The Use of Machine Learning Methods to Improve the US National Resources Inventory Survey

• Activity Number: 389 - Improving Survey Data Quality with Machine Learning Techniques
• Type: Invited
• Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Survey Research Methods Section
• Author(s): Zhengyuan Zhu*
• Companies: Iowa State University
• Keywords: classification; land cover; land use; gap filling; small area estimation; data fusion

Abstract

The National Resources Inventory (NRI) Survey is one of 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 3-year lag in publishing the NRI data due to resource constraints on data collection. We also receive strong 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 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 statistical 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.