Integration of the Cropland Data Layer Based Automatic Stratification Method into the Traditional Area Frame Construction Process

  • Claire Glendening Boryan United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)
  • Zhengwei Yang United States Department of Agriculture, National Agricultural Statistics Service
Keywords: Area sampling frame (ASF), automated stratification, cropland data layer (CDL), cultivated layer, land cover-based stratification

Abstract

A new automatic stratification method utilizing United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) geospatial Cropland Data Layers (CDLs) was recently implemented in NASS operations. Recent research findings indicated that using the automated stratification method significantly improved Area Sampling Frame (ASF) stratification accuracies in intensively cropped areas (>15% cultivation) and overall stratification accuracies when compared to traditional stratification based on visual analysis of aerial photography or satellite data , while reducing the cost of ASF construction (Boryan et al., 2014). Though the new automated stratification method has improved stratification efficiency, objectivity, accuracy in the intensively cropped areas it inherits the CDL classification errors and has lower accuracies in low or non-agricultural areas. This implies that the automated stratification process is not a perfect solution to directly replace the NASS traditional stratification method for ASF construction operationally. This paper describes a hybrid approach: an operational ASF construction process that integrates the automated stratification results with ASF editing/review methods. New 2014 - 2015 NASS ASFs for South Dakota, Oklahoma, Arizona, New Mexico, Georgia, Alabama and North Carolina were successfully built using the new integrated operational process. The seven updated ASFs delivered significant improvements in objectivity, operational efficiency, and frame accuracy, based on 2014 and 2015 June Area Survey (JAS) reported data.

Author Biographies

Claire Glendening Boryan, United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)
Claire G. Boryan received a BA from the University of Virginia, a MS in Geographic and Cartographic Science and Ph.D. in Earth Systems and GeoInformation Sciences from George Mason University. She is a Geographer and Head of the Geospatial Science and Survey Section in the Research and Development Division of the USDA/National Agricultural Statistics Service (NASS). Ms. Boryan is a member of the American Society of Photogrammetry and Remote Sensing. She was a recipient of the 2011 USDA Secretary’s Honor Award for Excellence. Her research interests include remote sensing methods, geographic information science and applied research using geospatial data to improve agricultural statistics.
Zhengwei Yang, United States Department of Agriculture, National Agricultural Statistics Service
Zhengwei Yang (M’02–) received a bachelor's degree in electrical engineering from Shanghai Science and Technology University in 1982, a Master degree in systems engineering from Shanghai Jiaotong University in 1985, and a Ph.D. in electrical engineering from Drexel University 1997. Dr. Yang is currently an IT Specialist with Research and Development Division, USDA National Agricultural Statistics Service, Washington DC, and an affiliate faculty with Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA. Dr. Yang is a member of American Geophyscial Union and American Society of Photogrametry and Remote Sensing. He was a recipient of the 2011 USDA Secretary’s Honor Award for Excellence. Dr. Yang's research interests include remote sensing methods and GIS technology and their application in agriculture. His work spans conceptual, theoretical and application research as well as system development.
Published
2017-10-20
How to Cite
Boryan, C. G., & Yang, Z. (2017). Integration of the Cropland Data Layer Based Automatic Stratification Method into the Traditional Area Frame Construction Process. Survey Research Methods, 11(3), 289-306. https://doi.org/10.18148/srm/2017.v11i3.6725