Integration of the Cropland Data Layer Based Automatic Stratification Method into the Traditional Area Frame Construction Process
Keywords:Area sampling frame (ASF), automated stratification, cropland data layer (CDL), cultivated layer, land cover-based stratification
AbstractA 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.
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