A priori evaluation of two-stage cluster sampling for accuracy assessment of large-area land-cover maps
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Abstract
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 priorievaluation was used as a decision-making tool when implementing the NLCD design.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A priori evaluation of two-stage cluster sampling for accuracy assessment of large-area land-cover maps |
| Series title | International Journal of Remote Sensing |
| DOI | 10.1080/0143116031000149998 |
| Volume | 25 |
| Issue | 6 |
| Year Published | 2004 |
| Language | English |
| Publisher | Taylor & Francis |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 28 p. |
| First page | 125 |
| Last page | 1252 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |