Huang Wenli

Supervisor of Doctorate Candidates  
Supervisor of Master's Candidates

School/Department:School of Resource and Environmental Sciences

Administrative Position:Associate Professor

Education Level:With Certificate of Graduation for Doctorate Study

Alma Mater:Wuhan University

Discipline:Cartography and Geoinformation Engineering
Cartography and Geography Information Systems

Honors and Titles

2022   Gold Award of the 12th "Challenge Cup · Bank of China" Undergraduate Entrepreneurship Plan Competition in Hubei Province

2023   Outstanding Instructor of Wuhan University 2022 Undergraduate Extracurricular Academic Science and Technology Innovation and Entrepreneurship Competition

2022   Gold Award of Innovation Group of China Youth Innovation and Entrepreneurship Competition (Digital Economy Special) (Instructor)

2014   Ann G. Wylie Dissertation Fellowship

2012   Goldhabor International Travel Grant

2006   Excellent undergraduate of Wuhan University


Enrollment Information

Hits:

Department:资源与环境科学学院

Specialized Courses:地图学与地理信息系统

Master-Research direction:空间信息的地学应用、空间信息可视化与分析

Year of Admission:2023

Admission Type:Master Degree Candidate

Discipline:Geography

Expected Number of Students to Be Admitted:1

Profile

Wenli Huang is an Associate Professor at Wuhan University. From 2015 to 2018, she worked as a Post-doc Research Associate with the Department of Geographical Sciences at the University of Maryland, College Park where she completed her Ph.D. in 2015. She received a B.S. from Wuhan University in 2006, and M.S. from the Beijing Normal University in 2009. 

Her current areas of interest cover important land cover components, including forest and water.

Wenli's primary research is aimed at creating a better understanding of terrestrial forest ecosystems. She is interested in measuring the tree canopy cover and carbon stocks stored in forest areas using lidar, optical/radar remote sensing, and field data. Also, she is actively involved in developing automated approaches for monitoring the inundated area using optical and radar remote sensing data.