Yujie Liu | Remote Sensing | Best Researcher Award

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Dr. Yujie Liu | Remote Sensing | Best Researcher Award

Dr. YujieLiu at Yinglin Branch Yunnan Institute of Forest Inventory and Planning, China

Liu Yujie, born in November 1992 in Wuxi, Jiangsu, is a dedicated researcher in forestry and entomology. A member of the Communist Party of China, he specializes in forestry quantitative remote sensing and pest control. With a strong academic background and extensive research experience, he has contributed significantly to understanding forest health and pest management. His work combines theoretical expertise with practical applications, aiding in environmental conservation efforts. He has served in both research and administrative roles, demonstrating a well-rounded career in forestry and agricultural policy. His contributions have been recognized in multiple scientific publications.

Publication Profile

Scopus

Academic Background

Liu Yujie pursued his undergraduate studies at Nanjing Forestry University (2011-2015), specializing in Forestry. He then continued his postgraduate education at Beijing Forestry University (2015-2022), where he enrolled in a combined Master’s and Doctoral program in Forest Protection under the guidance of Professor Luo Youqing. His academic journey focused on forestry quantitative remote sensing, entomology, and integrated pest control. His studies have provided him with in-depth knowledge of forest ecosystems, pest behavior, and advanced remote sensing techniques, equipping him to address critical challenges in forestry conservation and management.

Professional Background

Liu Yujie has gained extensive experience in both research and administrative roles. From October 2022 to January 2024, he worked at the Ningbo Haishu District Agriculture and Rural Bureau Office, focusing on agricultural comprehensive management and policy implementation. Following this, from January 2024 to July 2024, he served in the Ningbo Haishu District People’s Government Office, handling secretarial and governmental administrative duties. His experience bridges scientific research with practical policy applications, allowing him to contribute to forestry conservation, pest control strategies, and environmental sustainability. His career reflects a blend of academic excellence and hands-on governance experience.

Awards and Honors

Liu Yujie has received several accolades for his contributions to forestry research and pest control strategies. His studies on forest health assessment, remote sensing, and pest management have been published in high-impact journals such as the Chinese Journal of Applied Entomology and the Journal of Environmental Entomology. He has been recognized for his innovative approaches in pine wood nematode disease control, ash borer prevention, and volatile compound analysis in forestry. His work has earned him recognition in forestry research communities, establishing him as a promising researcher in forest protection and quantitative remote sensing applications.

Research Focus

Liu Yujie specializes in forestry quantitative remote sensing, pest management, and entomology. His research explores forest health monitoring using advanced remote sensing technologies and develops effective pest control strategies for protecting forest ecosystems. His studies on pine wood nematode disease, insect behavior, and chemical ecology contribute to sustainable forestry management. His work integrates data-driven approaches with field experiments, enhancing early detection and mitigation of forest pest outbreaks. By combining innovative technologies with traditional forestry principles, he aims to improve forest conservation efforts and address ecological challenges posed by invasive pests.

Publication Top Notes

📖 Acoustic detection of the wood borer, Semanotus bifasciatus, as an early monitoring technology

Year: 2022 📅 | Cited by: 5 🔍 | Journal: Pest Management Science

Conclusion

Liu Yujie is a highly qualified researcher specializing in forestry quantitative remote sensing, pest control, and entomology, with a Master’s and PhD from Beijing Forestry University. His strong academic background, impactful research publications (CSCD, CSSCI indexed), and practical experience in both scientific studies and governmental roles make him a well-rounded candidate for the Best Researcher Award. His work on forest health assessment, pest monitoring, and remote sensing applications has significant relevance to contemporary environmental challenges. While some administrative responsibilities may have momentarily shifted his research focus, his contributions to forestry science, innovative research, and policy applications solidify his strong candidacy for this prestigious award.

 

 

Han Gao | Radar Remote Sensing | Best Researcher Award

Published on by Academic Awards

Dr. Han Gao | Radar Remote Sensing | Best Researcher Award

Dr. Han Gao, China University of Petroleum (East China),China

Dr. Han Gao is an accomplished researcher at the College of Oceanography and Space Informatics, China University of Petroleum (East China). Specializing in radar remote sensing and microwave vision theory, his expertise extends to time series PolSAR data processing and remote sensing monitoring of flood disasters. Proficient in MATLAB, Python, and C++, he has developed innovative methods in crop classification and flood disaster monitoring, with significant applications in various Chinese provinces. Dr. Gao’s work has been published in top-tier journals like IEEE TGRS and RSE, earning substantial citations and recognition. 📡💻🛰️

Publication Profile

Orcid

Google Scholar

Education

Dr. Han Gao pursued his academic journey at Central South University, where he obtained a Ph.D. in Photogrammetry and Remote Sensing from the College of Geosciences and Info-physics in June 2022. Prior to his doctorate, he completed a Master’s degree in Geomatics Engineering in June 2018, following his Bachelor’s degree in the same field in June 2015. His extensive education has laid a solid foundation for his research in remote sensing and geosciences. 📡💻🛰️

 

Research Focus 🌍🔬

Dr. Han Gao’s research primarily focuses on advanced remote sensing techniques, particularly in radar remote sensing and microwave vision theory. He has developed innovative methods for crop classification using time-series dual-polarization SAR datasets, integrating data from various sources like GF-3 PolSAR and Sentinel-2A. His work extends to flood disaster monitoring and the development of adaptive filters for PolSAR data. Dr. Gao’s research also includes forest height estimation and phase optimization for DS-InSAR. His significant contributions are published in high-impact journals, highlighting his expertise in agricultural and ecological remote sensing. 🌾📡🌳

 

Publication Top Notes

  1. A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets – H Gao, C Wang, G Wang, H Fu, J Zhu – Remote Sensing of Environment 264, 112628 – 32 citations – 2021 📅📈
  2. A new crop classification method based on the time-varying feature curves of time series dual-polarization Sentinel-1 data sets – H Gao, C Wang, G Wang, Q Li, J Zhu – IEEE Geoscience and Remote Sensing Letters 17 (7), 1183-1187 – 30 citations – 2019 📅📈
  3. A crop classification method integrating GF-3 PolSAR and Sentinel-2A optical data in the Dongting Lake Basin – H Gao, C Wang, G Wang, J Zhu, Y Tang, P Shen, Z Zhu – Sensors 18 (9), 3139 – 28 citations – 2018 📅📈
  4. An adaptive nonlocal mean filter for PolSAR data with shape-adaptive patches matching – P Shen, C Wang, H Gao, J Zhu – Sensors 18 (7), 2215 – 21 citations – 2018 📅📈
  5. Forest height estimation using PolInSAR optimal normal matrix constraint and cross-iteration method – C Wu, C Wang, P Shen, J Zhu, H Fu, H Gao – IEEE Geoscience and Remote Sensing Letters 16 (8), 1245-1249 – 16 citations – 2019 📅📈
  6. TSPol-ASLIC: Adaptive superpixel generation with local iterative clustering for time-series quad-and dual-polarization SAR data – H Gao, C Wang, D Xiang, J Ye, G Wang – IEEE Transactions on Geoscience and Remote Sensing 60, 1-15 – 13 citations – 2021 📅📈
  7. A phase optimization method for DS-InSAR based on SKP decomposition from quad-polarized data – G Wang, B Xu, Z Li, H Fu, H Gao, J Wan, C Wang – IEEE Geoscience and Remote Sensing Letters 19, 1-5 – 13 citations – 2021 📅📈
  8. Fusion of spatially heterogeneous GNSS and InSAR deformation data using a multiresolution segmentation algorithm and its application in the inversion of slip distribution – H Yan, W Dai, H Liu, H Gao, WR Neely, W Xu – Remote Sensing 14 (14), 3293 – 5 citations – 2022 📅📈