报告人：黄文丽  博士（马里兰大学）

　　时间：2016年12月22日10:00 

　　地点：中科院尊龙凯时 - 人生就是搏!地球所（奥运园区）B202尊龙凯时 - 人生就是搏!科学国家重点尊龙凯时 - 人生就是搏!学术报告厅

　　报告人简介：黄文丽，Research Associate，现就职于美国马里兰大学地理系，2015年8月在马里兰大学获博士学位，随后从事博士后研究。已参与多项美国国家航空航天局项目，主要负责应用激光雷达数据估算森林生物量，以及研发算法应用星载光学与雷达数据监测淹没区域变化。目前已在国际期刊发表学术文章10篇，著作章节1项、及数据产品1项 。担任IEEE Transaction on Geoscience and Remote Sensing (TGRS) 、 Geoscience and Remote Sensing Letters (GRSL)、 Remote Sensing、Selected Topics in Applied Earth Observations、Canadian Journal of Remote Sensing、International Journal of Remote Sensing and Remote Sensing Letters、Forest、Wetland等多个国际期刊评审。

　　报告简介：

　　Accurate mapping of forest aboveground biomass is critical for reducing uncertainties in carbon monitoring and accounting systems.  Currently available regional to national scale products mostly rely on two-dimensional satellite data (i.e., multispectral and radar), hence they may have low sensitivity to canopy structure and biomass over high biomass stands and heterogeneous forests at fine scale.  To this end, this presentation will introduce the efforts we made to develop a robust, replicable and scalable framework that quantifies forest structure and aboveground biomass over large areas at fine resolution using airborne LiDAR and US Forest Inventory Analysis (FIA) plot data. The field-based estimates were related to LiDAR height and volume metrics through random forests models across four ecological regions in three Mid-Atlantic States in US.  Results show that the proposed framework can produce accurate estimates of biomass at fine spatial resolution.  Although different national products converge at coarser resolutions, our map reveals more details at finer scales.  Local, high-resolution LiDAR-derived biomass maps such as ours, provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale mapping efforts, and can aid in future development of a national carbon monitoring system.