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Yuxiu Resource and Environment Frontier Forum of the College of Earth and Environmental Sciences (Lecture 6, 2022)- Researcher Feng Lian
Release time:2022-11-09 10:16:46

At the invitation of Professor Jie Yaowen and Associate Professor Gong Jie of the College of Earth and Environmental Sciences of Lanzhou University and the Key Laboratory of Western China's Ministry of Environment and Education, Researcher Feng Lian from Southern University of Science and Technology will give an academic exchange and online academic report on November 9, 2022. All teachers and students are welcome to attend!

Speaker: Researcher Feng Lian, Southern University of Science and Technology

Topic: Advances in remote sensing of lakes at the global scale

Host: Professor Gong Jie, College of Earth and Environmental Sciences, Lanzhou University

Time: November 9, 2022 (Wednesday), 19:00-21:00

Tencent Conference Number: 854-6635-3184 Conference Password: 2209

Expert Introduction:

Feng Lian is a researcher/doctoral supervisor in the School of Environmental Science and Engineering at the Southern University of Science and Technology. He is mainly engaged in the research of theory, method, and application of remote sensing of the water environment. He has been awarded the National Top Ten Thousand Program Young Talents, Young Scholar of Guangdong Zhujiang, Shenzhen High-level "National Leading Talents", National Geography Young Scientist Award, and "Global Top 2% Scientists List". He has published more than 90 SCI papers, more than half of which are published in Nature, Nature Geoscience, Nature Communications, Remote Sensing of Environment, Geophysical Research Letters, and other top journals.

Report Introduction:

Due to human activities and climate change, lakes worldwide have been facing the deterioration of water quality and wetland degradation in recent years, which seriously threaten the ecological functions of lakes. This presentation will introduce how to use multi-source satellite remote sensing data to conduct global-scale long-time series dynamic studies on key parameters such as lake water extent, lake ice cover, and lake algal blooms.

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