consortium brings together experts of digital twins and remote sensing (VTT), forest sciences and statistical analyses (Natural Resource Institute Finland), and artificial intelligence and vegetation spectroscopy (Aalto university).
| Acronym | ARTISDIG |
|---|---|
| Status | Finished |
| Effective start/end date | 1/01/22 → 31/12/24 |
Collaborative partners
- VTT Technical Research Centre of Finland (lead)
- Aalto University
- Natural Resources Institute Finland (Luke)
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
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SDG 2 Zero Hunger
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SDG 12 Responsible Consumption and Production
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SDG 15 Life on Land
Research output
- 5 Article
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Hybrid regression method to predict forest variables from Earth observation data in boreal forests
Halme, E. & Mõttus, M., 9 Feb 2025, In: European Journal of Remote Sensing. 58, 1, 21 p., 2462032.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile36 Downloads (Pure) -
Illumination correction for close-range hyperspectral images using spectral invariants and random forest regression
Ihalainen, O., Sandmann, T., Rascher, U. & Mõttus, M., 15 Dec 2024, In: Remote Sensing of Environment. 315, 114467.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile10 Link opens in a new tab Citations (Scopus)174 Downloads (Pure) -
Seasonal and vertical variation in canopy structure and leaf spectral properties determine the canopy reflectance of a rice field
Liu, W., Mõttus, M., Gastellu-Etchegorry, J. P., Fang, H. & Atherton, J., 15 Aug 2024, In: Agricultural and Forest Meteorology. 355, 110132.Research output: Contribution to journal › Article › Scientific › peer-review
Open Access15 Link opens in a new tab Citations (Scopus)