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

Remote Sensing of Environment
By: , and 

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Abstract

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. ?? 2009 Elsevier Inc.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Estimating accuracy of land-cover composition from two-stage cluster sampling
Series title Remote Sensing of Environment
DOI 10.1016/j.rse.2009.02.011
Volume 113
Issue 6
Year Published 2009
Language English
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Remote Sensing of Environment
First page 1236
Last page 1249