states. Further, PSU size reductions for all cultivated strata resulting from the
additional manual review and editing procedures in the hybrid process resulted in
additional accuracy gains and overall significantly improved NASS area sampling
frames constructed at a reduced cost.
Overall, the geospatial CDL-based hybrid process is robust and has resulted in
significant improvements in area frame accuracy, efficiency, and objectivity based
on JAS reported data and a reduction in the cost of operational area frame construction. The hybrid approach enables more frequent area frame updates and revisions.
This operational hybrid area frame construction process, based on available
geospatial data, is easily applicable to the operations of other agencies or countries
that conduct area frame-based surveys and have available geospatial cropland
cover data.
Acknowledgments The authors wish to thank the USDA NASS Geospatial Science and Survey
Section staff, who construct the NASS area frames, for their continued dedication to produce and
sample high-quality area frames and for their willingness to share their knowledge of this process.
Further, the authors wish to thank Ms. Lee Ebinger of USDA NASS for creating the figures for this
chapter and Mr. Terry Broz and Ms. Avery Sandborn of USDA NASS for reviewing and providing
suggestions that significantly improved this chapter.
References
Arroway, P., Abreu, D. A., Lamas, A. C., Lopiano, K. K., & Young, L. Y. (2010). An alternate
approach to assessing misclassification in JAS. In Proceedings of the section on survey research
methods JSM 2010. Alexandria: American Statistical Association.
Benedetti, R., Postiglione, P., & Piersimoni, F. (2015). Sampling Spatial Units for Agricultural
Surveys (Advances in Spatial Science). Berlin: Springer.
Bing Maps. (2017). Bing Maps. https://www.bing.com/maps. Last accessed 24 April 2017.
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
Department of Agriculture, National Agricultural Statistics Service CDL Program. Geocarto
International, 26(5), 341–358.
Boryan, C., Yang, Z., & Di, L. (2012). Deriving 2011 cultivated land cover data sets using USDA
National Agricultural Statistics Service historic CDLs. In Proceeding of IEEE International
Geoscience and Remote Sensing Symposium. Munich, Germany, July 22–27, 2012.
Boryan, C., Yang, Z., Di, L., & Hunt, K. (2014). A new automatic stratification method for
U.S. Agricultural Area Sampling Frame Construction Based on the CDL. IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1939–1404, Nov.
2014, https://doi.org/10.1109/JSTARS.2014.2322584.
Carfagna, E., & Gallego, F. J. (2005). Using remote sensing for agricultural statistics. International
Statistical Review, 73, 389–404.
Cochran, W. (1977). Sampling techniques. New York: John Wiley and Sons.
Cotter, J., & Tomczac, C.M. (1994) An image analysis system to develop area sampling frames for
agricultural surveys Photogrammetric Engineering and Remote Sensing, 60 (3): 299–306.
Cotter, J. Davies, C., Nealon, J., & Roberts R. (2010). Area frame design for agricultural surveys in
agricultural survey methods (R. Benedetti, M. Bee, G. Espa, & F. Piersimoni, Eds.)., Chichester: Wiley. https://doi.org/10.1002/9780470665480.ch11.
296
C. G. Boryan and Z. Yang
additional manual review and editing procedures in the hybrid process resulted in
additional accuracy gains and overall significantly improved NASS area sampling
frames constructed at a reduced cost.
Overall, the geospatial CDL-based hybrid process is robust and has resulted in
significant improvements in area frame accuracy, efficiency, and objectivity based
on JAS reported data and a reduction in the cost of operational area frame construction. The hybrid approach enables more frequent area frame updates and revisions.
This operational hybrid area frame construction process, based on available
geospatial data, is easily applicable to the operations of other agencies or countries
that conduct area frame-based surveys and have available geospatial cropland
cover data.
Acknowledgments The authors wish to thank the USDA NASS Geospatial Science and Survey
Section staff, who construct the NASS area frames, for their continued dedication to produce and
sample high-quality area frames and for their willingness to share their knowledge of this process.
Further, the authors wish to thank Ms. Lee Ebinger of USDA NASS for creating the figures for this
chapter and Mr. Terry Broz and Ms. Avery Sandborn of USDA NASS for reviewing and providing
suggestions that significantly improved this chapter.
References
Arroway, P., Abreu, D. A., Lamas, A. C., Lopiano, K. K., & Young, L. Y. (2010). An alternate
approach to assessing misclassification in JAS. In Proceedings of the section on survey research
methods JSM 2010. Alexandria: American Statistical Association.
Benedetti, R., Postiglione, P., & Piersimoni, F. (2015). Sampling Spatial Units for Agricultural
Surveys (Advances in Spatial Science). Berlin: Springer.
Bing Maps. (2017). Bing Maps. https://www.bing.com/maps. Last accessed 24 April 2017.
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
Department of Agriculture, National Agricultural Statistics Service CDL Program. Geocarto
International, 26(5), 341–358.
Boryan, C., Yang, Z., & Di, L. (2012). Deriving 2011 cultivated land cover data sets using USDA
National Agricultural Statistics Service historic CDLs. In Proceeding of IEEE International
Geoscience and Remote Sensing Symposium. Munich, Germany, July 22–27, 2012.
Boryan, C., Yang, Z., Di, L., & Hunt, K. (2014). A new automatic stratification method for
U.S. Agricultural Area Sampling Frame Construction Based on the CDL. IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1939–1404, Nov.
2014, https://doi.org/10.1109/JSTARS.2014.2322584.
Carfagna, E., & Gallego, F. J. (2005). Using remote sensing for agricultural statistics. International
Statistical Review, 73, 389–404.
Cochran, W. (1977). Sampling techniques. New York: John Wiley and Sons.
Cotter, J., & Tomczac, C.M. (1994) An image analysis system to develop area sampling frames for
agricultural surveys Photogrammetric Engineering and Remote Sensing, 60 (3): 299–306.
Cotter, J. Davies, C., Nealon, J., & Roberts R. (2010). Area frame design for agricultural surveys in
agricultural survey methods (R. Benedetti, M. Bee, G. Espa, & F. Piersimoni, Eds.)., Chichester: Wiley. https://doi.org/10.1002/9780470665480.ch11.
296
C. G. Boryan and Z. Yang