Next-Generation Climate-Smart Forestry: Integrating Digital Twins, AI, IoT, Remote Sensing, and Ecosystem Modelling for Resilient Forest Management
1. Cong Huy, Vietnam National University of Forestry, Student, Vietnam
2. Ha Anh Ke, Vietnam National University of Forestry, Professor, Vietnam
Climate change continues to increase forest vulnerability through rising temperatures, altered precipitation, pest outbreaks, and extreme disturbances. Recent advances in digital technologies—especially Digital Twin systems, artificial intelligence (AI), high-resolution remote sensing, and IoT—are transforming the way forests are monitored, modeled, and managed. This review integrates findings from recent research between 2023–2025, including studies on Digital Twin architectures for forestry, AI-enabled climate-smart forestry, ecosystem modeling, and resilience assessment. Evidence shows that Digital Twin models provide a real-time virtual replica of forest ecosystems by merging satellite data, LiDAR, sensor networks, AI predictions, and simulation algorithms. Climate-smart forestry practices, when combined with AI-driven analytics, optimize carbon sequestration and support adaptation strategies under multiple climate scenarios. At the same time, digital transformation frameworks in forestry emphasize the integration of blockchain for transparency, IoT for continuous monitoring, and machine learning for early detection of disturbances. High-resolution remote sensing approaches further enhance predictive capability by providing fine-grained structural and functional data for ecosystem processes. This review synthesizes these technological advancements into a unified framework and highlights research gaps related to standardization, data governance, stakeholder adoption, and model interoperability.
Climate-smart forestry Digital Twin Artificial intelligence Remote sensing Ecosystem modelling Forest resilience
The convergence of Digital Twin technology, artificial intelligence, Internet of Things (IoT), high-resolution remote sensing, and advanced ecosystem modelling marks a transformative era in forestry science. Collectively, these innovations are redefining how forests are monitored, evaluated, and managed under accelerating climate change. Digital Twin ecosystems create a real-time, dynamic representation of forests, enabling managers to simulate disturbance scenarios, optimize restoration strategies, and evaluate carbon sequestration performance. AI plays a vital role by processing complex datasets and predicting ecological responses with higher precision than traditional models. Meanwhile, IoT sensors and remote sensing platforms provide continuous, multi-scale data, offering unparalleled visibility into forest health, hydrological cycles, microclimates, and biomass fluctuations.
Climate-smart forestry—empowered by these technologies—ensures forests are managed not only for sustainability but also for resilience and adaptability. Research demonstrates that integrating digital technologies with ecological knowledge enhances decision-making capacity, reduces management uncertainty, and provides early warnings for risks such as drought, disease outbreaks, and fires. Furthermore, innovations such as blockchain-enabled transparency, species-specific thinning strategies, and high-accuracy biomass estimation strengthen governance mechanisms and improve ecosystem service valuation.
However, challenges remain. Data standardization across sensors and platforms is still limited, reducing interoperability. Implementation costs are high in developing countries, and gaps in digital infrastructure restrict real-time monitoring systems. Furthermore, ethical considerations around data privacy, indigenous land rights, and technological dependence must be addressed. Despite these barriers, the rapid advancement in AI, remote-sensing analytics, and Digital Twin frameworks suggests a promising trajectory where forestry becomes more predictive, responsive, and climate-resilient. Continued interdisciplinary collaboration is essential to fully unlock the potential of next-generation forest management systems.
1. -
The author confirms sole responsibility for all stages of the study, including design, data collection, analysis, and manuscript writing.
This study did not receive specific financial support from funding agencies in the public, commercial, or non-profit sectors.
Not applicable.
The authors have no conflicts of interest to declare.
I appreciate the assistance and expertise provided by everyone involved in this research and manuscript, and the valuable comments from peer reviewers.
Not applicable.