@Article{M-10401,
AUTHOR = {Huy, Cong and Ke, Ha Anh},
TITLE = {Next-Generation Climate-Smart Forestry: Integrating Digital Twins, AI, IoT, Remote Sensing, and Ecosystem Modelling for Resilient Forest Management},
JOURNAL = {Scientific Research Journal of Agriculture and Veterinary Science},
VOLUME = {3},
YEAR = {2025},
NUMBER = {2},
ARTICLE-NUMBER = {M-10401},
URL = {https://isrdo.org/journal/SRJAV/currentissue/next-generation-climate-smart-forestry-integrating-digital-twins-ai-iot-remote-sensing-and-ecosystem-modelling-for-resilient-forest-management},
ISSN = {2584-1416},
ABSTRACT = {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.},
DOI = {}
}