AI-Based Digital Twin Framework for Biodiversity Conservation and Ecosystem Monitoring
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DOI:
https://doi.org/10.67228/30715636/IJETMR-2025PI3Q8KPublished 01-04-2025
Artificial Intelligence, Digital Twin, Biodiversity Conservation, Ecosystem Monitoring, Environmental Intelligence, Remote Sensing, Internet of Things, Explainable AI, Ecological Forecasting, Sustainable Environmental Management Issue
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ArticlesHow to Cite
[1]O.-J. Dahl and K. Nygaard, “AI-Based Digital Twin Framework for Biodiversity Conservation and Ecosystem Monitoring”, IJETMR, vol. 8, no. 1, pp. 01–17, Jan. 2025, doi: 10.67228/30715636/IJETMR-2025PI3Q8K.Abstract
Biodiversity conservation faces increasing challenges from habitat loss, climate change, deforestation, urbanization, pollution, and declining ecosystems. Conventional monitoring methods, including field surveys and satellite observations, often suffer from limited temporal coverage, high costs, and inadequate predictive capabilities. Recent advances in Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and Digital Twin technologies enable intelligent, real-time ecosystem monitoring and conservation planning. This study presents an AI-Based Digital Twin Framework for Biodiversity Conservation and Ecosystem Monitoring that integrates environmental sensing, AI analytics, remote sensing, and virtual ecosystem modeling into a unified platform. The framework comprises six layers: Environmental Data Acquisition, Data Integration and Preprocessing, AI Analytics, Digital Twin Simulation, Decision Intelligence, and Conservation Management. It supports continuous monitoring of biodiversity, habitat quality, species distribution, vegetation dynamics, climate conditions, and ecosystem health. The framework employs CNNs for species identification, LSTM and GNN models for habitat classification, ensemble learning for ecosystem health prediction, reinforcement learning for conservation optimization, and anomaly detection for identifying ecological disturbances. By synchronizing real-time environmental data with virtual ecosystem simulations, the Digital Twin enables biodiversity risk assessment, predictive conservation planning, and evidence-based decision-making. Explainable AI further enhances transparency and regulatory compliance. Overall, the proposed framework improves scalability, interoperability, predictive accuracy, and computational efficiency over traditional biodiversity monitoring approaches. It provides a robust foundation for intelligent ecosystem management, biodiversity conservation, climate resilience, and sustainable environmental governance.
References
[1] M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” in Transdisciplinary Perspectives on Complex Systems, Cham, Switzerland: Springer, 2017, pp. 85–113.
[2] F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, “Digital Twin in Industry: State-of-the-Art,” IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, Apr. 2019.
[3] Q. Qi and F. Tao, “Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison,” IEEE Access, vol. 6, pp. 3585–3593, 2018.
[4] J. A. Jiménez-Berni, P. J. Zarco-Tejada, L. Suárez, and E. Fereres, “Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 3, pp. 722–738, Mar. 2009.
[5] G. M. Foody, “Status of Land Cover Classification Accuracy Assessment,” Remote Sensing of Environment, vol. 80, no. 1, pp. 185–201, Apr. 2002.
[6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2012, pp. 1097–1105.
[7] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
[8] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
[9] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.
[10] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You? Explaining the Predictions of Any Classifier,” in Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
[11] S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 4765–4774.
[12] D. C. Schmeller, N. Pettorelli, and K. B. Kissling, “Towards a Global Biodiversity Monitoring System,” Nature Ecology & Evolution, vol. 1, no. 8, pp. 1–4, 2017.
[13] M. A. Wulder, J. C. White, T. R. Loveland, C. E. Woodcock, A. S. Belward, W. B. Cohen, E. A. Fosnight, J. G. Shaw, J. Masek, D. P. Roy, and C. J. Schaaf, “The Global Landsat Archive: Status, Consolidation, and Direction,” Remote Sensing of Environment, vol. 185, pp. 271–283, Nov. 2016.
[14] T. Schmidhuber, J. Horne, and D. L. Peterson, “IoT-Based Environmental Monitoring Systems: A Review of Technologies and Applications,” Sensors, vol. 19, no. 18, Art. no. 3948, 2019.
[15] N. Pettorelli, W. F. Laurance, T. G. O'Brien, M. Wegmann, H. Nagendra, and W. Turner, “Satellite Remote Sensing for Applied Ecologists: Opportunities and Challenges,” Journal of Applied Ecology, vol. 51, no. 4, pp. 839–848, Aug. 2014.
[16] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.
[17] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845
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How to Cite
[1]O.-J. Dahl and K. Nygaard, “AI-Based Digital Twin Framework for Biodiversity Conservation and Ecosystem Monitoring”, IJETMR, vol. 8, no. 1, pp. 01–17, Jan. 2025, doi: 10.67228/30715636/IJETMR-2025PI3Q8K.