Remote Sensing and Visualization Methods - Zhang, Huaiqing; Suárez-Minguez, Juan; Chen, Qi; Wang, Yunsheng; Tharib, Safa; Sun, Hua; Jing, Weipeng; Huang, Huaguo; Yun, Ting; Wang, Meili (MDPI - Multidisciplinary Digital Publishing Institute, 2026)
The aim of this reprint is to provide a comprehensive exploration of the latest advancements in forestry through the integration of emerging technologies. This publication delves into several key areas, including the application of remote sensing and visualization techniques, the use of digital twins and the metaverse, and the role of artificial intelligence in enhancing forest management. Each of these aspects plays a crucial role in shaping the future of smart forestry. First, remote sensing technologies have revolutionized our ability to create high-precision 3D models of forest ecosystems. These detailed models of terrain, trees, and entire environments provide unprecedented insights into forest structure and dynamics, which are essential for effective monitoring and management. Second, by leveraging the power of digital twin technology and the metaverse, this reprint explores how virtual and real-world environments can be seamlessly integrated. Third, artificial intelligence (AI) algorithms play a pivotal role in enhancing the efficiency and accessibility of forestry processes. AI-driven analytics can process vast amounts of data quickly, providing actionable insights that support decision-making in forest management. This reprint shows how AI is transforming forestry by improving the speed and accuracy of data analysis.