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Accuracy assessments and areal estimates using two-phase stratified random sampling, cluster plots, and the multivariate composite estimator

Informally Refereed
Authors: Raymond L. Ph.D..Czaplewski
Year: 2000
Type: Book Chapter
Station: Rocky Mountain Research Station
Source: In: Mowrer, H. Todd; Congalton, Russell G., editors. Quantifying spatial uncertainty in natural resources: Theory and applications for GIS and remote sensing. Chelsea, MI: Ann Arbor Press. p. 79-100.

Abstract

Consider the following example of an accuracy assessment. Landsat data are used to build a thematic map of land cover for a multicounty region. The map classifier (e.g., a supervised classification algorithm) assigns each pixel into one category of land cover. The classification system includes 12 different types of forest and land cover: black spruce, balsam fir, white-cedar, other softwoods, aspen, birch, other hardwoods, urban, wetland, water, pasture, and agriculture. The accuracy of the map must be known by a user of the map to conduct credible analyses.

Keywords

accuracy assessments, areal estimates, two-phase stratified random sampling, cluster plots, multivariate composite estimator, Landsat data, map classifier

Citation

Czaplewski, Raymond L. 2000. Accuracy assessments and areal estimates using two-phase stratified random sampling, cluster plots, and the multivariate composite estimator. In: Mowrer, H. Todd; Congalton, Russell G., editors. Quantifying spatial uncertainty in natural resources: Theory and applications for GIS and remote sensing. Chelsea, MI: Ann Arbor Press. p. 79-100.
https://www.fs.usda.gov/research/treesearch/33577