题 目：Multi-temporal remote sensing data analysis for agricultural applications
报告人：Yang Shao 助理教授
主持人：宋从和 教授
时 间：2015年6月17日（周三）9:00-11:00
地 点：闵行校区二教312室
报告人简介：Yang SHAO（邵阳）于2007在北卡罗来纳大学教堂山分校（UNC-Chapel Hill）获地理学博士学位，2007-2011在美国环保局（EPA）做博士后研究工作，2011到弗吉尼亚理工大学（Virginia Tech.）担任助理教授。主要研究领域是地球空间技术及其在土地利用土地覆盖变化、人与环境关系、水质监测和流域评估中的应用。已在International Journal of Remote Sensing,Environmental Management,ISPRS Journal of Photogrammetry and Remote Sensing,IEEE Geoscience and Remote Sensing letters, Photogrammetric Engineering and Remote Sensing,International Journal of Applied Earth Observation and Geoinformation等国际专业学术期刊发表论文18篇。
报告简介：Multi-temporal remote sensing data analysis has been increasingly used to characterize land-cover and monitor vegetation conditions at regional and global scales. For agricultural applications, multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) data are now routinely used for crop-specific mapping, crop yield forecasting, crop stress detection, crop phenology detection, and evapotranspiration estimation (ET). Recent research has also shown that multi-sensor data fusion at Landsat spatial scale (e.g., 30m) can greatly improve the temporal frequency of observation, a critical component for many RS-based analysis and modeling of agricultural system. This presentation will draw examples from the author's experiences in cropland mapping, yield forecasting, ET estimation, and watershed modeling efforts in the Great Lakes Basin and the Midwestern region of the United States. Main challenges and opportunities in data quality, data volume, and data mining will be discussed under a multi-temporal remote sensing framework.
