Abstract
The modelling of flora and fauna is vital for understanding and digitally representing our environment, yet their dynamic modelling in digital twins lags behind human-made inventions like manufacturing and the built environment. The interdisciplinary nature of this research complicates tracking advancements, and no comprehensive overview exists. This Systematic Literature Review (SLR), using the PRISMA method, addresses this gap by analysing studies on dynamic modelling of flora and fauna in digital twins and 3D city models. It covers descriptive metrics and qualitative aspects, identifying key research fields, directions, users, and developers. Additionally, this SLR details on digital twin data, modelling techniques, actuators, user experience with human-computer interaction, and ethical considerations. The findings highlight that the digital twin concept is being increasingly applied to the dynamical modelling of flora and fauna. Moreover, the broad relevance of this research is demonstrated across various fields including ecology, forestry, urban studies, and agriculture, where diverse methods and technologies are used, though progress remains uneven. Currently, precision agriculture is leading the way in automated, bidirectional synchronisation between digital twins and their physical counterparts. Complementing traditional modelling techniques with AI and machine learning where appropriate, expands modelling capabilities. Meanwhile, multimodal interfaces enhance the immersive user experience. Despite these advances, challenges persist in data availability, foundational knowledge, complex interaction modelling, standardisation and transferability, underscoring the need for continued research. Digital twins for the biotic environment show promise in supporting United Nations Sustainable Development Goals 2, 11, 13, 14, and 15. This overview supports researchers and practitioners in developing digital twin applications which include flora and fauna.
Original language | English |
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Article number | 111091 |
Pages (from-to) | 1-19 |
Number of pages | 19 |
Journal | Ecological Modelling |
Volume | 504 |
DOIs | |
Publication status | Published - 16 Mar 2025 |
MoE publication type | A2 Review article, Literature review, Systematic review |
Keywords
- digital twin
- dynamic modelling
- ecology
- flora and fauna
- natural environment
- systematic literature review
- Natural environment
- Digital twin
- Dynamic modelling
- Ecology
- Flora and fauna
- Systematic literature review