Estimating accuracy of land-cover composition from two-stage cluster sampling

✍ Scribed by Stephen V. Stehman; James D. Wickham; Lorenzo Fattorini; Timothy D. Wade; Federica Baffetta; Jonathan H. Smith

• Book ID: 104091859
• Publisher: Elsevier Science
• Year: 2009
• Tongue: English
• Weight: 1014 KB
• Volume: 113
• Category: Article
• ISSN: 0034-4257
• DOI: 10.1016/j.rse.2009.02.011

✦ Synopsis

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), root mean square error (RMSE), and correlation (CORR) to quantify accuracy of land-cover composition for a general two-stage cluster sampling design, and for the special case of simple random sampling without replacement (SRSWOR) at each stage. The bias of the estimators for the two-stage SRSWOR design is evaluated via a simulation study. The estimators of RMSE and CORR have small bias except when sample size is small and the land-cover class is rare. The estimator of MAD is biased for both rare and common land-cover classes except when sample size is large. A general recommendation is that rare land-cover classes require large sample sizes to ensure that the accuracy estimators have small bias.