A New Automatic Stratification Method for U.S. Agricultural Area Sampling Frame Construction Based on the Cropland Data Layer

Abstract

This paper, for the first time, proposes to apply USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) geospatial data for stratifying U.S. agricultural land. A new automated method is proposed 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 NASS CDLs are 30-56.0 m raster-formatted, georeferenced, cropland cover classifications derived from satellite data. The CDL stratification experiment was successfully conducted for Oklahoma, Ohio, Virginia, Georgia, and Arizona. The stratification accuracies of the traditional (visual interpretation) and new automated CDL stratification methods were compared based on 2010 June Area Survey data. Experimental results indicated that the CDL stratification method achieved higher accuracies in the intensively cropped areas, while the traditional method achieved higher accuracies in low or nonagricultural areas. The differences in the accuracies were statistically significant at a 95% confidence level. It was found that using multiyear composite, CDL-based cultivated layers did not improve stratification accuracies as compared to the results of single-year CDL data. Two applications of the CDL-automated stratification method in official USDA NASS operations are described. The novelty of the proposed method was using geospatial CDL data to objectively and automatically compute percent cultivation of the ASF PSUs as compared to the traditional method that subjectively determines percent cultivation using visual interpretation of satellite data. This proposed new CDL-based process improved efficiency, objectivity, and accuracy as compared to the traditional stratification method.Index Terms-Area sampling frame (ASF), automated stratification, cropland data layer (CDL), cultivated data layer, land cover-based stratification.

Post stratification assessment of the NASS automated stratification method based on the Cropland Data Layer

“…Experimental results indicate that the CDL automated stratification (AS) method achieved higher accuracies in intensively cropped areas while the traditional method (visual interpretation) achieved higher accuracies in low agricultural areas. Accuracy differences were statistically significant at a 95% confidence level [4].…”

“…Area Sampling Frames (ASFs) are the foundation of the agricultural statistics program of the USDA National Agricultural Statistics Service (NASS) and many other statistical survey programs around the world [1], [2], [3]. Research was recently conducted to develop and assess an automated method to stratify the NASS ASFs by calculating percent cultivation at the Primary Sampling Unit (PSU) level based on geospatial NASS Cropland Data Layers (CDL) [4]. The NASS CDLs are 30-56.0 meter raster-formatted, georeferenced, cropland cover classifications derived from satellite data [5].…”

Are avian population targets achievable through programs that restore habitat on private‐lands?

“…To extrapolate to unsampled fields enrolled in CREP, we quantified the proportion of these variables for all CREP fields within our sampling CP categories using polypolyisect function in the Geospatial Modelling Environment (Beyer ) in ArcGIS (ESRI ), using digital orthophotographs (30 m resolution), data from the 2014 National Agricultural Service Cropland Data Layer (NASS, CDL: Boryan et al. ) and a 2012 NRCS CRP Data Layer. Unsampled patches included 79,578 ha among our four CP types: CP22 (18% of enrolled CREP fields), CP23 (28% of enrolled CREP fields), CP3A (3% of enrolled CREP fields), and CP4D (30% of enrolled CREP fields).…”

US geospatial crop frequency data layers

“…Single year and multiyear CDL data were used in NASS area sampling frame (ASF) construction to automatically and objectively stratify land cover in the U.S. based on percent cultivation. The automated CDL based stratification significantly improved stratification accuracies statistically at a 95 percent confidence level in intensively cropped areas as compared with the visual interpretation based traditional stratification method [9] [10], [11]. The proposed automated stratification methodology has been implemented into USDA NASS Area Frame operational procedures.…”