visual interpretation of aerial or satellite data and topographic maps since the 1950s.
This popular method requires a manual, subjective, and labor-intensive process. The
traditional stratification method does not utilize existing land cover data, such as the
geospatial Cropland Data Layer (CDL) (Boryan et al. 2011; Han et al. 2012) in an
automated, objective, and efficient manner to stratify area frames.
This chapter describes (1) recent research, (2) the development of an automated
stratification method, based on readily available geospatial CDLs, and (3) its integration into the USDA NASS operational stratification process, which utilizes
results of both traditional and automated stratification. The CDL-based automated
stratification objectively, consistently, and rapidly stratifies US land cover, by
percent cultivation, of the area frame PSUs (Boryan et al. 2014), while the integration of automated stratification into the traditional operational process further refines
area frames with manual editing and review procedures and significantly improves
the operational efficiency and area frame accuracy. A performance comparison was
made between the NASS traditional stratification method and the CDL-based automated stratification method. The effectiveness of both traditional and automated
stratification methods in determining percent cultivation, at the area frame PSU
level, was also assessed using in situ validation data collected at the segment level
as part of the 2010 JAS. Though the automated stratification method improves
efficiency and objectivity as well as accuracy in the intensively cropped areas, it
inherits the CDL classification errors and has lower accuracies in low or
nonagricultural areas. This implies that the automated stratification process is not a
perfect solution to directly replace the NASS traditional stratification method for
area frame construction operationally. To further improve the accuracy of the
automated method in low-intensity agricultural areas and maintain its efficiency
and objectivity, the automated method is integrated into the traditional stratification
process to further refine area frames with manual editing and review procedures. The
results of the integration are further compared with the original results of the
traditional and automated methods.
The chapter sections are organized as follows. Sections 14.2 includes background
on related work, the NASS area sampling frames, the NASS Cropland Data Layer,
and NASS Cultivated Layer. Section 14.3 identifies the study areas for both the
traditional and automatic stratification study and the assessment of the integration of
automated stratification into NASS operations. Section 14.4 describes the automated
stratification methodology, including specifics of the stratification method, as well as
a stratification analysis and result evaluation. Subsections include a discussion
comparing the traditional and automatic stratification results. Section 14.5 describes
the integration of automatic stratification into NASS operations with a description of
the integration scope and data and the integration method steps. Section 14.6
includes the integration results with specifics regarding (1) stratification accuracy
and (2) the evaluation of mean stratum percent cultivation range, standard deviations, and PSU size. Section 14.7 includes a discussion based on the integration
results with an additional comparison of labor costs. Section 14.8 includes concluding remarks.
14 Geospatial Land Use and Land Cover Data for Improving Agricultural Area. . .
267
This popular method requires a manual, subjective, and labor-intensive process. The
traditional stratification method does not utilize existing land cover data, such as the
geospatial Cropland Data Layer (CDL) (Boryan et al. 2011; Han et al. 2012) in an
automated, objective, and efficient manner to stratify area frames.
This chapter describes (1) recent research, (2) the development of an automated
stratification method, based on readily available geospatial CDLs, and (3) its integration into the USDA NASS operational stratification process, which utilizes
results of both traditional and automated stratification. The CDL-based automated
stratification objectively, consistently, and rapidly stratifies US land cover, by
percent cultivation, of the area frame PSUs (Boryan et al. 2014), while the integration of automated stratification into the traditional operational process further refines
area frames with manual editing and review procedures and significantly improves
the operational efficiency and area frame accuracy. A performance comparison was
made between the NASS traditional stratification method and the CDL-based automated stratification method. The effectiveness of both traditional and automated
stratification methods in determining percent cultivation, at the area frame PSU
level, was also assessed using in situ validation data collected at the segment level
as part of the 2010 JAS. Though the automated stratification method improves
efficiency and objectivity as well as accuracy in the intensively cropped areas, it
inherits the CDL classification errors and has lower accuracies in low or
nonagricultural areas. This implies that the automated stratification process is not a
perfect solution to directly replace the NASS traditional stratification method for
area frame construction operationally. To further improve the accuracy of the
automated method in low-intensity agricultural areas and maintain its efficiency
and objectivity, the automated method is integrated into the traditional stratification
process to further refine area frames with manual editing and review procedures. The
results of the integration are further compared with the original results of the
traditional and automated methods.
The chapter sections are organized as follows. Sections 14.2 includes background
on related work, the NASS area sampling frames, the NASS Cropland Data Layer,
and NASS Cultivated Layer. Section 14.3 identifies the study areas for both the
traditional and automatic stratification study and the assessment of the integration of
automated stratification into NASS operations. Section 14.4 describes the automated
stratification methodology, including specifics of the stratification method, as well as
a stratification analysis and result evaluation. Subsections include a discussion
comparing the traditional and automatic stratification results. Section 14.5 describes
the integration of automatic stratification into NASS operations with a description of
the integration scope and data and the integration method steps. Section 14.6
includes the integration results with specifics regarding (1) stratification accuracy
and (2) the evaluation of mean stratum percent cultivation range, standard deviations, and PSU size. Section 14.7 includes a discussion based on the integration
results with an additional comparison of labor costs. Section 14.8 includes concluding remarks.
14 Geospatial Land Use and Land Cover Data for Improving Agricultural Area. . .
267