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A priori evaluation of two-stage cluster sampling for accuracy assessment of large-area land-cover maps

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Two-stage cluster sampling reduces the cost of collecting accuracy assessment reference data by constraining sample elements to fall within a limited number of geographic domains (clusters). However, because classification error is typically positively spatially correlated, within-cluster correlation may reduce the precision of the accuracy estimates. The detailed population information to quantify a priori the effect of within-cluster correlation on precision is typically unavailable. Consequently, a convenient, practical approach to evaluate the likely performance of a two-stage cluster sample is needed. We describe such an a priori evaluation protocol focusing on the spatial distribution of the sample by land-cover class across different cluster sizes and costs of different sampling options, including options not imposing clustering. This protocol also assesses the two-stage design's adequacy for estimating the precision of accuracy estimates for rare land-cover classes. We illustrate the approach using two large-area, regional accuracy assessments from the National Land-Cover Data (NLCD), and describe how the a priori evaluation was used as a decision-making tool when implementing the NLCD design.

Document Type: Research Article


Affiliations: 1: National Exposure Research Laboratory US Environmental Protection Agency (E243-05) Research Triangle Park NC 27711 USA 2: SUNY-ESF 320 Bray Hall Syracuse NY 13210 USA 3: National Center for Environmental Research (8723R) US Environmental Protection Agency 1200 Pennsylvania Ave NW Washington DC 20460 USA 4: Science Applications International Corporation (SAIC) EROS Data Center Sioux Falls SD 57198 USA

Publication date: 2004-03-01

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