Chapter 14
Geospatial Land Use and Land Cover Data
for Improving Agricultural Area Sampling
Frames
Claire G. Boryan and Zhengwei Yang
Abstract This chapter describes the development of an automated stratification
method, based on readily available geospatial Cropland Data Layers and its integration into the USDA National Agricultural Statistic Service operational stratification
process. Automated stratification, based on the NASS Cropland Data Layers https://
nassgeodata.gmu.edu/CropScape/, objectively, consistently, and rapidly stratifies
United States land cover of the area frame primary sampling units, by percent
cultivation. Subsequently, the integration of automated stratification into the traditional operational process further refines area frames with manual editing and review
processes and significantly improves operational efficiency and area frame accuracy.
A performance comparison is described between the NASS traditional stratification
method, which is based on subjective visual interpretation of aerial or satellite data,
and the Cropland Data Layer based automated stratification method. The effectiveness of both traditional and automated stratification methods is also assessed using in
situ validation data collected at the segment level as part of the 2010 NASS June
Area Survey. Though the automated stratification method improves efficiency and
objectivity as well as accuracy in the intensively cropped areas, it inherits the
Cropland Data Layer classification errors and has lower accuracies in low or
non-agricultural areas. 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 have shown further improvement in stratification accuracy.
Keywords Area sampling frame · Automated stratification · Cropland Data Layer ·
Cultivated data layer · Land cover based stratification
C. G. Boryan (*) · Z. Yang
United States Department of Agriculture, National Agricultural Statistics Service, Washington,
DC, USA
e-mail: claire.boryan@usda.gov
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_14
265
Geospatial Land Use and Land Cover Data
for Improving Agricultural Area Sampling
Frames
Claire G. Boryan and Zhengwei Yang
Abstract This chapter describes the development of an automated stratification
method, based on readily available geospatial Cropland Data Layers and its integration into the USDA National Agricultural Statistic Service operational stratification
process. Automated stratification, based on the NASS Cropland Data Layers https://
nassgeodata.gmu.edu/CropScape/, objectively, consistently, and rapidly stratifies
United States land cover of the area frame primary sampling units, by percent
cultivation. Subsequently, the integration of automated stratification into the traditional operational process further refines area frames with manual editing and review
processes and significantly improves operational efficiency and area frame accuracy.
A performance comparison is described between the NASS traditional stratification
method, which is based on subjective visual interpretation of aerial or satellite data,
and the Cropland Data Layer based automated stratification method. The effectiveness of both traditional and automated stratification methods is also assessed using in
situ validation data collected at the segment level as part of the 2010 NASS June
Area Survey. Though the automated stratification method improves efficiency and
objectivity as well as accuracy in the intensively cropped areas, it inherits the
Cropland Data Layer classification errors and has lower accuracies in low or
non-agricultural areas. 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 have shown further improvement in stratification accuracy.
Keywords Area sampling frame · Automated stratification · Cropland Data Layer ·
Cultivated data layer · Land cover based stratification
C. G. Boryan (*) · Z. Yang
United States Department of Agriculture, National Agricultural Statistics Service, Washington,
DC, USA
e-mail: claire.boryan@usda.gov
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_14
265