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


Paper Publications

基于机载LiDAR和多光谱图像的建筑物震害自动识别方法

Hits:

Journal:遥感信息

Co-author: 袁小祥, 王晓青, 黄文丽,马宗晋

First Author:窦爱霞

Indexed by:Journal paper

Correspondence Author:窦爱霞

Discipline:Natural Science

Document Type:J

Translation or Not:no

Date of Publication:2012-12-08

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.