李焕，博士，教授。南京大学地理学博士（美国伊利诺伊大学香槟分校联合培养），香港城市大学博士后，国家社科基金重点项目主持专家，浙江省杰青，浙江省高校领军人才（青年优秀人才），执笔《科技工作者建议》3份、《浙江社科要报》1份，获发明专利3项，表SSCI/SCI论文30余篇，其中ESI 1% 高被引论文3篇，出版专著3部，研发软件著作权2项。

Li Huan, Ph.D., Professor

Ph.D. in Geography from Nanjing University (jointly trained at the University of Illinois at Urbana-Champaign, USA), Postdoctoral Fellow at City University of Hong Kong, Lead Principal Investigator of National Social Science Fund Key Projects, Distinguished Young Scholar of Zhejiang Province, and Leading Talent in Zhejiang Higher Education Institutions (Outstanding Youth Talent). Authored 3 Science and Technology Worker Recommendations and 1 Zhejiang Social Science Report, holds 3 invention patents, published over 30 SSCI/SCI-indexed papers, including 3 ESI 1% highly cited papers, authored 3 monographs, and developed 2 software copyrights.

研究聚焦于城市韧性、土地利用/覆被变化（LUCC）和农业可持续发展，旨在通过多学科交叉方法揭示人类-环境系统的复杂关系及其对可持续发展的影响。在城市韧性研究中，我探索城市系统应对气候变化和社会经济冲击的能力；在LUCC方面，我分析土地利用变化的驱动机制及其对生态系统服务和人类福祉的影响；在农业可持续发展领域，我研究农业土地利用优化及其对粮食安全和生态保护的作用。在方法上，我擅长运用地理信息系统（GIS）、地理人工智能（GeoAI）、机器学习（如随机森林、支持向量机）和Python编程，通过空间分析、遥感数据解译和精细化建模，揭示城市韧性、土地利用变化和农业可持续发展之间的内在联系。未来，我将深化GeoAI和机器学习在多尺度研究中的应用，结合复杂系统建模和大数据分析，为可持续发展提供科学依据和政策支持。

Research focuses on urban resilience, land use/cover change (LUCC), and agricultural sustainable development, aiming to uncover the complex interactions within human-environment systems and their implications for sustainable development through interdisciplinary approaches. In urban resilience research, I investigate the capacity of urban systems to adapt to climate change and socio-economic shocks. In LUCC studies, I analyze the driving mechanisms behind land use changes and their impacts on ecosystem services and human well-being. In the field of agricultural sustainable development, I explore strategies for optimizing agricultural land use to balance food security and ecological conservation.

Methodologically, I specialize in employing Geographic Information Systems (GIS), GeoAI, machine learning techniques (e.g., random forest, support vector machines), and Python programming to conduct spatial analysis, remote sensing data interpretation, and refined modeling. These approaches enable me to elucidate the intricate relationships between urban resilience, land use changes, and agricultural sustainability. Moving forward, I aim to further integrate GeoAI and machine learning into multi-scale research, combining complex system modeling and big data analytics to provide robust scientific evidence and policy recommendations for sustainable development