报告时间：2019年1月16日 10:00-11:00
报告地点：明德主楼1016会议室
报告主题：The Use of Machine Learning Methods and Remote Sensing Data to improve the US National Resources Inventory Survey
报告摘要：
The National Resources Inventory (NRI) Survey is the largest annual longitudinal survey of soil, water, and related environmental resources in the US designed to assess conditions and trends on non-federal US lands. It was designed to provide accurate national and state estimates. One challenge in NRI is that there is a multi-year lag in publishing the NRI data due to resource constraints on data collection. We also receive requests from local stakeholders to provide data at county and small watershed level. In order to provide more timely estimates at smaller spatial scales, it is necessary to integrate alternative big data sources such as administrative data and satellite remote sensing data with the survey data in our estimation. In this talk we give a brief introduction to the NRI, and share our experience using satellite data and machine learning methods to improve NRI estimation. New spatial-temporal functional imputation method for satellite data gap-filling and machine learning methods for satellite data based land-cover classification will be introduced, which are useful for the NRI small area estimation and forecasting.
报告人简介:
Zhengyuan Zhu is Professor of Statistics at Iowa State University and the director of the Center for Survey Statistics and Methodology. He received his Ph.D. degree in Statistics from the University of Chicago and was an Assistant Professor of Statistics at the University of North Carolina at Chapel Hill before joining Iowa State University in 2009. He has expertise in spatial statistics, survey statistics, spatial sampling design, and time series analysis, and is interested in applications in environmental statistics, remote sensing, natural resource surveys, and agricultural statistics. He is the Principle Investigator and co-Principle Investigator for a number of national large scale longitudinal surveys including the US National Resource Inventory survey, the US BLM-Managed Lands survey, and the surveys for the Conservation Effects Assessment Project.