A new land cover classification based stratification 

method for area sampling frame construction 

Claire G. Boryan, Zhengwei Yang 

USDA National Agricultural Statistics Service 

3521 Old Lee Highway, Room 305, Fairfax, VA 22030, U.S.A 

Email: Claire.Boryan@nass.usda.gov 

Abstract—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. 

Keywords-stratification; land cover; area sampling frame; 

primary sampling unit; CDL 

I. INTRODUCTION

Area Sampling Frames (ASFs) are the foundation of the 

agricultural statistics program of the National Agricultural 

Statistics Service (NASS) and many other statistical survey 

programs. ASFs have been used since 1954 as a primary tool 

for conducting surveys to gather information on crop acreage 

and other agricultural information. They are considered “the 

backbone to the agricultural statistics program of the National 

Agricultural Statistics Service (NASS)” [3]. NASS’ primary 

area frame survey is the June Area Survey (JAS) in which 

11,000, one square mile sample segments, are visited by 

enumerators each year at the beginning of the growing season 

to collect crop type and acreage information. Estimates of crop 

acreage and livestock inventories are based on the data 

collected during the JAS. The NASS ASFs are based on the 

stratification of U.S. land cover into homogeneous groups or 

strata based on percent cultivation. This stratification of land 

cover has been conducted using visual interpretation of aerial 

or satellite data for the past fifty eight years. The precision and 

accuracy of the survey statistics are dependent on the 

techniques used to construct and sample the NASS Area 

Frame. 

NASS, also, has a remote sensing acreage estimation 

program in which satellite data acquired throughout the 

growing season are utilized as inputs to produce crop specific 

land cover classifications from which independent acreage 

estimates are generated. These state level agricultural land 

cover classifications are known as Cropland Data Layer (CDL) 

products. The NASS CDLs are 30-56.0 meter raster-formatted, 

geo-referenced, crop-specific land cover classifications. 

Historically, CDLs were produced, beginning in 1997, for 

major crop producing states in the Midwest of the United 

States to provide acreage estimates to the NASS Agricultural 

Statistics Board (ASB) and Field Offices (FOs). Over the 

years, the program has expanded to include all 48 US 

conterminous states for years 2008-2011. Total crop mapping 

accuracies for historic CDLs ranged from 85% to 95% for the 

major crop categories. Boryan et al., [1] provide greater detail 

regarding CDL production. The CDL data are publically 

available from NASS’ online geospatial application - 

CropScape [2]. 

Currently, NASS’ Research and Development Division is 

working to improve the efficiency, reduce the cost and improve 

the precision of the estimates generated from the June Area 

Survey. Toward this goal, a new automated method has been 

developed to objectively, consistently, and rapidly stratify U.S. 

land cover, based on percent cultivation, of the Area Sampling 

Frame (ASF) Primary Sampling Units (PSUs) using the 2010 

CDL products. 

This paper proposes a new automated, NASS CDL based 

stratification method and makes a performance comparison 

between the NASS traditional method which is based on visual 

interpretation and the new automated CDL stratification 

method. The effectiveness of the traditional and CDL based 

methods in determining percent cultivation, at the Area Frame 

Primary Sampling Unit (PSU) level, were assessed using in situ 

validation data collected at the segment level as part of the 

2010 JAS. The goal of this investigation was to determine the 

utility of the automated CDL based method for use in the 

stratification of U.S. land cover and potentially in ASF 

construction [3] 

II. BACKGROUND

A. NASS Area Sampling Frames 

The NASS ASFs are based on a stratification of land cover 

in the U.S. by percent cultivated cropland, and are the 

statistical foundation for providing estimates with complete 

coverage of U.S. agriculture. The Area Frame program is 
conducted in 49 states using approximately 11,000 one square 

mile segments made up of approximately 41,000 individual 

farms. Selected farms are visited each year by enumerators, as 

part of the JAS, to identify the planting intentions for all 

agricultural land within the segments, including planted 

acreage and acreage intended for harvest. Acreage estimates for 

major commodities such as corn, soybeans, winter wheat, 

spring wheat, durum wheat and cotton are generated from the 

JAS at the state and national level. 

The primary use of the ASF within NASS is as the 

foundation of the JAS. The ASF is also used to measure the 

incompleteness of NASS list frame, provide ground truth for 

the NASS Cropland Data Layer (CDL) program to generate 

independent acreage estimates, and for additional surveys such 

as NASS’ objective yield survey and the Agricultural Coverage 

Evaluation Survey. 

Area frame construction is a lengthy process conducted one 

state at a time. Frames are generally in use for approximately 

15 to 20 years with some in operation for as long as 30 or more 

years. Fig. 1 illustrates the implementation years of current 

NASS state level Area Frames. Originally when ASFs were 

created on paper, only two frames were built per year. 

Currently three to four frames are built each year due to 

technological improvements including the use of ESRI’s 

ArcGIS software, aerial photography, satellite imagery and 

ancillary agricultural information when available. On average, 

five full time employees working for a period of four months 

per state are required for new frame construction [3]. 

Figure 1. US map illustrating the implementation years of current 

NASS Area Frames

Area Sampling Frames (ASFs) have been used in a variety 

of research applications including an evaluation of the 

prevalence of brown stem rot in the north central United States 

[4], to improve agricultural ground survey estimates as part of 

the Monitoring Agriculture with Remote Sensing (MARS) 

project launched by the European Union in 1989 [5] and to 

developed a Geographic Information System for crop area 

estimation at a regional level in the Islamic Republic of Iran 

[6]. Faulkenberry and Garoui [7] compared the utility of 

frequently used estimators based on an ASF utilized in 

agricultural surveys. 

B. NASS Area Sampling Frame: Land Use Stratification 

Land use stratification is “the division of land area into 

broad land use categories” and is known to improve efficiency 

for statistical sampling and estimation. In the construction of a 

NASS area sampling frame (ASF), general cropland (based on 

percentage cultivation), agriculture/urban, residential/ 

commercial, and non agriculture are the commonly identified 

strata. The agricultural strata definitions vary between states 

depending on the type and intensity of agricultural production. 

Strata homogeneity is critical for the performance of the NASS 

ASFs. Once strata definitions are assigned, all land is 

subdivided into primary sampling units (PSUs). Specific PSUs 

are allocated for inclusion in JAS. These PSUs are further 

subdivided into segments, and a segment is randomly selected 

from each allocated PSU for enumeration [3]. 

TABLE I. Land-use stratification codes and definitions represented in 

the NASS Area Sampling Frames (Benedetti, 2010) 

Land-Use Strata 

Codes (Stratum) 

Strata Definition 

11 General Cropland, greater than 75% 

cultivated. 

12 General Cropland, 51-75% cultivated 

20 General Cropland, 15-50% cultivated. 

31 Ag-Urban, less than 15% cultivated, more than 

100 dwellings per square mile, residential 

mixed with agriculture. 

32 Residential/Commercial, no cultivation, more 

than 100 dwellings per square mile. 

40 Less than 15% cultivated 

50 Non-agricultural, 

62 Water 

For the past 58 years, land use stratification has been 

conducted using visual interpretation of satellite imagery or 

aerial photography. Most recently, the satellite data used was 

acquired by the Landsat Thematic Mapper (TM), which was 

primarily relied upon to identify cultivated cropland and, when 

necessary, identify specific crop types. The National 

Agricultural Imagery Program (NAIP) data; which are one 

meter, ortho-rectified, air photos acquired during the growing 

season; are utilized as the base for digitizing PSU boundaries. 

C. The NASS Cropland Data Layer Products 

The NASS Cropland Data Layer (CDL) products are 30-

56.0 meter, raster-formatted, geo-referenced, crop-specific land 

cover classifications. The first state level CDL was produced in 

1997 and CDLs for all 48 conterminous states in the U.S were 

produced from 2008- 2011. The purpose of the CDL program 

is to use satellite data to provide acreage estimates for 

important crop producing states to the NASS Agricultural 

Statistics Board (ASB). All historic CDL products are 

publically available on-line for accessing, visualization, 

downloading, map printing, as well as on the fly statistical and 

change analysis from the NASS’ web service based CDL 

application – CropScape [2]. 
The 2010 CDLs were produced for all 48 states at a 30 

meter spatial resolution as illustrated in Fig. 2. The 2010 CDLs 

were produced using satellite data acquired, during the growing 

season, from the Indian RESOURCESAT-1 (IRS-P6) 

Advanced Wide Field Sensor (AWiFS), Landsat 5 Thematic 

Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus 

(ETM+). Ancillary data inputs were also used. They included: 

the United States Geological Survey (USGS) National 

Elevation Dataset (NED), the USGS Percent Canopy layer, the 

USGS Percent Impervious layer, and the National Aeronautics 

and Space Administration (NASA) Moderate Resolution 

Imaging Spectroradiometer (MODIS) 250 meter 16 day 

Normalized Difference Vegetation Index (NDVI) composites. 

Farm Service Agency (FSA) Common Land Unit (CLU) data 

were used for training and validation of agricultural categories. 

The USGS National Land Cover Dataset (NLCD) 2001 was 

used as training and validation for the non agricultural 

categories. In general, total crop mapping accuracies for the 

2010 CDLs ranged from 85% to 95% for the major crop 

categories [1]. The accuracies for major crops, such as corn and 

soybean in major US agricultural areas are in the high 90th 

percentile. These accuracy numbers are considered high for 

stratification purposes as errors between crop categories can be 

canceled in the stratification process. 

Figure 2. 2010 NASS Cropland Data Layer products. 

III. METHODOLOGY

A. Data and Preprocessing 

The data files required for each of the five state analyses 

conducted in this investigation included: a state level ASF with 

strata specific PSUs, a 2010 state level NASS CDL, and a 2010 

JAS segment file with segment level percent cultivation 

calculated. The 2010 CDLs included as many as fifty 

categories and a wide variety of crops. For the purpose of 

stratification, a crop mask [8] was first generated by recoding 

CDL pixels of crop categories into “cultivated” and non crop 

pixels into “non-cultivated”. To build the 2010 CDL crop 

masks, crop and non-crop categories were recoded to “1” and 

“0” respectively. A typical crop mask is illustrated in Fig.3 (a) 

with green and white colors representing the cultivated and 

non-cultivated pixels respectively. The crop mask is overlaid 

with an ASF with PSU boundaries. The exact crop types in 

each PSU are illustrated in Fig. 3(b) by overlaying the ASF 

over the CDL. The results of the traditional stratification and 

the corresponding CDL based stratifications were compared 

based on the JAS segment in situ ground truth. 

Figure 3. (a) An ASF with PSU boundaries overlaying a CDL crop mask 

(green- cultivated, white- non cultivated); (b) the same ASF overlaying a 

2010 CDL image product (brown – winter wheat, yellow, corn, orange – 

sorghum, pink- alfalfa, pale green – other hay, blue-water). 

Figure 4. (a) CDL crop mask based PSU percent cultivation; (b) an ASF 

with CDL derived stratification. 

B. Stratification Method 

NASS’ traditional area frame stratification process 

involves visual interpretation of ASF PSUs into different 

strata based on percent cultivated land within a PSU boundary. 

The percent cultivation of each ASF PSU can be calculated 

from state level crop masks derived from CDL by counting 

pixels with value “1” (cultivated) and total number of pixels 

within the PSU boundary. The percent cultivated is given by 

the number of “1” pixels divided by total number of pixels. 

Fig. 4(a) illustrates PSU percent cultivation calculated from 

CDL based crop mask. With the calculated percent cultivation, 

each PSU’s stratum could be labeled into a different stratum 

category based on the state specific strata definitions as given 

by Table 1. The resulting CDL based stratification for the 

same frame is shown as Fig. 4(b). This proposed new CDL 

based process is objective and automated while the traditional 

stratum labeling method is subjective and manual. This new 

method improves efficiency, objectiveness and accuracy in 

stratification. 
TABLE II. Oklahoma 2010 ASF Analysis, Traditional vs. CDL Stratification Method

 Traditional Stratification CDL Stratification p-value 

Stratum % Cultivated Segments Correct Accuracy (p1) Segments Correct Accuracy(p2) Ha: p1 ≠ p2

11 >75% 140 47 34% 43 27 63% 0.001 

12 51% - 75% 48 9 19% 77 30 39% 0.024 

20 15% - 50% 74 26 35% 98 42 43% 0.305 

40 < 15% 61 61 100% 105 96 91% 0.027 

Total 323 323 

TABLE III. Ohio 2010 ASF Analysis, Traditional vs. CDL Stratification Method 

 Traditional Stratification CDL Stratification p-value 

Stratum % Cultivated Segments Correct Accuracy (p1) Segments Correct Accuracy(p2) Ha: p1 ≠ p2

11 >75% 110 84 76% 85 76 89% 0.019 

12 51% - 75% 35 15 43% 42 23 55% 0.055 

20 15% - 50% 42 28 67% 48 37 77% 0.271 

40 < 15% 53 47 89% 65 57 88% 0.869 

Total 240 240 

TABLE IV. Five State 2010 Strata Summary, Traditional vs. CDL Stratification Method 

Traditional Stratification CDL Stratification p-value 

Stratum % Cultivated Segments Correct Accuracy (p1) Segments Correct Accuracy(p2) Ha: p1 ≠ p2

11 >75% 250 131 52% 128 103 80% 0.000 

12 51% - 75% 83 24 29% 119 53 45% 0.025 

13 >50% 171 90 53% 91 69 76% 0.000 

20 15% - 50% 371 177 48% 387 219 57% 0.000 

40 < 15% 322 305 95% 472 407 86% 0.000 

Total 1197 727 61% 1197 851 71% 0.000 

C. Stratification Result Evaluation 

To evaluate the effectiveness of the CDL based 

stratification method, the results were compared with the 

traditional stratification method based on 2010 JAS segment 

ground truth data. Stratification performance was assessed 

based on the percent of segments matching the definition 

(correctly labeled stratum) among all segments labeled with a 

given stratum of the PSUs within which they are located. The 

reference segment data were derived from JAS survey. The 

enumerators recorded ground truth in the survey. 

An assessment of the resulting accuracies indicated that the 

CDL stratification method generally resulted in higher 

accuracies, but these results did not indicate whether the 

differences in the two methods were significant statistically. 

The ultimate goal of this evaluation was to determine whether 

the proposed CDL based stratification method achieved 

equivalent or improved accuracies when compared to the 

traditional Area Frame stratification method. Therefore, twotailed proportion tests, a Chi-Square test or a Fisher’s Exact test 

for sample sizes less than five were conducted for each state 

and each stratum. In these tests, two sample proportions were 

p1 the accuracy results from the traditional stratification 

method and p2 the accuracy results from the proposed CDL 

based stratification method. The hypotheses of the significance 

tests were H0: p1=p2 and Ha: p1≠p2. The null hypothesis stated 

that there was no difference in the accuracies of the two 

stratification methods while the alternative hypothesis stated 

that the accuracies of the two stratification methods were 

significantly different. The tests were performed and p values 

were calculated for each state and each stratum with a 

confidence level of 95%. 

IV. DISCUSSION

A comparison was made between the stratification results 

achieved by the traditional stratification method and those of 

the CDL based method using JAS reported data as in situ 

validation. It should be noted that the CDL stratification 

method could only identify percent cultivation, non agriculture, 

and water. ASFs often have additional strata such as stratum 

31 (Ag-Urban, less than 15% cultivated, more than 100 

dwellings per square mile) and stratum 32 (residential/ 

commercial, no cultivation, more than 100 dwelling per square 

mile). For this analysis, these strata were included in stratum 

40 (less than 15% cultivated). All stratification was PSU based. 

Table 2 and Fig. 5 presented the accuracy results for both 

the traditional stratification of the ASF PSUs and those of the 

CDL derived stratification of the ASF PSUs for 2010 

Oklahoma frame analysis. All other states were evaluated in 

the same manner. In Tables 2, 3, and 4, the accuracy results of 

the traditional (visual interpretation) stratification method and 

the CDL based stratification method were presented side by 

side. The column at the far right identifies the p-values of the 

Chi Square or Fisher’s Exact tests. 

As shown in Table 2, for the Oklahoma 2010 traditional 

stratification, of the 323 total segments in the state, 140 were in 

PSUs that were identified as being stratum 11 (greater than 

75% cultivated) by NASS carto-technicians based on stratum 

definition. Of the 140 segments, JAS reported that 47 were 

stratum 11, an accuracy of 34%. For the CDL stratification 

results, of the 323 segments, 43 were labeled in stratum 11 

PSUs. Of the 43 segments, the JAS reported 27 segments were 
stratum 11, an accuracy of 63%. The accuracies of the CDL 

stratification method were higher than those of the traditional 

area frame stratification method. 

Figure 5. Oklahoma 2010 ASF Analysis, Traditional Method vs. CDL

Based Stratification Method

Figure 6. Ohio 2010 ASF Analysis, Traditional Method vs. CDL Based 

Stratification Method

Figure 7. Five State 2010 Strata Summary, Traditional Method vs. CDL 

Based Stratification Method 

Similarly, in Stratum 20 (15% - 50% cultivated), 74 

segments were in PSUs identified by the traditional 

stratification as stratum 20. Of these 74 segments, the JAS 

reported that 26 were stratum 20, which represented an 

accuracy of 35%. Of the 323 segments, 98 were located in 

PSUs identified by the CDL based stratification as stratum 20. 

Of the 98 stratum 20 segments, the JAS reported that 42 were 

stratum 20, an accuracy of 43%. The accuracies of the CDL 

stratification method were higher than those of the traditional 

stratification method. 

As shown in Table 2, the accuracies of the traditional 

stratification, the CDL based stratification and the JAS survey 

reported were calculated for the Oklahoma ASF and 

summarized in the same manner for all strata. In Table 2, the pvalues were highlighted in red if the differences were 

considered statistically significant at the 95% confidence level. 

Out of the eight comparisons, six were significantly different 

from one another. Stratum 20 was the only stratum that 

showed no statistically significant difference between the two 

stratification methods at the confidence level of 95% although 

the accuracies of the CDL stratification method were higher 

than those of the traditional stratification method. 

Note that in Table 2 the accuracies are derived for 

Oklahoma segments meeting formal stratum definitions using 

the traditional and CDL based stratification methods. Tables 2 

and 3 illustrate the results for the Oklahoma and Ohio analyses. 

Figs. 5 and 6 provide graphical representations of the 

Oklahoma and Ohio results. 

Table 4 and Fig. 7 provide a summary of all state level 

analyses across five strata. In summary, land cover in the five 

states stratified with the automated CDL method has a higher 

rate of accuracy than that of the traditional method when using 

the JAS survey data as validation. Across all ASF strata 

specifically, 11, 12, 13, 20 and 40, the traditional method 

achieved a total accuracy of 61% and the CDL stratification 

method achieved a total accuracy of 71%, as shown in Table 4. 

The CDL stratification method achieved higher accuracies in 

all strata except stratum 40 (Traditional Method - 95% vs. CDL 

method - 86%). The CDL method achieved higher accuracy in 

the more highly cultivated strata such as stratum 11 

(Traditional Method - 52% vs. CDL Method - 80%) and 

stratum 13 (Traditional Method - 53% vs. CDL Method -76%). 

 Two sided proportion tests were conducted on all 

comparisons to test whether the differences in accuracies 

achieved using the two different methods were statistically 

significant. In these tests the significance level was set to be 

0.05 and the null hypothesis was accepted if the p-value 

exceeded the significance level. Across all strata, when state 

results were combined, the p-values were less than 0.05. The 

tests indicated that in strata 11, 12, 13 and 20 the CDL 

stratification method was more accurate at determining percent 

cultivation. In stratum 40, the traditional method was more 

accurate. 

As observed in Tables 2, 3 and 4, the CDL method 

achieved higher rates of accuracy in the highly cultivated strata 

(11, and 13). Identification of cropland is the strength of the 

CDL process. Satellite imagery collected across the entire 

growing season, decision tree classification software, useful 
ancillary data and an abundance of training data enable the 

CDLs to achieve total crop accuracies of approximately 85 – 

95%. Furthermore, the CDL method is objective and 

consistent. 

Both methods struggled in having the identification of 

percent cultivation for the PSUs match the JAS segment level 

data in strata 12 and 20. The primary issue with these strata is 

due to the heterogeneity of the land cover. In the construction 

of the ASF, cartographic technicians attempt to define PSUs 

that are homogeneous. It is very difficult to define a stratum 12 

or 20 PSU that is homogeneous across the PSU. The cropland 

generally is clustered in one portion of the PSU and the 

remainder of the PSU has small amounts of agriculture. When 

the segments are selected in these PSUs it is not unusual that 

they not represent their strata definition. One recommendation 

to improve PSU homogeneity would be to reduce the size of 

PSUs during new ASF construction. This would improve ASF 

performance in strata 12 and 20 PSUs. One area of further 

research for this project is to determine if the CDL based 

method can more accurately identify percent cultivation in the 

strata 12 and 20 PSU using multi-year data rather than single 

year CDL data. 

V. CONCLUSION

This paper proposed a new automated CDL based method for 

deriving percent cultivation and subsequently stratifying U.S. 

land cover. The CDL based stratification of NASS ASF PSUs 

was successfully conducted for Oklahoma, Ohio, Virginia, 

Georgia, and Arizona. The stratification accuracies of the 

traditional and new CDL based stratification methods were 

compared based on in situ validation data collected by 

enumerators during the 2010 JAS. Results of the five state 

analyses indicated that the new automated CDL method was 

more accurate in determining U.S. percent cultivation in 

intensively cropped areas and weaker in non agricultural areas. 

The CDL based stratification achieved higher accuracies in 

strata 11, 12, 13 and 20 while the traditional method achieved 

higher accuracies in stratum 40. The differences in the 

accuracies were statistically significant at a 95% confidence 

level. The novelty of the proposed method is using geospatial 

CDL data to objectively and automatically compute percent 

cultivation of the ASF PSUs as compared to the traditional 

method that subjectively determining percent cultivation based 

on visual estimation of satellite data. This proposed new CDL 

based process improved efficiency, objectiveness and 

accuracy as compared to the traditional stratification method. 

It is concluded that adoption of the automated CDL 

stratification method in ASF construction will help NASS 

achieve the goals of improving efficiency, reducing cost and 

improving the precision of JAS estimates by updating the 

NASS ASFs with greater frequency and stratification 

accuracy. 

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