应国际泥沙研究培训中心邀请，美国康涅狄格大学助理教授Jason Parent博士将于2017年1月11日（周三）来我院进行学术交流并作专题报告，介绍激光雷达和在林学研究中的探索应用。欢迎院内外相关研究人员和研究生参加。

        报告题目：Exploring LiDAR and its Applications in Forest Research

        报告人：Jason R Parent

        报告时间：2017年1月11日周三上午10:00

        报告地点：中国水科院泥沙中心401会议室

        联系人：屈丽琴

        联系方式：6416  13910699628  liqin.qu@iwhr.com

        报告人简历：

　　Dr. Jason Parent is an assistant research professor in the Department of Natural Resources and the Environment at College of Agriculture, Health, and Natural Resources (CAHNR), in the University of Connecticut(Uconn). He specializes in the application of remote sensing and geospatial technologies to address problems involving natural resources. He is affiliated with UConn’s Eversource Energy Center and his research focuses on developing improved methods for monitoring and managing vegetation along power lines. Dr. Parent earned a Ph.D. and an M.S., from UConn, in Natural Resources with concentrations in Earth Resources Information Systems and Landscape Ecology. 　　

        报告内容简介：

        Light Detection and Ranging (LiDAR) technology allows the 3D structure of the environment to be measured and analyzed. The applications of LiDAR data are numerous and include assessing forest structure, modeling terrain and flood risk, and mapping infrastructure. Most publicly available LiDAR data tends to be collected for the purposes of terrain modeling and consequently are collected during leaf-off conditions and with relatively low spatial resolution. The ability to use terrain-optimized LiDAR for forest research is highly desirable because the high cost of acquiring LiDAR data is often prohibitive for many agencies and universities. Our research shows that these leaf-off, low-resolution datasets can be effective for measuring forest canopy height and density in the temperate deciduous forests of the northeastern United States. This presentation will focus on our evaluation of the use of terrain-optimized LiDAR for measuring forest canopy height and density as well as discuss the varying characteristics of LiDAR datasets.