A new land cover classification based stratification method for area sampling frame construction
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This paper proposes a new automated USDA National
Agricultural Statistics Service (NASS) Cropland Data Layer
(CDL) based method for stratifying U.S. land cover. The
proposed method is used to stratify the NASS state level Area
Sampling Frames (ASFs) by automatically calculating percent
cultivation at the Primary Sampling Unit (PSU) level based on
the CDL data. The CDL based stratification experiment was
successfully conducted for Oklahoma, Ohio, Virginia, Georgia,
and Arizona. The stratification accuracies of the traditional and
new automated CDL stratification methods were compared
based on 2010 June Area Survey (JAS) data. Experimental
results indicated that the CDL based stratification method
achieved higher accuracies in the intensively cropped areas while
the traditional method achieved higher accuracies in low or non
agricultural areas. The differences in the accuracies were
statistically significant at a 95% confidence level. It is concluded
that the CDL based stratification method will improve efficiency
and reduce cost in NASS ASF construction, and improve the
precision of NASS JAS estimates.