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Accuracy assessment with complex sampling designs

Informally Refereed
Authors: Raymond L. Ph.D..Czaplewski
Year: 2010
Type: Paper (invited, offered, keynote)
Station: Rocky Mountain Research Station
Source: In: Tate, N. J.; Fisher, P. F., eds. Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences; Accuracy 2010 Symposium; July 20-23; Leicester, UK. Great Britain: University of Leicester, MPG Books Group. p. 174-176.

Abstract

A reliable accuracy assessment of remotely sensed geospatial data requires a sufficiently large probability sample of expensive reference data. Complex sampling designs reduce cost or increase precision, especially with regional, continental and global projects. The General Restriction (GR) Estimator and the Recursive Restriction (RR) Estimator separate a complex sample survey into simple statistical components, each of which is sequentially combined into the final estimate. GR and RR produce a design-consistent Empirical Best Linear Unbiased Estimator (EBLUE) for any sample survey design, regardless of its complexity.

Keywords

Kalman filter, error matrix, GIS, geospatial database, MODIS, Landsat, LiDAR, photo-interpretation

Citation

Czaplewski, Raymond L. 2010. Accuracy assessment with complex sampling designs. In: Tate, N. J.; Fisher, P. F., eds. Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences; Accuracy 2010 Symposium; July 20-23; Leicester, UK. Great Britain: University of Leicester, MPG Books Group. p. 174-176.
https://www.fs.usda.gov/research/treesearch/36942