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

International Journal of Remote Sensing
By: , and 

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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.

Additional publication details

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